A vehicle safety protection intelligent prompt alarm method

Abstract

The present invention relates to the field of vehicle safety technology, and discloses a vehicle safety protection intelligent prompt alarm method, including: receiving an intelligent prompt instruction, acquiring a fault history data set based on a data acquisition instruction, acquiring an initial fitting stage set using the fault history data set, confirming receiving an information receiving instruction from an information receiving unit, acquiring target analysis data using the information receiving instruction, confirming one or more first target fitting stages in the initial fitting stage set according to the target analysis data, acquiring a fitting analysis value based on the one or more first target fitting stages, acquiring a target analysis value based on the target analysis data, and when the target analysis value is greater than or equal to the fitting analysis value, confirming receiving a feedback instruction from a result feedback unit, and realizing an intelligent prompt alarm for vehicle safety protection based on the feedback instruction. The main purpose of the present invention is to realize an intelligent explosion-proof prompt for tires.

Description

Technical Field

[0001] The invention relates to an intelligent prompt alarm method for vehicle safety protection, belonging to the technical field of vehicle safety. Background Art

[0002] With the development of automobile technology, more and more vehicles are put into use. Correspondingly, the safety of vehicles while driving is becoming more and more important. Therefore, how to provide intelligent explosion-proof reminders for the tires of moving vehicles has become an urgent problem to be solved.

[0003] At present, the tire pressure monitoring of a moving vehicle mainly relies on a tire pressure monitoring system, which can promptly remind the driver when the tire pressure drops.

[0004] Although the above technology can realize intelligent monitoring of tire pressure, it cannot realize intelligent reminder of tire explosion prevention according to the vehicle's driving status. Summary of the invention

[0005] The present invention provides a vehicle safety protection intelligent prompt alarm method, device and computer-readable storage medium, the main purpose of which is to achieve intelligent explosion-proof prompts for vehicle tires.

[0006] To achieve the above object, the present invention provides a vehicle safety protection intelligent prompt alarm method, comprising:

[0007] Receiving an intelligent prompt instruction, and confirming an intelligent prompt system based on the intelligent prompt instruction, wherein the intelligent prompt system includes: a data collection unit, an information receiving unit, and a result feedback unit;

[0008] Confirming receipt of a data acquisition instruction from a data acquisition unit, and acquiring a fault history data set based on the data acquisition instruction, wherein the fault history data set includes a plurality of fault history data;

[0009] Acquire a target historical data set based on the fault historical data set, and acquire an initial fitting stage set using the target historical data set, wherein the initial fitting stage set includes a plurality of initial fitting data;

[0010] Confirming receipt of an information receiving instruction from an information receiving unit, and using the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series;

[0011] According to the target analysis data, one or more first target fitting stages are centrally identified in the initial fitting stage, fitting analysis values ​​are obtained based on the one or more first target fitting stages, and a target analysis value is obtained based on the target analysis data. When the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and an intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction.

[0012] Optionally, acquiring a target historical data set based on the fault historical data set includes:

[0013] Obtaining a diagnostic constraint set for classifying the fault history data, wherein the diagnostic constraint set is composed of three different diagnostic constraints, and the three different diagnostic constraints are: tire damage cause, tire damage time and tire model, wherein the tire damage cause is composed of tire self-burst or tire external force burst;

[0014] Extracting fault history data from the fault history data set in sequence, and performing the following operations on the extracted fault history data:

[0015] Using the diagnostic constraint set, performing a marking operation on the extracted fault history data to obtain marked history data;

[0016] If the identified historical data lacks at least one diagnostic constraint in the diagnostic constraint set, the identified historical data is discarded;

[0017] Otherwise, identifying the historical data as first historical data, and determining whether the first historical data is preset second historical data;

[0018] If the first historical data is not the second historical data, discarding the first historical data;

[0019] If the first historical data is the second historical data, aggregating the second historical data to obtain a second historical data set;

[0020] A target historical data set is acquired based on the second historical data set.

[0021] Optionally, the determining whether the first historical data is preset second historical data includes:

[0022] A first tire pressure time series set and a second tire pressure time series set for reference are obtained, wherein the first tire pressure time series set includes a plurality of first tire pressure time series, the second tire pressure time series set includes a plurality of second tire pressure time series, and the collection time intervals of the first tire pressure time series and the second tire pressure time series are preset, wherein the tire damage cause corresponding to the first tire pressure time series set is: tire self-burst, and the tire damage cause corresponding to the second tire pressure time series set is: tire external force burst, wherein the first tire pressure time series is as follows:

[0023] T1={P1,P2,…,P i ,…,P n}

[0024] Wherein, T1 represents the first tire pressure time sequence, P1, P2, ..., P i ,…,P n Both represent the first tire pressure, and P i represents the i-th first tire pressure in the first tire pressure time series, n represents the preset collected value, and P n =0;

[0025] Based on the first tire pressure time series set, obtaining a first tire pressure mean and a first tire pressure variance, wherein the first tire pressure mean and the first tire pressure variance are respectively the mean and variance of a plurality of first tire pressure time series in the first tire pressure time series set;

[0026] Based on the second tire pressure time series set and a preset reference mean relationship, a second tire pressure mean value set is obtained, wherein the second tire pressure mean value set includes a plurality of second tire pressure means, and based on the second tire pressure mean value set, a second tire pressure mean value and a second tire pressure variance are obtained, wherein the second tire pressure mean value and the second tire pressure variance are respectively the mean and variance of the plurality of second tire pressure means in the second tire pressure mean value set;

[0027] An initial tire pressure time series is obtained using the first historical data, and whether the first historical data is the second historical data is determined based on the initial tire pressure time series, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean, and the second tire pressure variance.

[0028] Optionally, the acquiring the second tire pressure mean value set based on the second tire pressure time series set and a preset reference mean value relationship formula includes:

[0029] The following operations are performed on the second tire pressure time series in the second tire pressure time series set:

[0030] The second tire pressure is sequentially extracted from the second tire pressure time series, and the following operations are performed on the extracted second tire pressure:

[0031] comparing the extracted second tire pressure with a preset second pressure threshold;

[0032] When the extracted second tire pressure is less than or equal to the second pressure threshold, the extracted second tire pressure is used as a target to retrieve the tire pressure, and the target retrieved tire pressure is used to confirm a second reference tire pressure time sequence in the second tire pressure time sequence;

[0033] Among them, the second reference tire pressure time series is as follows:

[0034] T2={q1,q2,…,q i ,…,q n}

[0035] Wherein, T2 represents the second reference tire pressure time series, q1, q2, ..., q i ,…,q n Both represent the second target tire pressure, and q i represents the i-th second target tire pressure in the second reference tire pressure time series, and q n ≤Q A , where Q A is the second pressure threshold;

[0036] The reference mean relationship is as follows:

[0037]

[0038] Wherein, J represents the mean value of the second tire pressure, Δt represents the sampling time interval, H represents the time corresponding to the first second target tire pressure in the second reference tire pressure time series, a and b are only used for counting, and q b represents the bth second target tire pressure in the second reference tire pressure time series;

[0039] The second tire pressure means are summarized to obtain a second tire pressure mean set.

[0040] Optionally, judging whether the first historical data is the second historical data based on the initial tire pressure time sequence, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean, and the second tire pressure variance includes:

[0041] The following operations are performed on the first tire pressure time series in the first tire pressure time series set:

[0042] Retrieving a first target tire pressure in the first tire pressure time sequence, wherein the first target tire pressure is greater than the second pressure threshold, and a collection time interval between the first target tire pressure and the nth first tire pressure in the first tire pressure time sequence is the shortest;

[0043] Obtaining a first reference absolute time based on the first target tire pressure and the nth first tire pressure, summarizing the first reference absolute times to obtain a first reference absolute time set, and obtaining a first absolute time mean using the first reference absolute time set, wherein the first absolute time mean is the mean of multiple first reference absolute times in the first reference absolute time set;

[0044] The initial tire pressures are sequentially extracted from the initial tire pressure time series, and the following operations are performed on the extracted initial tire pressures:

[0045] comparing the extracted initial tire pressure with the second pressure threshold;

[0046] When the extracted initial tire pressure is less than the second pressure threshold, the target tire pressure is retrieved from the initial tire pressure time series using the extracted initial tire pressure, a target absolute time is obtained based on the target tire pressure and the extracted initial tire pressure, a target tire pressure time series is confirmed based on the extracted initial tire pressure, and a target tire mean and a target tire variance are obtained using the target tire pressure time series;

[0047] Acquire a second absolute time mean value based on the second tire pressure time series set;

[0048] Calculate the first similarity and the second similarity using a pre-constructed similarity relation, a first tire pressure mean, a first tire pressure variance, a second tire pressure mean, a second tire pressure variance, a target absolute time, a target tire mean, a target tire variance, a first absolute time mean, and a second absolute time mean;

[0049] comparing the first similarity with the second similarity;

[0050] If the second similarity is less than or equal to the first similarity, the first historical data is confirmed to be the second historical data.

[0051] Optionally, the similarity relationship is as follows:

[0052]

[0053] Among them, X1 and X2 represent the first similarity and the second similarity respectively, μ1 and μ2 represent the first tire pressure mean and the second tire pressure mean respectively, σ1 and σ2 represent the first tire pressure variance and the second tire pressure variance respectively, t1 and t2 represent the first absolute time mean and the second absolute time mean respectively, μ0 represents the target tire mean, σ0 represents the target tire variance, and t0 represents the target absolute time.

[0054] Optionally, the obtaining an initial fitting stage set by using the target historical data set includes:

[0055] The following operations are performed on the target historical data in the target historical data set:

[0056] The target tire pressure time series corresponding to the target historical data is used as the sampling period, and the target driving temperature time series, the target driving speed time series and the target driving acceleration time series are obtained respectively, and the target tire service life is obtained based on the target historical data;

[0057] The target driving temperature time series and the target driving speed time series are used to respectively obtain the target driving temperature mean, the target driving temperature variance, the target driving speed mean and the target driving speed variance;

[0058] Obtaining the target driving acceleration absolute mean and the target driving acceleration absolute variance based on the target driving acceleration time series;

[0059] Associating the target driving temperature mean, the target driving temperature variance, the target driving speed mean, the target driving speed variance, the target driving acceleration absolute mean, and the target driving acceleration absolute variance with the target tire service life to obtain initial fitting data;

[0060] The initial fitting data are aggregated to obtain an initial fitting stage set.

[0061] Optionally, centrally identifying one or more first target fitting stages in an initial fitting stage according to the target analysis data includes:

[0062] A screening relationship is constructed based on the tire service life corresponding to the target analysis data, wherein the screening relationship is as follows:

[0063] S=(αT,βT)

[0064] Wherein, S represents the screening relationship, α and β are both preset coefficients, and T represents the tire service life;

[0065] The following operations are performed on the initial fitting data in the initial fitting stage:

[0066] Determining whether the target tire service life corresponding to the initial fitting data satisfies the screening relationship;

[0067] If the target tire service life corresponding to the initial fitting data satisfies the screening relationship, the initial fitting data is retained;

[0068] Otherwise, the initial fitting data is discarded;

[0069] The retained initial fitting data are aggregated to obtain one or more first target fitting stages.

[0070] Optionally, obtaining fitting analysis values ​​based on the one or more first target fitting stages includes:

[0071] Using the pre-constructed hierarchical analysis method, the index weight set is obtained with the associated data corresponding to the first target fitting stage as the index, wherein the associated data is composed of 7 different associated values, and the 7 different associated values ​​are: target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life, wherein the index weight set includes multiple index weight values, and the index weight values ​​correspond to the associated values ​​one by one;

[0072] Using the target analysis data, the target analysis temperature mean, the target analysis temperature variance, the target analysis speed mean, the target analysis speed variance, the target analysis acceleration absolute mean, and the target analysis acceleration absolute variance are obtained;

[0073] A fitting analysis matrix is ​​constructed based on the indicator weight set, one or more first target fitting stages, and the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, and target analysis acceleration absolute variance, wherein the fitting analysis matrix is ​​as follows:

[0074]

[0075] Wherein, B represents the fitting analysis matrix, γ1, γ2, γ3, γ4, γ5, γ6, and γ7 represent the weight values ​​corresponding to the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life in the index weight set, respectively, W0, R0, E0, Y0, U0, I0, and O0 represent the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, target analysis acceleration absolute variance, and tire service life corresponding to the target analysis data, W1, R1, E1, Y1, U1, I1, and O1 represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life corresponding to the first target fitting stage in one or more first target fitting stages, respectively, and k represents that there are k first target fitting stages in total;

[0076] Calculating comprehensive evaluation values ​​based on the fitting analysis matrix and the pre-constructed comprehensive evaluation relationship, and summarizing the comprehensive evaluation values ​​to obtain a comprehensive evaluation value set;

[0077] The comprehensive evaluation value set is screened to obtain a target comprehensive value set, and the fitting analysis value is obtained using the target comprehensive value set.

[0078] Optionally, the comprehensive evaluation relationship is as follows:

[0079]

[0080] Among them, Z g represents the comprehensive evaluation value corresponding to the g-th first target fitting stage in one or more first target fitting stages, W g , R g 、E g , Y g , U g ,I g , O g They respectively represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life corresponding to the g-th first target fitting stage in one or more first target fitting stages.

[0081] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0082] at least one processor; and,

[0083] a memory communicatively connected to the at least one processor; wherein,

[0084] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned vehicle safety protection intelligent prompt alarm method.

[0085] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned vehicle safety protection intelligent prompt alarm method.

[0086] Compared with the problem described in the background technology, the present invention first confirms the receipt of the data acquisition instruction from the data acquisition unit, and obtains the fault history data set based on the data acquisition instruction, wherein the fault history data set includes multiple fault history data. It can be seen that the present invention not only considers the existing data but also the fault history data. Furthermore, the embodiment of the present invention obtains the target history data set based on the fault history data set, and uses the target history data set to obtain the initial fitting stage set. It can be seen that the embodiment of the present invention not only considers the fault history data set, but also screens the fault history data in the fault history data set multiple times, and formulates the first tire pressure time series set and the second tire pressure time series set, and uses the first tire pressure time series set and the second tire pressure time series set to realize the screening of the fault history data, so as to improve the accuracy of the obtained target history data, and the initial fitting stage of the embodiment of the present invention not only considers the tire pressure but also considers the temperature, the tire life and its related parameters, so as to improve the accuracy of the safety protection prompts for tire explosion prevention. The embodiment of the present invention confirms receiving an information receiving instruction from an information receiving unit, and uses the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series; one or more first target fitting stages are centrally confirmed in the initial fitting stage according to the target analysis data; fitting analysis values ​​are obtained based on the one or more first target fitting stages; and the target analysis values ​​are obtained based on the target analysis data; when the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction. It can be seen that the embodiment of the present invention combines the target analysis data to perform multi-faceted screening on the initial fitting stage set, wherein the screened initial fitting stage set is one or more first target fitting stages, and further, the one or more first target fitting stages obtained can more accurately characterize the target analysis data, and when the one or more first target fitting stages are used to obtain the fitting analysis values, the preset coefficients are incorporated, so that when the fitting analysis values ​​are used as the judgment basis, the safety of the tire explosion-proof prompt alarm can be improved. Therefore, the vehicle safety protection intelligent prompt alarm method, device, electronic device and computer-readable storage medium proposed in the present invention mainly aim to achieve intelligent explosion-proof prompts for tires. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A schematic diagram of a process flow of a vehicle safety protection intelligent prompt alarm method provided by an embodiment of the present invention;

[0088] Figure 2 A schematic diagram of the structure of an electronic device for implementing the vehicle safety protection intelligent prompt alarm method provided by an embodiment of the present invention.

[0089] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0090] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0091] The embodiment of the present application provides a vehicle safety protection intelligent prompt alarm method. The execution subject of the vehicle safety protection intelligent prompt alarm method includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the vehicle safety protection intelligent prompt alarm method can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0092] Embodiment 1:

[0093] Reference Figure 1 FIG. 1 is a flow chart of a vehicle safety protection intelligent prompt alarm method provided by an embodiment of the present invention. In this embodiment, the vehicle safety protection intelligent prompt alarm method includes:

[0094] S1. Receive an intelligent prompt instruction, and confirm an intelligent prompt system based on the intelligent prompt instruction, wherein the intelligent prompt system includes: a data collection unit, an information receiving unit, and a result feedback unit.

[0095] It should be explained that the intelligent prompt instruction is an instruction issued by the vehicle user. The intelligent prompt system refers to software or APP used for intelligent prompt alarm for vehicle safety protection, and the intelligent prompt system includes: a data acquisition unit, an information receiving unit and a result feedback unit. For the specific application of the unit, please refer to the subsequent embodiments. In general, when a vehicle has a tire blowout during driving, it will cause the vehicle to lose control and even cause serious danger. Therefore, the embodiments of the present invention mainly focus on the safety protection of vehicle tires and intelligent prompt alarms.

[0096] For example, Xiao Zhang is going to drive the vehicle through a highway. Therefore, before driving the vehicle, Xiao Zhang initiates the intelligent prompt instruction on the on-board computer to implement intelligent protection prompts for the vehicle when driving on the highway, thereby avoiding accidents caused by tire blowouts.

[0097] S2. Confirm receipt of a data acquisition instruction from a data acquisition unit, and acquire a fault history data set based on the data acquisition instruction, wherein the fault history data set includes a plurality of fault history data.

[0098] It is understandable that the fault history data refers to data recorded when the vehicle is repaired, wherein the data may include information such as the time when the vehicle was repaired, the model of the vehicle, etc. Optionally, the fault history data set is obtained by aggregating data records when the vehicle is repaired in multiple auto repair shops.

[0099] S3. Acquire a target historical data set based on the fault historical data set, and use the target historical data set to acquire an initial fitting stage set, wherein the initial fitting stage set includes a plurality of initial fitting data.

[0100] It should be understood that the step of acquiring a target historical data set based on the fault historical data set includes:

[0101] Obtaining a diagnostic constraint set for classifying the fault history data, wherein the diagnostic constraint set is composed of three different diagnostic constraints, and the three different diagnostic constraints are: tire damage cause, tire damage time and tire model, wherein the tire damage cause is composed of tire self-burst or tire external force burst;

[0102] Extracting fault history data from the fault history data set in sequence, and performing the following operations on the extracted fault history data:

[0103] Using the diagnostic constraint set, performing a marking operation on the extracted fault history data to obtain marked history data;

[0104] If the identified historical data lacks at least one diagnostic constraint in the diagnostic constraint set, the identified historical data is discarded;

[0105] Otherwise, identifying the historical data as first historical data, and determining whether the first historical data is preset second historical data;

[0106] If the first historical data is not the second historical data, discarding the first historical data;

[0107] If the first historical data is the second historical data, aggregating the second historical data to obtain a second historical data set;

[0108] A target historical data set is acquired based on the second historical data set.

[0109] It is understandable that a tire blowout refers to a blowout caused by the tire itself, for example, when the tire is actually used, the tire is too thin due to severe wear, so the tire's load-bearing capacity is greatly reduced, and then, when carrying the same mass of goods, the tire blowout occurs due to excessive wear. A tire blowout caused by external force refers to a blowout caused by the tire being affected by the outside world, for example, a tire blowout caused by being pierced by a sharp object on the road. Tire damage time refers to the time when the tire cannot be used further due to tire damage. A tire model refers to the model of the tire corresponding to the standard implemented during the production of the tire. For example, there are now three tires with the same size parameters, among which different tires have different production processes or materials used, and these three tires are respectively classified as the first tire model, the second tire model and the third tire model. Different tire models may have different actual service lives under the same working conditions. Therefore, considering the tire model can improve the accuracy of analyzing the cause of tire blowout. For the specific analysis process, please refer to the subsequent embodiments.

[0110] Exemplarily, the fault history data set includes three fault history data, wherein the first fault history data is composed of tire blowout and type A tire model, the second fault history data is composed of the first tire damage time and type A tire model, and the third fault history data is composed of tire blowout due to external force, the second tire damage time and type A tire model, wherein type A tire model here represents a specific tire model. The third fault history data is marked using the diagnostic constraint set to obtain 1-second tire damage time-A, wherein 1 represents that the tire damage cause is tire blowout due to external force, and A represents type A tire model, wherein the effects that can be achieved by the first fault history data and the second fault history data are the same as the effects that can be achieved by the third fault history data, which will not be repeated here. Therefore, through the marked three fault history data and the diagnostic constraint set, the marked first fault history data and the marked second fault history data can be eliminated, and the marked three fault history data are the first history data.

[0111] It should be explained that the determining whether the first historical data is the preset second historical data includes:

[0112] A first tire pressure time series set and a second tire pressure time series set for reference are obtained, wherein the first tire pressure time series set includes a plurality of first tire pressure time series, the second tire pressure time series set includes a plurality of second tire pressure time series, and the collection time intervals of the first tire pressure time series and the second tire pressure time series are preset, wherein the tire damage cause corresponding to the first tire pressure time series set is: tire self-burst, and the tire damage cause corresponding to the second tire pressure time series set is: tire external force burst, wherein the first tire pressure time series is as follows:

[0113] T1={P1,P2,…,P i ,…,P n}

[0114] Wherein, T1 represents the first tire pressure time sequence, P1, P2, ..., P i ,…,P n Both represent the first tire pressure, and P i represents the i-th first tire pressure in the first tire pressure time series, n represents the preset collected value, and P n =0;

[0115] Based on the first tire pressure time series set, obtaining a first tire pressure mean and a first tire pressure variance, wherein the first tire pressure mean and the first tire pressure variance are respectively the mean and variance of a plurality of first tire pressure time series in the first tire pressure time series set;

[0116] Based on the second tire pressure time series set and a preset reference mean relationship, a second tire pressure mean value set is obtained, wherein the second tire pressure mean value set includes a plurality of second tire pressure means, and based on the second tire pressure mean value set, a second tire pressure mean value and a second tire pressure variance are obtained, wherein the second tire pressure mean value and the second tire pressure variance are respectively the mean and variance of the plurality of second tire pressure means in the second tire pressure mean value set;

[0117] An initial tire pressure time series is obtained using the first historical data, and whether the first historical data is the second historical data is determined based on the initial tire pressure time series, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean, and the second tire pressure variance.

[0118] Furthermore, when other conditions are the same, which may include: vehicle load, vehicle driving status, tire service life, etc., when the tire blowout is caused by the tire itself, it will often show a certain regularity in the first tire pressure time series obtained. For example, when other conditions are the same, the first pressure mean value in the first tire pressure time series corresponding to a certain tire will be higher than the pressure mean value corresponding to a normally used tire.

[0119] It should be understood that, optionally, a tire pressure time sequence is obtained through a tire monitoring system, wherein the tire pressure time sequence refers to a pressure sequence obtained by sorting the vehicle tire pressure change values ​​in a time-ordered order. The first tire pressure time sequence is obtained by first confirming the first tire pressure that meets a preset condition in the tire pressure time sequence, and the condition is P n =0, taking the first tire pressure that meets the preset condition as the starting point, the first tire pressure is retrieved from the tire pressure sequence. For example, the tire pressure sequence is: 5.1, 5.2, ..., 5.3, 5.6, 5.6, 5.7, 5.7, 5.7, 5.8, 5.8, 6.0 and 0, and the collected value is 6, then the first tire pressure sequence obtained through the tire pressure sequence is: 5.7, 5.7, 5.8, 5.8, 6.0 and 0.

[0120] It should be explained that the step of obtaining the second tire pressure mean value set based on the second tire pressure time series set and a preset reference mean value relationship formula includes:

[0121] The following operations are performed on the second tire pressure time series in the second tire pressure time series set:

[0122] The second tire pressure is sequentially extracted from the second tire pressure time series, and the following operations are performed on the extracted second tire pressure:

[0123] comparing the extracted second tire pressure with a preset second pressure threshold;

[0124] When the extracted second tire pressure is less than or equal to the second pressure threshold, the extracted second tire pressure is used as a target to retrieve the tire pressure, and the target retrieved tire pressure is used to confirm a second reference tire pressure time sequence in the second tire pressure time sequence;

[0125] Among them, the second reference tire pressure time series is as follows:

[0126] T2={q1,q2,…,q i ,…,q n}

[0127] Wherein, T2 represents the second reference tire pressure time series, q1, q2, ..., q i ,…,q n Both represent the second target tire pressure, and q i represents the i-th second target tire pressure in the second reference tire pressure time series, and q n ≤Q A , where Q A is the second pressure threshold;

[0128] The reference mean relationship is as follows:

[0129]

[0130] Wherein, J represents the mean value of the second tire pressure, Δt represents the sampling time interval, H represents the time corresponding to the first second target tire pressure in the second reference tire pressure time series, a and b are only used for counting, and q b represents the bth second target tire pressure in the second reference tire pressure time series;

[0131] The second tire pressure means are summarized to obtain a second tire pressure mean set.

[0132] It is understandable that the method for obtaining the second tire pressure time series is the same as the method for obtaining the tire pressure time series, and can achieve the same effect, which will not be repeated here. The method for obtaining the second tire pressure time series is: confirming the target tire pressure that meets the preset conditions in the tire pressure time series, and the preset conditions in the second tire pressure time series are: q n ≤Q A , and the method for obtaining the second tire pressure time series is the same as the method for obtaining the first tire pressure time series, which will not be repeated here.

[0133] It should be explained that, in the embodiment of the present invention, unlike the tire bursting itself, the process of tire bursting due to external force is a long process. For example, after the tire is punctured by a sharp object, part of the gas in the tire leaks, but the tire after the leak can still be used normally. During the continued driving process, the tire bursts due to tire wear or rolling. Therefore, it is necessary to consider the time corresponding to different second target tire pressures in the second tire pressure time series to calculate the second tire pressure mean, thereby improving the accuracy of the calculated second tire pressure mean.

[0134] It should be understood that the determining whether the first historical data is the second historical data based on the initial tire pressure time sequence, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean and the second tire pressure variance includes:

[0135] The following operations are performed on the first tire pressure time series in the first tire pressure time series set:

[0136] Retrieving a first target tire pressure in the first tire pressure time sequence, wherein the first target tire pressure is greater than the second pressure threshold, and a collection time interval between the first target tire pressure and the nth first tire pressure in the first tire pressure time sequence is the shortest;

[0137] Obtaining a first reference absolute time based on the first target tire pressure and the nth first tire pressure, summarizing the first reference absolute times to obtain a first reference absolute time set, and obtaining a first absolute time mean using the first reference absolute time set, wherein the first absolute time mean is the mean of multiple first reference absolute times in the first reference absolute time set;

[0138] The initial tire pressures are sequentially extracted from the initial tire pressure time series, and the following operations are performed on the extracted initial tire pressures:

[0139] comparing the extracted initial tire pressure with the second pressure threshold;

[0140] When the extracted initial tire pressure is less than the second pressure threshold, the target tire pressure is retrieved from the initial tire pressure time series using the extracted initial tire pressure, a target absolute time is obtained based on the target tire pressure and the extracted initial tire pressure, a target tire pressure time series is confirmed based on the extracted initial tire pressure, and a target tire mean and a target tire variance are obtained using the target tire pressure time series;

[0141] Acquire a second absolute time mean value based on the second tire pressure time series set;

[0142] Calculate the first similarity and the second similarity using a pre-constructed similarity relation, a first tire pressure mean, a first tire pressure variance, a second tire pressure mean, a second tire pressure variance, a target absolute time, a target tire mean, a target tire variance, a first absolute time mean, and a second absolute time mean;

[0143] comparing the first similarity with the second similarity;

[0144] If the second similarity is less than or equal to the first similarity, the first historical data is confirmed to be the second historical data.

[0145] It should be understood that the embodiment of the present invention defines the difference in the time process between a tire blowout caused by itself and a tire blowout caused by external forces, that is, the time corresponding to a tire blowout caused by itself should be much shorter than the time corresponding to a tire blowout caused by external forces. The key point is that a tire blowout caused by external forces is generally uncontrollable, so a tire blowout caused by external forces is not the subject of the present invention. The reason for distinguishing between a tire blowout caused by external forces and a tire blowout caused by itself is to screen the identification historical data again to improve the accuracy of the identification historical data after screening, and further, to improve the accuracy of the safety protection prompts for vehicle tires.

[0146] It is understandable that the first reference absolute time is the absolute difference between the first target tire pressure and the time corresponding to the nth first tire pressure. The method of retrieving the target tire pressure in the initial tire pressure time sequence is the same as the method of retrieving the first target tire pressure in the first tire pressure time sequence, and can achieve the same effect, which will not be repeated here. The target absolute time has the same effect as the first reference absolute time, which will not be repeated here.

[0147] It should be understood that determining the target tire pressure sequence based on the extracted initial tire pressure means taking the extracted initial tire pressure as the starting point, taking n-1 initial tire pressures forward in the initial tire pressure sequence, and sorting these n initial tire pressures in order of time from first to last to obtain the target tire pressure sequence. The target tire mean and the first tire pressure mean are obtained in the same way as the second tire pressure mean, and can achieve the same effect. The first tire pressure variance and the target tire variance are obtained in the same way as the first tire pressure variance, and can achieve the same effect.

[0148] Furthermore, the similarity relationship is as follows:

[0149]

[0150] Among them, X1 and X2 represent the first similarity and the second similarity respectively, μ1 and μ2 represent the first tire pressure mean and the second tire pressure mean respectively, σ1 and σ2 represent the first tire pressure variance and the second tire pressure variance respectively, t1 and t2 represent the first absolute time mean and the second absolute time mean respectively, μ0 represents the target tire mean, σ0 represents the target tire variance, and t0 represents the target absolute time.

[0151] It should be explained that the first similarity and the second similarity respectively represent the similarity between the target tire pressure timing series and the first tire pressure timing series set and the second tire pressure timing series set. When the first similarity is greater than or equal to the second similarity, it can be determined that the cause of tire damage corresponding to the target tire pressure timing series and the first tire pressure timing series set is the same.

[0152] Further, the acquiring a target historical data set based on the second historical data set includes:

[0153] Obtain the target tire model as the target, and perform the following operations on each second historical data in the second historical data set:

[0154] If the tire model corresponding to the second historical data is the same as the target tire model, the second historical data is used as the target historical data;

[0155] The target historical data is aggregated to obtain a target historical data set.

[0156] It should be explained that the use of the target historical data set to obtain the initial fitting stage set includes:

[0157] The following operations are performed on the target historical data in the target historical data set:

[0158] The target tire pressure time series corresponding to the target historical data is used as the sampling period, and the target driving temperature time series, the target driving speed time series and the target driving acceleration time series are obtained respectively, and the target tire service life is obtained based on the target historical data;

[0159] The target driving temperature time series and the target driving speed time series are used to respectively obtain the target driving temperature mean, the target driving temperature variance, the target driving speed mean and the target driving speed variance;

[0160] Obtaining the target driving acceleration absolute mean and the target driving acceleration absolute variance based on the target driving acceleration time series;

[0161] Associating the target driving temperature mean, the target driving temperature variance, the target driving speed mean, the target driving speed variance, the target driving acceleration absolute mean, and the target driving acceleration absolute variance with the target tire service life to obtain initial fitting data;

[0162] The initial fitting data are aggregated to obtain an initial fitting stage set.

[0163] It should be understood that the sampling period refers to the time period corresponding to the target tire pressure sequence, and the sampling period can be confirmed by the last target tire pressure in the target tire pressure sequence. For example: the acquired target tire pressure sequence includes a total of 8 target tire pressures, and the last target tire pressure is 0, and the time corresponding to the last target tire pressure is 8 o'clock in the evening, and the sampling time interval is once every 150 seconds, then the sampling period can be confirmed at 8 o'clock in the evening: 7:40 to 8 o'clock in the evening. The method of obtaining the target driving temperature sequence, target driving speed sequence and target driving acceleration sequence using this sampling period is the same as the method of obtaining the target tire pressure sequence, and can achieve the same effect, which will not be repeated here. Optionally, the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean and target driving acceleration absolute variance are associated with the target tire service life in the form of a sampling matrix. For example, if the target driving temperature mean is 2, the target driving temperature variance is 3, the target driving speed mean is 4, the target driving speed variance is 5, the target driving acceleration absolute mean is 6, the target driving acceleration absolute variance is 7, and the target tire service life is 8, then these parameters are associated, and the initial fitting data obtained is as follows:

[0164] N={2,3,4,5,6,7,8}

[0165] Wherein, N represents the initial fitting data.

[0166] It should be understood that the acquisition of the target driving acceleration absolute mean and the target driving acceleration absolute variance based on the target driving acceleration time series includes:

[0167] The following operations are performed for the target driving acceleration in the target driving acceleration sequence:

[0168] An absolute value operation is performed on the target driving acceleration to obtain a reference driving acceleration, the reference driving accelerations are summarized to obtain a reference driving acceleration set, and an absolute mean of the target driving acceleration and an absolute variance of the target driving acceleration are obtained based on the reference driving acceleration set, wherein the absolute mean of the target driving acceleration and the absolute variance of the target driving acceleration are respectively the mean and variance of multiple reference driving accelerations in the reference driving acceleration set.

[0169] S4. Confirm receipt of the information receiving instruction from the information receiving unit, and use the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure timing, tire service life, driving acceleration timing, driving speed timing and tire temperature timing.

[0170] It is understandable that the tire service life refers to the life of the tire when it is actually used. Optionally, a speed sensor is used to obtain the driving speed time series at the sampling time interval, an acceleration sensor is used to obtain the driving acceleration time series at the sampling time interval, a temperature sensor is used to obtain the tire temperature time series at the sampling time interval, and a tire pressure monitoring system is used to obtain the tire pressure time series at the sampling time interval. Other technologies can achieve the same effect and are not described in detail here.

[0171] S5. Confirm one or more first target fitting stages in the initial fitting stage according to the target analysis data, obtain fitting analysis values ​​based on the one or more first target fitting stages, obtain target analysis values ​​based on the target analysis data, and when the target analysis value is greater than or equal to the fitting analysis value, confirm receipt of feedback instructions from the result feedback unit, and implement intelligent prompt alarm for vehicle safety protection based on the feedback instructions.

[0172] It should be understood that the step of centrally identifying one or more first target fitting stages in the initial fitting stage based on the target analysis data includes:

[0173] A screening relationship is constructed based on the tire service life corresponding to the target analysis data, wherein the screening relationship is as follows:

[0174] S=(αT,βT)

[0175] Wherein, S represents the screening relationship, α and β are both preset coefficients, and T represents the tire service life;

[0176] The following operations are performed on the initial fitting data in the initial fitting stage:

[0177] Determining whether the target tire service life corresponding to the initial fitting data satisfies the screening relationship;

[0178] If the target tire service life corresponding to the initial fitting data satisfies the screening relationship, the initial fitting data is retained;

[0179] Otherwise, the initial fitting data is discarded;

[0180] The retained initial fitting data are aggregated to obtain one or more first target fitting stages.

[0181] Furthermore, the screening relationship can be used to preliminarily screen out data with obvious errors from the initial fitting data. For example, a tire has a quality problem that causes the target tire service life to be much shorter than the normal value, or a vehicle has not been used for a long time, causing the target tire service life to be much longer than the normal value.

[0182] It should be explained that the obtaining of fitting analysis values ​​based on the one or more first target fitting stages includes:

[0183] Using the pre-constructed hierarchical analysis method, the index weight set is obtained with the associated data corresponding to the first target fitting stage as the index, wherein the associated data is composed of 7 different associated values, and the 7 different associated values ​​are: target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life, wherein the index weight set includes multiple index weight values, and the index weight values ​​correspond to the associated values ​​one by one;

[0184] Using the target analysis data, the target analysis temperature mean, the target analysis temperature variance, the target analysis speed mean, the target analysis speed variance, the target analysis acceleration absolute mean, and the target analysis acceleration absolute variance are obtained;

[0185] A fitting analysis matrix is ​​constructed based on the indicator weight set, one or more first target fitting stages, and the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, and target analysis acceleration absolute variance, wherein the fitting analysis matrix is ​​as follows:

[0186]

[0187] Wherein, B represents the fitting analysis matrix, γ1, γ2, γ3, γ4, γ5, γ6, and γ7 represent the weight values ​​corresponding to the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life in the index weight set, respectively, W0, R0, E0, Y0, U0, I0, and O0 represent the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, target analysis acceleration absolute variance, and tire service life corresponding to the target analysis data, W1, R1, E1, Y1, U1, I1, and O1 represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life corresponding to the first target fitting stage in one or more first target fitting stages, respectively, and k represents that there are k first target fitting stages in total;

[0188] Calculating comprehensive evaluation values ​​based on the fitting analysis matrix and the pre-constructed comprehensive evaluation relationship, and summarizing the comprehensive evaluation values ​​to obtain a comprehensive evaluation value set;

[0189] The comprehensive evaluation value set is screened to obtain a target comprehensive value set, and the fitting analysis value is obtained using the target comprehensive value set.

[0190] It should be understood that the analytic hierarchy process is a prior art and will not be described in detail here. The analytic hierarchy process can be used to obtain the indicator weight value corresponding to each associated data in the associated data by taking the associated data as an indicator, and the set of all indicator weight values ​​is the indicator weight set. The target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean and target analysis acceleration absolute variance are obtained in the same way as the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean and target driving acceleration absolute variance, respectively, and can achieve the same effect, so they will not be described in detail here.

[0191] It should be explained that the comprehensive evaluation relationship is as follows:

[0192]

[0193] Among them, Z g represents the comprehensive evaluation value corresponding to the g-th first target fitting stage in one or more first target fitting stages, W g , R g 、E g , Yg , U g ,I g , O g They respectively represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life corresponding to the g-th first target fitting stage in one or more first target fitting stages.

[0194] It should be understood that the smaller the comprehensive evaluation value is, the closer the data corresponding to the first target fitting stage is to the target analysis data. Therefore, the comprehensive evaluation value can be used to screen multiple first target fitting stages, making the screened multiple first target fitting stages more accurate, thereby improving the accuracy of tire safety protection prompts.

[0195] It is understandable that the screening of the comprehensive evaluation value set to obtain the target comprehensive value set includes:

[0196] The following operations are performed on the comprehensive evaluation values ​​in the comprehensive evaluation value set:

[0197] comparing the comprehensive evaluation value with a preset comprehensive evaluation threshold;

[0198] If the comprehensive evaluation value is less than or equal to the comprehensive evaluation threshold, the comprehensive evaluation value is retained;

[0199] Otherwise, the comprehensive evaluation value is eliminated, and the retained comprehensive evaluation values ​​are summarized to obtain the target evaluation value set.

[0200] It should be understood that the use of the target comprehensive value set to obtain the fitting analysis value includes:

[0201] Extract comprehensive evaluation values ​​from the target comprehensive value set in sequence, and perform the following operations on the extracted comprehensive evaluation values:

[0202] The fitting analysis value is calculated based on the first target fitting stage corresponding to the extracted comprehensive evaluation value and the pre-constructed fitting analysis relationship, wherein the fitting analysis relationship is as follows:

[0203]

[0204] Wherein, H represents the fitting analysis value, W b , R b 、E b , Y b , U b ,I b , O bThey respectively represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life corresponding to the bth first target fitting stage in one or more first target fitting stages, and σ is a preset coefficient.

[0205] It can be understood that the target analysis value refers to the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, target analysis acceleration absolute variance and weighted mean of tire service life corresponding to the target analysis data.

[0206] It should be understood that when the target analysis value is greater than or equal to the fitting analysis value, it indicates that there may be a risk of tire blowout, and therefore, a prompt needs to be initiated in time to avoid tire blowout.

[0207] Exemplarily, Xiao Wang initiates an intelligent prompt command before driving the vehicle. The intelligent prompt system will dynamically update the target analysis value according to the collection time interval. When the target analysis value is greater than or equal to the fitting analysis value, the intelligent prompt system will remind Xiao Wang that there may be a risk of tire blowout, so that Xiao Wang can slow down or take a rest nearby to avoid the tire itself from blowing out.

[0208] Compared with the problem described in the background technology, the present invention first confirms the receipt of the data acquisition instruction from the data acquisition unit, and obtains the fault history data set based on the data acquisition instruction, wherein the fault history data set includes multiple fault history data. It can be seen that the present invention not only considers the existing data but also the fault history data. Furthermore, the embodiment of the present invention obtains the target history data set based on the fault history data set, and uses the target history data set to obtain the initial fitting stage set. It can be seen that the embodiment of the present invention not only considers the fault history data set, but also screens the fault history data in the fault history data set multiple times, and formulates the first tire pressure time series set and the second tire pressure time series set, and uses the first tire pressure time series set and the second tire pressure time series set to realize the screening of the fault history data, so as to improve the accuracy of the obtained target history data, and the initial fitting stage of the embodiment of the present invention not only considers the tire pressure but also considers the temperature, the tire life and its related parameters, so as to improve the accuracy of the safety protection prompts for tire explosion prevention. The embodiment of the present invention confirms receiving an information receiving instruction from an information receiving unit, and uses the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series; one or more first target fitting stages are centrally confirmed in the initial fitting stage according to the target analysis data; fitting analysis values ​​are obtained based on the one or more first target fitting stages; and the target analysis values ​​are obtained based on the target analysis data; when the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction. It can be seen that the embodiment of the present invention combines the target analysis data to perform multi-faceted screening on the initial fitting stage set, wherein the screened initial fitting stage set is one or more first target fitting stages, and further, the one or more first target fitting stages obtained can more accurately characterize the target analysis data, and when the one or more first target fitting stages are used to obtain the fitting analysis values, the preset coefficients are incorporated, so that when the fitting analysis values ​​are used as the judgment basis, the safety of the tire explosion-proof prompt alarm can be improved. Therefore, the vehicle safety protection intelligent prompt alarm method, device, electronic device and computer-readable storage medium proposed in the present invention mainly aim to achieve intelligent explosion-proof prompts for tires.

[0209] Embodiment 2:

[0210] like Figure 2 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a vehicle safety protection intelligent prompt alarm method provided by an embodiment of the present invention.

[0211] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a vehicle safety protection intelligent prompt alarm program.

[0212] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the vehicle safety protection intelligent prompt alarm program, but also be used to temporarily store data that has been output or is to be output.

[0213] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as vehicle safety protection intelligent prompt alarm programs, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0214] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0215] Figure 2 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0216] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0217] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0218] Optionally, the electronic device 1 may further include a user interface, which may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0219] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0220] The vehicle safety protection intelligent prompt alarm program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0221] Receiving an intelligent prompt instruction, and confirming an intelligent prompt system based on the intelligent prompt instruction, wherein the intelligent prompt system includes: a data collection unit, an information receiving unit, and a result feedback unit;

[0222] Confirming receipt of a data acquisition instruction from a data acquisition unit, and acquiring a fault history data set based on the data acquisition instruction, wherein the fault history data set includes a plurality of fault history data;

[0223] Acquire a target historical data set based on the fault historical data set, and acquire an initial fitting stage set using the target historical data set, wherein the initial fitting stage set includes a plurality of initial fitting data;

[0224] Confirming receipt of an information receiving instruction from an information receiving unit, and using the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series;

[0225] According to the target analysis data, one or more first target fitting stages are centrally identified in the initial fitting stage, fitting analysis values ​​are obtained based on the one or more first target fitting stages, and a target analysis value is obtained based on the target analysis data. When the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and an intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction.

[0226] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figure 1 to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0227] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0228] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0229] Receiving an intelligent prompt instruction, and confirming an intelligent prompt system based on the intelligent prompt instruction, wherein the intelligent prompt system includes: a data collection unit, an information receiving unit, and a result feedback unit;

[0230] Confirming receipt of a data acquisition instruction from a data acquisition unit, and acquiring a fault history data set based on the data acquisition instruction, wherein the fault history data set includes a plurality of fault history data;

[0231] Acquire a target historical data set based on the fault historical data set, and acquire an initial fitting stage set using the target historical data set, wherein the initial fitting stage set includes a plurality of initial fitting data;

[0232] Confirming receipt of an information receiving instruction from an information receiving unit, and using the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series;

[0233] According to the target analysis data, one or more first target fitting stages are centrally identified in the initial fitting stage, fitting analysis values ​​are obtained based on the one or more first target fitting stages, and a target analysis value is obtained based on the target analysis data. When the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and an intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction.

[0234] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0235] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0236] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A vehicle safety protection intelligent prompt alarm method, characterized in that: The method comprises: Receiving an intelligent prompt instruction, and confirming an intelligent prompt system based on the intelligent prompt instruction, wherein the intelligent prompt system includes: a data collection unit, an information receiving unit, and a result feedback unit; Confirming receipt of a data acquisition instruction from a data acquisition unit, and acquiring a fault history data set based on the data acquisition instruction, wherein the fault history data set includes a plurality of fault history data; Acquire a target historical data set based on the fault historical data set, and acquire an initial fitting stage set using the target historical data set, wherein the initial fitting stage set includes a plurality of initial fitting data; Confirming receipt of an information receiving instruction from an information receiving unit, and using the information receiving instruction to obtain target analysis data, wherein the target analysis data is composed of: tire pressure time series, tire service life, driving acceleration time series, driving speed time series and tire temperature time series; One or more first target fitting stages are centrally identified in the initial fitting stage according to the target analysis data, fitting analysis values ​​are obtained based on the one or more first target fitting stages, and a target analysis value is obtained based on the target analysis data; when the target analysis value is greater than or equal to the fitting analysis value, it is confirmed that a feedback instruction from a result feedback unit is received, and an intelligent prompt alarm for vehicle safety protection is implemented based on the feedback instruction.

2. The vehicle safety protection intelligent prompt alarm method according to claim 1, characterized in that: The acquiring a target historical data set based on the fault historical data set includes: Obtaining a diagnostic constraint set for classifying the fault history data, wherein the diagnostic constraint set is composed of three different diagnostic constraints, and the three different diagnostic constraints are: tire damage cause, tire damage time and tire model, wherein the tire damage cause is composed of tire self-burst or tire external force burst; Extracting fault history data from the fault history data set in sequence, and performing the following operations on the extracted fault history data: Using the diagnostic constraint set, performing a marking operation on the extracted fault history data to obtain marked history data; If the identified historical data lacks at least one diagnostic constraint in the diagnostic constraint set, the identified historical data is discarded; Otherwise, identifying the historical data as first historical data, and determining whether the first historical data is preset second historical data; If the first historical data is not the second historical data, discarding the first historical data; If the first historical data is the second historical data, aggregating the second historical data to obtain a second historical data set; A target historical data set is acquired based on the second historical data set.

3. The vehicle safety protection intelligent prompt alarm method according to claim 2, characterized in that: The determining whether the first historical data is the preset second historical data includes: A first tire pressure time series set and a second tire pressure time series set for reference are obtained, wherein the first tire pressure time series set includes a plurality of first tire pressure time series, the second tire pressure time series set includes a plurality of second tire pressure time series, and the collection time intervals of the first tire pressure time series and the second tire pressure time series are preset, wherein the tire damage cause corresponding to the first tire pressure time series set is: tire self-burst, and the tire damage cause corresponding to the second tire pressure time series set is: tire external force burst, wherein the first tire pressure time series is as follows: T1={P1,P2,…,P i ,…,P n } Wherein, T1 represents the first tire pressure time sequence, P1, P2, ..., P i ,…,P n Both represent the first tire pressure, and P i represents the i-th first tire pressure in the first tire pressure time series, n represents the preset collected value, and P n =0; Based on the first tire pressure time series set, obtaining a first tire pressure mean and a first tire pressure variance, wherein the first tire pressure mean and the first tire pressure variance are respectively the mean and variance of a plurality of first tire pressure time series in the first tire pressure time series set; Based on the second tire pressure time series set and a preset reference mean relationship, a second tire pressure mean value set is obtained, wherein the second tire pressure mean value set includes a plurality of second tire pressure means, and based on the second tire pressure mean value set, a second tire pressure mean value and a second tire pressure variance are obtained, wherein the second tire pressure mean value and the second tire pressure variance are respectively the mean and variance of the plurality of second tire pressure means in the second tire pressure mean value set; An initial tire pressure time series is obtained using the first historical data, and whether the first historical data is the second historical data is determined based on the initial tire pressure time series, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean, and the second tire pressure variance.

4. The vehicle safety protection intelligent prompt alarm method according to claim 3, characterized in that: The step of obtaining a second tire pressure mean value set based on the second tire pressure time series set and a preset reference mean value relationship formula includes: The following operations are performed on the second tire pressure time series in the second tire pressure time series set: The second tire pressure is sequentially extracted from the second tire pressure time series, and the following operations are performed on the extracted second tire pressure: comparing the extracted second tire pressure with a preset second pressure threshold; When the extracted second tire pressure is less than or equal to the second pressure threshold, the extracted second tire pressure is used as a target to retrieve the tire pressure, and the target retrieved tire pressure is used to confirm a second reference tire pressure time sequence in the second tire pressure time sequence; Among them, the second reference tire pressure time series is as follows: T2=[q1,q2,…,q i ,…,q n } Wherein, T2 represents the second reference tire pressure time series, q1, q2, ..., q i ,…,q n Both represent the second target tire pressure, and q i represents the i-th second target tire pressure in the second reference tire pressure time series, and q n ≤Q A , where Q A is the second pressure threshold; The reference mean relationship is as follows: Wherein, J represents the mean value of the second tire pressure, Δt represents the sampling time interval, H represents the time corresponding to the first second target tire pressure in the second reference tire pressure time series, a and b are only used for counting, and q b represents the bth second target tire pressure in the second reference tire pressure time series; The second tire pressure means are summarized to obtain a second tire pressure mean set.

5. The vehicle safety protection intelligent prompt alarm method according to claim 4, characterized in that: The determining whether the first historical data is the second historical data based on the initial tire pressure time sequence, the first tire pressure mean, the first tire pressure variance, the second tire pressure mean, and the second tire pressure variance includes: The following operations are performed on the first tire pressure time series in the first tire pressure time series set: Retrieving a first target tire pressure in the first tire pressure time sequence, wherein the first target tire pressure is greater than the second pressure threshold, and a collection time interval between the first target tire pressure and the nth first tire pressure in the first tire pressure time sequence is the shortest; Obtaining a first reference absolute time based on the first target tire pressure and the nth first tire pressure, summarizing the first reference absolute times to obtain a first reference absolute time set, and obtaining a first absolute time mean using the first reference absolute time set, wherein the first absolute time mean is the mean of multiple first reference absolute times in the first reference absolute time set; The initial tire pressures are sequentially extracted from the initial tire pressure time series, and the following operations are performed on the extracted initial tire pressures: comparing the extracted initial tire pressure with the second pressure threshold; When the extracted initial tire pressure is less than the second pressure threshold, the target tire pressure is retrieved from the initial tire pressure time series using the extracted initial tire pressure, a target absolute time is obtained based on the target tire pressure and the extracted initial tire pressure, a target tire pressure time series is confirmed based on the extracted initial tire pressure, and a target tire mean and a target tire variance are obtained using the target tire pressure time series; Acquire a second absolute time mean value based on the second tire pressure time series set; Calculate the first similarity and the second similarity using a pre-constructed similarity relation, a first tire pressure mean, a first tire pressure variance, a second tire pressure mean, a second tire pressure variance, a target absolute time, a target tire mean, a target tire variance, a first absolute time mean, and a second absolute time mean; comparing the first similarity with the second similarity; If the second similarity is less than or equal to the first similarity, the first historical data is confirmed to be the second historical data.

6. The vehicle safety protection intelligent prompt alarm method according to claim 5, characterized in that: The similarity relationship is as follows: Among them, X1 and X2 represent the first similarity and the second similarity respectively, μ1 and μ2 represent the first tire pressure mean and the second tire pressure mean respectively, σ1 and σ2 represent the first tire pressure variance and the second tire pressure variance respectively, t1 and t2 represent the first absolute time mean and the second absolute time mean respectively, μ0 represents the target tire mean, σ0 represents the target tire variance, and t0 represents the target absolute time.

7. The vehicle safety protection intelligent prompt alarm method according to claim 6, characterized in that: The step of obtaining an initial fitting stage set by using the target historical data set includes: The following operations are performed on the target historical data in the target historical data set: The target tire pressure time series corresponding to the target historical data is used as the sampling period, and the target driving temperature time series, the target driving speed time series and the target driving acceleration time series are obtained respectively, and the target tire service life is obtained based on the target historical data; The target driving temperature time series and the target driving speed time series are used to respectively obtain the target driving temperature mean, the target driving temperature variance, the target driving speed mean and the target driving speed variance; Obtaining the target driving acceleration absolute mean and the target driving acceleration absolute variance based on the target driving acceleration time series; Associating the target driving temperature mean, the target driving temperature variance, the target driving speed mean, the target driving speed variance, the target driving acceleration absolute mean, and the target driving acceleration absolute variance with the target tire service life to obtain initial fitting data; The initial fitting data are aggregated to obtain an initial fitting stage set.

8. The vehicle safety protection intelligent prompt alarm method according to claim 7, characterized in that: The step of centrally identifying one or more first target fitting stages in the initial fitting stage according to the target analysis data includes: A screening relationship is constructed based on the tire service life corresponding to the target analysis data, wherein the screening relationship is as follows: S=(αT,βT) Wherein, S represents the screening relationship, α and β are both preset coefficients, and T represents the tire service life; The following operations are performed on the initial fitting data in the initial fitting stage: Determining whether the target tire service life corresponding to the initial fitting data satisfies the screening relationship; If the target tire service life corresponding to the initial fitting data satisfies the screening relationship, the initial fitting data is retained; Otherwise, the initial fitting data is discarded; The retained initial fitting data are aggregated to obtain one or more first target fitting stages.

9. The vehicle safety protection intelligent prompt alarm method according to claim 8, characterized in that: The obtaining of fitting analysis values ​​based on the one or more first target fitting stages includes: Using the pre-constructed hierarchical analysis method, the index weight set is obtained with the associated data corresponding to the first target fitting stage as the index, wherein the associated data is composed of 7 different associated values, and the 7 different associated values ​​are: target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life, wherein the index weight set includes multiple index weight values, and the index weight values ​​correspond to the associated values ​​one by one; Using the target analysis data, the target analysis temperature mean, the target analysis temperature variance, the target analysis speed mean, the target analysis speed variance, the target analysis acceleration absolute mean, and the target analysis acceleration absolute variance are obtained; A fitting analysis matrix is ​​constructed based on the indicator weight set, one or more first target fitting stages, and the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, and target analysis acceleration absolute variance, wherein the fitting analysis matrix is ​​as follows: Wherein, B represents the fitting analysis matrix, γ1, γ2, γ3, γ4, γ5, γ6, and γ7 represent the weight values ​​corresponding to the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life in the index weight set, respectively, W0, R0, E0, Y0, U0, I0, and O0 represent the target analysis temperature mean, target analysis temperature variance, target analysis speed mean, target analysis speed variance, target analysis acceleration absolute mean, target analysis acceleration absolute variance, and tire service life corresponding to the target analysis data, W1, R1, E1, Y1, U1, I1, and O1 represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance, and target tire service life corresponding to the first target fitting stage in one or more first target fitting stages, respectively, and k represents that there are k first target fitting stages in total; Calculating comprehensive evaluation values ​​based on the fitting analysis matrix and the pre-constructed comprehensive evaluation relationship, and summarizing the comprehensive evaluation values ​​to obtain a comprehensive evaluation value set; The comprehensive evaluation value set is screened to obtain a target comprehensive value set, and the fitting analysis value is obtained using the target comprehensive value set.

10. The vehicle safety protection intelligent prompt alarm method according to claim 9, characterized in that: The comprehensive evaluation relationship is as follows: Among them, Z g represents the comprehensive evaluation value corresponding to the g-th first target fitting stage in one or more first target fitting stages, W g , R g 、E g , Y g , U g ,I g , O g They respectively represent the target driving temperature mean, target driving temperature variance, target driving speed mean, target driving speed variance, target driving acceleration absolute mean, target driving acceleration absolute variance and target tire service life corresponding to the g-th first target fitting stage in one or more first target fitting stages.

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