CN117434872A - Method and system for realizing acquisition of electric vehicle controller data based on Internet of things terminal - Google Patents

Abstract

The invention relates to the technical field of electric vehicle controllers, and discloses a method and a system for realizing acquisition of electric vehicle controller data based on an internet of things terminal, wherein the method comprises the following steps: sequentially extracting control indexes from the control index association map, calculating the association degree of the associated control indexes by using an association degree formula, calibrating the association degree by using the association degree pair to obtain a target control index association map, judging whether an abnormal monitoring value exists according to the target control index association map, if so, calculating an abnormal fluctuation value, identifying the abnormal control index, calculating an abnormal fluctuation value set, calculating a target data acquisition frequency by using a data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set, and executing abnormal data acquisition according to the target data acquisition frequency. The invention mainly aims to solve the problems of low data acquisition efficiency and poor acquisition accuracy in the current acquisition of the data of the electric vehicle controller.

Description

Method and system for realizing acquisition of electric vehicle controller data based on Internet of things terminal

Technical Field

The invention relates to a method and a system for acquiring data of an electric vehicle controller based on an Internet of things terminal, and belongs to the technical field of electric vehicle controllers.

Background

With the increasing popularity of electric vehicles such as electric bicycles, the acquisition and analysis of electric vehicle controller data becomes more and more important. The controller is used as a core component of the electric vehicle and manages the functions of power output, braking, charging and the like of the electric vehicle. Conventional electric vehicle controller data collection usually adopts a wired communication mode, for example: CAN, RS-232, etc., and these modes have the advantages of low cost, good real-time performance, etc.

The current technology of Internet of things is used for realizing the collection and monitoring of the data of the electric vehicle controller, so that the trend is realized. The internet of things technology has the advantages of wireless communication, remote monitoring, data analysis and the like, can improve the collection efficiency of the electric vehicle controller data, and at present, two main methods for realizing the collection of the electric vehicle controller data based on the internet of things technology are provided: firstly, wireless transmission of controller data is realized by utilizing a special wireless communication module such as GSM, wi-Fi, bluetooth and the like; secondly, the intelligent mobile phone is used as an internet of things terminal, and collection of controller data is achieved through near field communication technologies such as Bluetooth and NFC, however, no method can achieve dynamic change of collection frequency according to abnormal change of the electric vehicle controller data, and therefore the problems of low data collection efficiency and poor collection accuracy exist in the current collection of the electric vehicle controller data.

Disclosure of Invention

The invention provides a method, a system and a computer-readable storage medium for realizing acquisition of electric vehicle controller data based on an Internet of things terminal, and mainly aims to solve the problems of low data acquisition efficiency and poor acquisition accuracy in the current acquisition of electric vehicle controller data.

In order to achieve the above purpose, the invention provides a method for realizing the collection of the data of the electric vehicle controller based on the terminal of the internet of things, which comprises the following steps:

acquiring control association relation of control indexes in a control index set, and constructing an initial control index association map by utilizing the control index set according to the control association relation of the control indexes, wherein the control index set refers to controllable operation parameter indexes in an electric vehicle controller;

sequentially extracting control indexes from the control index association map, identifying an association control index set of the control indexes, and calculating the association degree of each association control index in the control indexes and the association control index set by using a pre-constructed association degree formula, wherein the association degree formula is as follows:

wherein,representing the degree of association of the ith control index with the jth associated control index, +. >Representing the associated regulatory factor,/->The number of associated control indexes in the associated control index set representing the ith control index, +.>The number of control association relations representing the j-th associated control index;

performing association degree calibration on the control association relation in the initial control index association map by using the association degree to obtain a target control index association map;

acquiring current index monitoring data by utilizing a pre-constructed internet of things terminal;

judging whether the current index monitoring data has abnormal monitoring values according to the target control index association map;

if the current index monitoring data does not have the abnormal monitoring value, returning to the step of acquiring the current index monitoring data by utilizing the pre-constructed internet of things terminal;

if the current index monitoring data has an abnormal monitoring value, calculating an abnormal fluctuation value of the abnormal monitoring value;

identifying an abnormal control index corresponding to the abnormal monitoring value, extracting an abnormal associated index set of the abnormal control index, acquiring an abnormal associated data set of the abnormal associated index set, and calculating an abnormal fluctuation value set corresponding to the abnormal associated data set;

calculating the target data acquisition frequency of the abnormal control index by using a pre-constructed data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set, wherein the data acquisition frequency formula is as follows:

Wherein,initial data acquisition frequency indicating the ith abnormality control index, +.>Target data sampling frequency representing the ith abnormality control index, +.>Frequency adjustment factor indicating the ith abnormality control index, +.>Abnormal fluctuation value indicating the ith abnormal control index,/->Abnormal fluctuation value of the jth abnormality related index indicating the ith abnormality control index, ++>A frequency adjustment index indicating an ith abnormality control index, J indicating the number of abnormality related indexes;

and performing abnormal data acquisition on the abnormal control index according to the target data sampling frequency to obtain a refined abnormal monitoring value set.

Optionally, the acquiring the control association relationship of the control indexes in the control index set includes:

sequentially extracting control indexes from the control index set, and judging whether the control index set has associated control indexes of the control indexes;

if the control index set does not have the associated control index of the control index, the control index is removed from the control index set, and the step of sequentially extracting the control index in the control index set is returned;

if the control index set contains the associated control index of the control index, acquiring a control association relation between the associated control index and the control index;

Summarizing the control association relation between all the control indexes and the associated control indexes to obtain the control association relation between every two control indexes.

Optionally, the constructing an initial control index association map according to the control association relationship of the control indexes by two sets of control indexes includes:

sequentially extracting control indexes from the control index set, and taking the control indexes as control index entities;

extracting a control association relation set of the control index entity from the control association relation of the control indexes;

and identifying an associated control index entity set of the control index entity, and correspondingly connecting the control index entity with the associated control index entity set by utilizing the control associated relation set to obtain the initial control index associated map.

Optionally, the performing association degree calibration on the control association relationship in the initial control index association map by using the association degree to obtain a target control index association map includes:

sequentially extracting control association relations from the initial control index association map to obtain association degrees of the control association relations;

extracting a control relation line segment corresponding to the control association relation;

Regulating the thickness of the control relation line segment according to the association degree to obtain the target control index association map.

Optionally, the obtaining the current index monitoring data by using the pre-built terminal of the internet of things includes:

acquiring a current monitoring control index set and determining initial data acquisition frequency;

acquiring a current control index value corresponding to each current monitoring control index in the current monitoring control index set according to the initial data acquisition frequency by using a pre-constructed electric vehicle controller;

and uploading the current control index value to the terminal of the Internet of things to obtain the current index monitoring data.

Optionally, the determining whether the current index monitoring data has an abnormal monitoring value according to the target control index association map includes:

sequentially extracting current control index values from the current index monitoring data;

extracting a current association index value set of the current control index value according to the target control index association map;

sequentially extracting current associated index values from the current associated index value set;

extracting a current control association relation between the current association index value and the current control index value from the target control index association map;

Judging whether the current association index value and the current control index value accord with the current control association relation or not;

if the current association index value and the current control index value do not accord with the current control association relation, judging that abnormal monitoring values exist in the current index monitoring data;

if the current association index value and the current control index value accord with the current control association relation, judging whether the current association index value is extracted;

if the current associated index value is not extracted, returning to the step of sequentially extracting the current associated index value in the current associated index value set;

if the current associated index value is extracted, returning to the step of sequentially extracting the current control index value from the current index monitoring data until the current control index value is extracted;

judging whether the current index monitoring data has a current control index value which does not accord with the current control association relation or not;

if the current index monitoring data does not have the current control index value which does not accord with the current control association relation, the current index monitoring data does not have an abnormal monitoring value;

If the current index monitoring data has the current control index value which does not accord with the current control association relation, the current index monitoring data has an abnormal monitoring value.

Optionally, before the calculating the abnormal fluctuation value of the abnormal monitor value, the method further includes:

extracting an instruction input numerical value from the current index monitoring data according to a preset instruction input index;

sequentially extracting control index entities from the target control index association map;

calculating a standard control index numerical range of the control index entity according to the target control index association map by utilizing the instruction input numerical value;

summarizing all the standard control index numerical ranges to obtain a standard control index numerical range set;

sequentially extracting current control index values from the current index monitoring data;

extracting the corresponding index value range of the current control index value in the standard control index value range set;

judging whether the current control index value belongs to the corresponding index value range or not;

if the current control index value does not belong to the corresponding index value range, taking the current control index value as an abnormal monitoring value;

And if the current control index value belongs to the corresponding index value range, not taking the current control index value as an abnormal monitoring value.

Optionally, the calculating the abnormal fluctuation value of the abnormal monitoring value includes:

according to the standard control index numerical range and the abnormal monitoring numerical value, calculating the abnormal fluctuation value by using a pre-constructed abnormal fluctuation value formula, wherein the abnormal fluctuation value formula is as follows:

wherein,abnormal fluctuation value indicating the ith abnormal control index,/->Minimum value of standard control index value range representing the ith abnormal control index, +.>Maximum value of standard control index value range representing the ith abnormal control index, +.>An abnormality monitoring value indicating an ith abnormality control index.

Optionally, the extracting the abnormality association index set of the abnormality control index includes:

extracting a control association relation set of the abnormal control index from the target control index association map;

sequentially extracting control association relations from the control association relation set, and identifying undetermined control indexes corresponding to the control association relations;

identifying an index monitoring value of the undetermined control index, and judging whether the index monitoring value is an abnormal monitoring value or not;

If the index monitoring value is not the abnormality monitoring value, the undetermined control index is not an abnormality association index;

if the index monitoring value is the abnormality monitoring value, the undetermined control index is an abnormality association index;

and summarizing all the abnormal associated indexes to obtain the abnormal associated index set.

Optionally, the performing, according to the target data sampling frequency, abnormal data collection on the abnormal control index to obtain a refined abnormal monitoring value set includes:

sequentially extracting direct-connection abnormal control index pairs from the target control index association map;

simultaneously executing abnormal data acquisition on two abnormal control indexes in the direct-connection abnormal control index pair according to the target data sampling frequency to obtain a refined abnormal monitoring numerical value pair set;

summarizing all the obtained refined abnormal monitoring value pair sets, and de-duplicating the refined abnormal monitoring value pair sets to obtain the refined abnormal monitoring value sets.

In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to realize the method for realizing the acquisition of the electric vehicle controller data based on the terminal of the Internet of things.

In order to solve the above problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored in the computer readable storage medium, where the at least one instruction is executed by a processor in an electronic device to implement the above method for implementing the data acquisition method of the electric vehicle controller based on the internet of things terminal.

Compared with the problems in the prior art, the embodiment of the invention firstly acquires the control association relation of the control indexes in the control index set, because the control association relation exists between every two control indexes in the control index set, the control index set can be connected in a relation way according to the control association relation of every two control indexes, so as to obtain an initial control index association map, thereby realizing the analysis of the control indexes, and the number of the control association relation represents the association degree of the control indexes and other control indexes because the number of the control association relation is different, the association control index set of the control indexes can be identified, the association degree of each association control index in the association control index set is calculated by utilizing a pre-constructed association degree formula, the association degree is larger, the more closely the relation between the control index and the associated control index is represented, the relevance degree calibration can be performed on the control relevance relation in the initial control index relevance graph by using the relevance degree, the target control index relevance graph capable of intuitively showing the relevance degree of the control index and the associated control index is obtained by the relevance degree calibration, the current index monitoring data is required to be obtained by using the pre-built internet of things terminal, and the current index monitoring data can be judged by the target control index relevance graph because the control relevance relations of the control indexes are recorded in the target control index relevance graph, if the current index monitoring data has the abnormal monitoring value, the abnormal fluctuation value of the abnormal monitoring value is calculated, and the abnormal fluctuation value set corresponding to the abnormal relevance data set associated with the abnormal monitoring value is calculated, the invention provides a method, electronic equipment and a computer-readable storage medium for acquiring electric vehicle controller data based on an Internet of things terminal, which mainly aims to solve the problems of low data acquisition efficiency and poor acquisition accuracy in the current acquisition of electric vehicle controller data.

Drawings

Fig. 1 is a flow chart of a method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device for implementing the method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to an embodiment of the present invention.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The embodiment of the application provides a method for realizing acquisition of electric vehicle controller data based on an Internet of things terminal. The execution main body of the method for realizing the acquisition of the electric vehicle controller data based on the internet of things terminal comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the method for realizing the collection of the electric vehicle controller data based on the internet of things terminal can be executed by software or hardware installed in the terminal equipment or the server equipment. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Example 1:

referring to fig. 1, a flow chart of a method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to an embodiment of the present invention is shown. In this embodiment, the method for implementing data collection of an electric vehicle controller based on an internet of things terminal includes:

s1, acquiring control association relations of control indexes in a control index set, and constructing an initial control index association map by utilizing the control index set according to the control association relations of the control indexes, wherein the control index set refers to controllable operation parameter indexes in an electric vehicle controller.

The control index set may be a battery voltage, a battery current, a motor speed, a motor power, a motor torque, a vehicle speed, a motor temperature, an ambient temperature, a brake signal, an accelerator signal, and the like. The saidThe control association relationship refers to an operation association relationship between the control indexes, for example: when the control index is motor power and point motor torque, the control association relationship is as follows: motor torque=9550Motor power/motor speed, motor torque=f (vehicle speed), battery voltage=battery electromotive force-battery current +.>The control association relation among the internal resistance of the battery, the braking signal and the motor power is that the motor power is reduced, and the motor power is=f (accelerator opening) =f (k +) >Throttle signal strength) and the like, and in a word, the control association relationship describes the association relationship between two control indexes, when a fixed formula exists, the fixed formula is used as the control association relationship, and when the fixed formula does not exist, the control association relationship between the two control indexes is utilized for connection. The initial control index association graph refers to a knowledge graph constructed by a control index set and a control association relation.

In the embodiment of the present invention, the obtaining the control association relationship of the control indexes in the control index set includes:

sequentially extracting control indexes from the control index set, and judging whether the control index set has associated control indexes of the control indexes;

if the control index set does not have the associated control index of the control index, the control index is removed from the control index set, and the step of sequentially extracting the control index in the control index set is returned;

if the control index set contains the associated control index of the control index, acquiring a control association relation between the associated control index and the control index;

summarizing the control association relation between all the control indexes and the associated control indexes to obtain the control association relation between every two control indexes.

It is understood that the control index set may not have associated control indexes of the control index, for example: ambient temperature, so it can be rejected.

Further, there may be a plurality of associated control indicators of the control indicators in the control indicator set, for example: the battery voltage may be related to both the battery current and the motor power.

In the embodiment of the present invention, the constructing an initial control index association map by using the control index set according to the control association relationship of the control indexes, includes:

sequentially extracting control indexes from the control index set, and taking the control indexes as control index entities;

extracting a control association relation set of the control index entity from the control association relation of the control indexes;

and identifying an associated control index entity set of the control index entity, and correspondingly connecting the control index entity with the associated control index entity set by utilizing the control associated relation set to obtain the initial control index associated map.

Further, since the control association relationship may not be strictly satisfied between two control indexes having the control association relationship, for example, there may be a case of using abrasion or the like, and there may be an error, it is necessary to set a standard control index numerical range for the two control indexes having the control association relationship, and when the control association relationship is not satisfied, it means that there is no abnormality in the standard control index numerical range. For example: when the control association relationship is: motor torque=9550 When the motor power/motor rotating speed is, the control association relation under the actual condition meets the following conditions "The standard control index value range. The median value of the standard control index value range may be +.>。

S2, sequentially extracting control indexes from the control index association map, identifying an association control index set of the control indexes, and calculating the association degree of each association control index in the control index and the association control index set by using a pre-constructed association degree formula.

It is understood that the association degree refers to the degree of association between the control index and the associated control index.

In detail, the association formula is as follows:

wherein,representing the degree of association of the ith control index with the jth associated control index, +.>Representing the associated regulatory factor,/->The number of associated control indexes in the associated control index set representing the ith control index, +.>The number of control association relations indicating the j-th associated control index.

It should be understood that when the number of associated control indexes in the associated control index set of control indexes is smaller, the closer the relationship between the control index and the associated control index is indicated, and thus the degree of association is inversely proportional to the number of associated control indexes.

And S3, calibrating the association degree of the control association relation in the initial control index association map by using the association degree to obtain a target control index association map.

Further, the target control index association map refers to an initial control index association map for calibrating the control association relationship by using the association degree.

In the embodiment of the present invention, the performing association degree calibration on the control association relationship in the initial control index association map by using the association degree to obtain a target control index association map includes:

sequentially extracting control association relations from the initial control index association map to obtain association degrees of the control association relations;

extracting a control relation line segment corresponding to the control association relation;

regulating the thickness of the control relation line segment according to the association degree to obtain the target control index association map.

It can be understood that the control relation line segment refers to a connection line segment between the control index entity and the control index entity in the initial control index association map, and the connection line segment represents the control association relation.

Further, the degree of association can be indicated by regulating the thickness degree of the control relation line segment, and the larger the association degree is, the thicker the control relation line segment is. Thus, the association degree between two control index entities can be represented in a visual way.

S4, acquiring current index monitoring data by utilizing the pre-built terminal of the Internet of things.

It can be understood that the internet of things terminal can collect the current index monitoring data for the internet of things terminal such as a smart phone and a tablet through near field communication technology such as Bluetooth set NFC.

Further, the abnormality monitoring numerical value refers to a numerical value of a control index that does not conform to the control association relationship in the target control index association map.

In the embodiment of the present invention, the obtaining current index monitoring data by using the pre-constructed terminal of the internet of things includes:

acquiring a current monitoring control index set and determining initial data acquisition frequency;

acquiring a current control index value corresponding to each current monitoring control index in the current monitoring control index set according to the initial data acquisition frequency by using a pre-constructed electric vehicle controller;

and uploading the current control index value to the terminal of the Internet of things to obtain the current index monitoring data.

Further, the initial data acquisition frequency refers to the acquisition frequency of the current monitoring control index in a normal state, for example: the acquisition frequency of the battery voltage is 10Hz (10 times/s), and the acquisition frequency of the vehicle speed is 1 time/s.

And S5, judging whether the current index monitoring data has abnormal monitoring values according to the target control index association map.

In the embodiment of the present invention, the determining whether the current index monitoring data has an abnormal monitoring value according to the target control index association graph includes:

sequentially extracting current control index values from the current index monitoring data;

extracting a current association index value set of the current control index value according to the target control index association map;

sequentially extracting current associated index values from the current associated index value set;

extracting a current control association relation between the current association index value and the current control index value from the target control index association map;

judging whether the current association index value and the current control index value accord with the current control association relation or not;

if the current association index value and the current control index value do not accord with the current control association relation, judging that abnormal monitoring values exist in the current index monitoring data;

if the current association index value and the current control index value accord with the current control association relation, judging whether the current association index value is extracted;

If the current associated index value is not extracted, returning to the step of sequentially extracting the current associated index value in the current associated index value set;

if the current associated index value is extracted, returning to the step of sequentially extracting the current control index value from the current index monitoring data until the current control index value is extracted;

judging whether the current index monitoring data has a current control index value which does not accord with the current control association relation or not;

if the current index monitoring data does not have the current control index value which does not accord with the current control association relation, the current index monitoring data does not have an abnormal monitoring value;

if the current index monitoring data has the current control index value which does not accord with the current control association relation, the current index monitoring data has an abnormal monitoring value.

It can be understood that whether the abnormal monitoring value exists can be judged according to whether the current control index value accords with the corresponding current control association relation, and the abnormal monitoring value is indicated when the current control index value does not accord with the control association relation because the control association relation between the control indexes in the target control index association map is determined.

And if the current index monitoring data does not have the abnormal monitoring value, returning to the step of acquiring the current index monitoring data by utilizing the pre-built internet of things terminal.

And if the current index monitoring data has an abnormal monitoring value, executing S6, and calculating an abnormal fluctuation value of the abnormal monitoring value.

The abnormal fluctuation value refers to a difference value between the abnormal monitoring value and a preset normal value.

In an embodiment of the present invention, before the calculating the abnormal fluctuation value of the abnormal monitoring value, the method further includes:

extracting an instruction input numerical value from the current index monitoring data according to a preset instruction input index;

sequentially extracting control index entities from the target control index association map;

calculating a standard control index numerical range of the control index entity according to the target control index association map by utilizing the instruction input numerical value;

summarizing all the standard control index numerical ranges to obtain a standard control index numerical range set;

sequentially extracting current control index values from the current index monitoring data;

extracting the corresponding index value range of the current control index value in the standard control index value range set;

Judging whether the current control index value belongs to the corresponding index value range or not;

if the current control index value does not belong to the corresponding index value range, taking the current control index value as an abnormal monitoring value;

and if the current control index value belongs to the corresponding index value range, not taking the current control index value as an abnormal monitoring value.

It may be appreciated that the command input index refers to an input command index of the electric vehicle, for example: the command input index can be battery voltage, accelerator signal intensity, brake signal intensity and the like, and other control indexes are changed along with the command input index according to the control association relation because the command input index is the reason for the change of other control indexes. For example: when the throttle signal strength is determined, the motor speed, motor torque, etc. will be determined accordingly.

In an embodiment of the present invention, the calculating the abnormal fluctuation value of the abnormal monitoring value includes:

according to the standard control index numerical range and the abnormal monitoring numerical value, calculating the abnormal fluctuation value by using a pre-constructed abnormal fluctuation value formula, wherein the abnormal fluctuation value formula is as follows:

Wherein,abnormal fluctuation value indicating the ith abnormal control index,/->Minimum value of standard control index value range representing the ith abnormal control index, +.>Maximum value of standard control index value range representing the ith abnormal control index, +.>An abnormality monitoring value indicating an ith abnormality control index.

Further, since there are complicated factors, it is impossible to change each control index strictly in accordance with the control association relationship in the target control index association map, and thus a range section can be set.

S7, identifying an abnormal control index corresponding to the abnormal monitoring numerical value, extracting an abnormal associated index set of the abnormal control index, acquiring an abnormal associated data set of the abnormal associated index set, and calculating an abnormal fluctuation value set corresponding to the abnormal associated data set.

Further, the calculation mode of the abnormal fluctuation value set corresponding to the abnormal correlation data set is consistent with the calculation mode of the abnormal fluctuation value of the abnormal monitoring value, and will not be described herein. The abnormal associated index set refers to a control index which has a direct control associated relation with the abnormal associated index. For example: when the abnormality related index is an accelerator opening degree, since there is motor torque=9550 Motor power/motor rotation speed, motor torque=f (vehicle speed), motor power=f (accelerator opening) =f (k +.>Throttle signal intensity), so that the motor power is an abnormality related index of the throttle opening, and the motor torque is also an abnormality related index of the throttle opening, and the corresponding control related relationship is motor torque=9550[f（k/>Throttle signal intensity)]The motor speed, the motor temperature and the ambient temperature are not abnormal associated indexes of the accelerator opening degree, because the motor temperature, the ambient temperature and the accelerator opening degree have no obvious and definite control associated relation.

In the embodiment of the present invention, the extracting the abnormality association index set of the abnormality control index includes:

extracting a control association relation set of the abnormal control index from the target control index association map;

sequentially extracting control association relations from the control association relation set, and identifying undetermined control indexes corresponding to the control association relations;

identifying an index monitoring value of the undetermined control index, and judging whether the index monitoring value is an abnormal monitoring value or not;

if the index monitoring value is not the abnormality monitoring value, the undetermined control index is not an abnormality association index;

If the index monitoring value is the abnormality monitoring value, the undetermined control index is an abnormality association index;

and summarizing all the abnormal associated indexes to obtain the abnormal associated index set.

It can be explained that whether the index monitoring value is an abnormal monitoring value can be determined by the range of the index values corresponding to the instruction input index and the undetermined control index, when the index monitoring value does not accord with the instruction input index and the corresponding index valueIn the limiting of the range, the index monitoring value is an abnormality monitoring value, for example: when the instruction input index is motor power, the motor torque should satisfy 9550Motor power/motor speed", wherein->Representing a corresponding standard control index value range.

Further, if there is an abnormal control index that does not have an abnormal associated index, it indicates that the control index in the target control index associated map may not be completely constructed, and further analysis and addition of the associated control index are required.

S8, calculating the target data acquisition frequency of the abnormal control index by utilizing a pre-constructed data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set.

In detail, the data acquisition frequency formula is as follows:

wherein,initial data acquisition frequency indicating the ith abnormality control index, +.>Target data sampling frequency representing the ith abnormality control index, +.>Frequency adjustment factor indicating the ith abnormality control index, +.>Indicating the ith exception controlAbnormal fluctuation value of index->Abnormal fluctuation value of the jth abnormality related index indicating the ith abnormality control index, ++>The frequency adjustment index indicating the i-th abnormality control index, and J indicating the number of abnormality related indexes.

It can be understood that, when the abnormal fluctuation value is larger, the incontrollability of the abnormal control index is indicated to be stronger, and the abnormal control index having a control association relationship with the abnormal control index is indicated to be stronger, so that the incontrollability is in direct proportion to the magnitude of the abnormal fluctuation value and the number of the associated abnormal fluctuation values, the data sampling frequency should be increased, and when the association degree between the abnormal association indexes is larger, the relationship between the two abnormal association indexes is indicated to be tighter, so that the association degree is larger, and the data sampling frequency is also increased.

S9, performing abnormal data acquisition on the abnormal control indexes according to the target data sampling frequency to obtain a refined abnormal monitoring numerical value set.

It can be understood that the refined abnormal monitoring value set refers to an abnormal monitoring value set corresponding to the abnormal control index acquired according to the target data sampling frequency. The greater the sampling frequency of the target data is, the more the abnormal monitoring values are acquired in unit time, and the more the operation detail data of the abnormal control index can be acquired. Therefore, the magnitude of the data sampling frequency should be regulated according to the abnormal fluctuation value and the abnormal fluctuation value set of the abnormal control index so as to provide more accurate analysis.

In the embodiment of the present invention, the performing, according to the target data sampling frequency, abnormal data acquisition on the abnormal control index to obtain a refined abnormal monitoring value set includes:

sequentially extracting direct-connection abnormal control index pairs from the target control index association map;

simultaneously executing abnormal data acquisition on two abnormal control indexes in the direct-connection abnormal control index pair according to the target data sampling frequency to obtain a refined abnormal monitoring numerical value pair set;

summarizing all the obtained refined abnormal monitoring value pair sets, and de-duplicating the refined abnormal monitoring value pair sets to obtain the refined abnormal monitoring value sets.

It should be understood that the direct-connected abnormal control index pair refers to two abnormal control index pairs directly connected by using a control association relationship, for example: the motor torque and the vehicle speed are directly connected by utilizing a control association relation, and because the parameters in the data acquisition frequency formula comprise the abnormal fluctuation value of the abnormal control index and the abnormal fluctuation value of the abnormal association index, the data acquisition frequencies of the two abnormal control indexes in the refined abnormal monitoring numerical value pair are the same, namely the target data sampling frequency is common to the two abnormal control indexes in the refined abnormal monitoring numerical value pair.

Further, since there may be a case where the same abnormality control index is calculated twice, for example: when the abnormal control index is motor power and the abnormal related index of the motor power is accelerator opening, motor torque and the like, a first target data sampling frequency is calculated according to the abnormal fluctuation value of the motor power, the abnormal fluctuation value of the accelerator opening, and the degree of association of the motor power and the accelerator opening, and a second target data sampling frequency is calculated according to the abnormal fluctuation value of the motor power, the abnormal fluctuation value of the motor torque, and the degree of association of the motor power and the motor torque, at the moment, the motor power has two different target data sampling frequencies, so that the weight needs to be removed, and a larger target data sampling frequency can be adopted to collect abnormal monitoring values of the motor power, for example: at this time, the first target data sampling frequency is larger, the abnormal monitoring value of the motor power is collected according to the first target data sampling frequency, and the target data sampling frequency of the motor torque is kept unchanged (if the second target data sampling frequency is also the maximum target data sampling frequency of the motor torque, otherwise, the maximum target data sampling frequency is needed to be replaced).

Compared with the problems in the prior art, the embodiment of the invention firstly acquires the control association relation of the control indexes in the control index set, because the control association relation exists between every two control indexes in the control index set, the control index set can be connected in a relation way according to the control association relation of every two control indexes, so as to obtain an initial control index association map, thereby realizing the analysis of the control indexes, and the number of the control association relation represents the association degree of the control indexes and other control indexes because the number of the control association relation is different, the association control index set of the control indexes can be identified, the association degree of each association control index in the association control index set is calculated by utilizing a pre-constructed association degree formula, the association degree is larger, the more closely the relation between the control index and the associated control index is represented, the relevance degree calibration can be performed on the control relevance relation in the initial control index relevance graph by using the relevance degree, the target control index relevance graph capable of intuitively showing the relevance degree of the control index and the associated control index is obtained by the relevance degree calibration, the current index monitoring data is required to be obtained by using the pre-built internet of things terminal, and the current index monitoring data can be judged by the target control index relevance graph because the control relevance relations of the control indexes are recorded in the target control index relevance graph, if the current index monitoring data has the abnormal monitoring value, the abnormal fluctuation value of the abnormal monitoring value is calculated, and the abnormal fluctuation value set corresponding to the abnormal relevance data set associated with the abnormal monitoring value is calculated, the invention provides a method, electronic equipment and a computer-readable storage medium for acquiring electric vehicle controller data based on an Internet of things terminal, which mainly aims to solve the problems of low data acquisition efficiency and poor acquisition accuracy in the current acquisition of electric vehicle controller data.

Example 2:

fig. 2 is a schematic structural diagram of an electronic device for implementing a method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a bus 12, and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a collection program for implementing electric vehicle controller data based on an internet of things terminal.

The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard) or the like, provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used to store not only application software installed in the electronic device 1 and various data, for example, codes for implementing an acquisition program of electric vehicle controller data based on an internet of things terminal, but also temporarily store data that has been output or is to be output.

The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CentralProcessingunit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 10 is a control unit (control unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, an acquisition program for implementing electric vehicle controller data based on an internet of things terminal, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

The bus may be an Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.

Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.

For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.

Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.

The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light-emitting diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The collection program stored in the memory 11 of the electronic device 1 and used for realizing the data of the electric vehicle controller based on the terminal of the internet of things is a combination of a plurality of instructions, and when running in the processor 10, the method can be realized:

acquiring control association relation of control indexes in a control index set, and constructing an initial control index association map by utilizing the control index set according to the control association relation of the control indexes, wherein the control index set refers to controllable operation parameter indexes in an electric vehicle controller;

Sequentially extracting control indexes from the control index association map, identifying an association control index set of the control indexes, and calculating the association degree of each association control index in the control indexes and the association control index set by using a pre-constructed association degree formula, wherein the association degree formula is as follows:

wherein the method comprises the steps of，Representing the degree of association of the ith control index with the jth associated control index, +.>Representing the associated regulatory factor,/->The number of associated control indexes in the associated control index set representing the ith control index, +.>The number of control association relations representing the j-th associated control index;

performing association degree calibration on the control association relation in the initial control index association map by using the association degree to obtain a target control index association map;

acquiring current index monitoring data by utilizing a pre-constructed internet of things terminal;

judging whether the current index monitoring data has abnormal monitoring values according to the target control index association map;

if the current index monitoring data does not have the abnormal monitoring value, returning to the step of acquiring the current index monitoring data by utilizing the pre-constructed internet of things terminal;

If the current index monitoring data has an abnormal monitoring value, calculating an abnormal fluctuation value of the abnormal monitoring value;

identifying an abnormal control index corresponding to the abnormal monitoring value, extracting an abnormal associated index set of the abnormal control index, acquiring an abnormal associated data set of the abnormal associated index set, and calculating an abnormal fluctuation value set corresponding to the abnormal associated data set;

calculating the target data acquisition frequency of the abnormal control index by using a pre-constructed data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set, wherein the data acquisition frequency formula is as follows:

wherein,initial data acquisition frequency indicating the ith abnormality control index, +.>Target data sampling frequency representing the ith abnormality control index, +.>Frequency adjustment factor indicating the ith abnormality control index, +.>Abnormal fluctuation value indicating the ith abnormal control index,/->Abnormal fluctuation value of the jth abnormality related index indicating the ith abnormality control index, ++>A frequency adjustment index indicating an ith abnormality control index, J indicating the number of abnormality related indexes;

and performing abnormal data acquisition on the abnormal control index according to the target data sampling frequency to obtain a refined abnormal monitoring value set.

Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.

Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).

The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:

acquiring control association relation of control indexes in a control index set, and constructing an initial control index association map by utilizing the control index set according to the control association relation of the control indexes, wherein the control index set refers to controllable operation parameter indexes in an electric vehicle controller;

Sequentially extracting control indexes from the control index association map, identifying an association control index set of the control indexes, and calculating the association degree of each association control index in the control indexes and the association control index set by using a pre-constructed association degree formula, wherein the association degree formula is as follows:

/>

wherein,representing the degree of association of the ith control index with the jth associated control index, +.>Representing the associated regulatory factor,/->The number of associated control indexes in the associated control index set representing the ith control index, +.>The number of control association relations representing the j-th associated control index;

performing association degree calibration on the control association relation in the initial control index association map by using the association degree to obtain a target control index association map;

acquiring current index monitoring data by utilizing a pre-constructed internet of things terminal;

judging whether the current index monitoring data has abnormal monitoring values according to the target control index association map;

if the current index monitoring data does not have the abnormal monitoring value, returning to the step of acquiring the current index monitoring data by utilizing the pre-constructed internet of things terminal;

If the current index monitoring data has an abnormal monitoring value, calculating an abnormal fluctuation value of the abnormal monitoring value;

identifying an abnormal control index corresponding to the abnormal monitoring value, extracting an abnormal associated index set of the abnormal control index, acquiring an abnormal associated data set of the abnormal associated index set, and calculating an abnormal fluctuation value set corresponding to the abnormal associated data set;

calculating the target data acquisition frequency of the abnormal control index by using a pre-constructed data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set, wherein the data acquisition frequency formula is as follows:

wherein,initial data acquisition frequency indicating the ith abnormality control index, +.>Target data sampling frequency representing the ith abnormality control index, +.>Frequency adjustment factor indicating the ith abnormality control index, +.>Abnormal fluctuation value indicating the ith abnormal control index,/->Abnormal fluctuation value of the jth abnormality related index indicating the ith abnormality control index, ++>A frequency adjustment index indicating an ith abnormality control index, J indicating the number of abnormality related indexes;

and performing abnormal data acquisition on the abnormal control index according to the target data sampling frequency to obtain a refined abnormal monitoring value set.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over 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.

In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (10)

1. The method for realizing the acquisition of the data of the electric vehicle controller based on the terminal of the Internet of things is characterized by comprising the following steps:

acquiring control association relation of control indexes in a control index set, and constructing an initial control index association map by utilizing the control index set according to the control association relation of the control indexes, wherein the control index set refers to controllable operation parameter indexes in an electric vehicle controller;

sequentially extracting control indexes from the control index association map, identifying an association control index set of the control indexes, and calculating the association degree of each association control index in the control indexes and the association control index set by using a pre-constructed association degree formula, wherein the association degree formula is as follows:

wherein,representing the degree of association of the ith control index with the jth associated control index, +.>Representing the associated regulatory factor,/->The number of associated control indexes in the associated control index set representing the ith control index, +.>The number of control association relations representing the j-th associated control index;

performing association degree calibration on the control association relation in the initial control index association map by using the association degree to obtain a target control index association map;

Acquiring current index monitoring data by utilizing a pre-constructed internet of things terminal;

judging whether the current index monitoring data has abnormal monitoring values according to the target control index association map;

if the current index monitoring data does not have the abnormal monitoring value, returning to the step of acquiring the current index monitoring data by utilizing the pre-constructed internet of things terminal;

if the current index monitoring data has an abnormal monitoring value, calculating an abnormal fluctuation value of the abnormal monitoring value;

identifying an abnormal control index corresponding to the abnormal monitoring value, extracting an abnormal associated index set of the abnormal control index, acquiring an abnormal associated data set of the abnormal associated index set, and calculating an abnormal fluctuation value set corresponding to the abnormal associated data set;

calculating the target data acquisition frequency of the abnormal control index by using a pre-constructed data acquisition frequency formula according to the abnormal fluctuation value and the abnormal fluctuation value set, wherein the data acquisition frequency formula is as follows:

wherein,initial data acquisition frequency indicating the ith abnormality control index, +.>Target data sampling frequency representing the ith abnormality control index, +. >Frequency adjustment factor indicating the ith abnormality control index, +.>Abnormal fluctuation value indicating the ith abnormal control index,/->Abnormal fluctuation value of the jth abnormality related index indicating the ith abnormality control index, ++>A frequency adjustment index indicating an ith abnormality control index, J indicating the number of abnormality related indexes;

and performing abnormal data acquisition on the abnormal control index according to the target data sampling frequency to obtain a refined abnormal monitoring value set.

2. The method for implementing data collection of the electric vehicle controller based on the internet of things terminal according to claim 1, wherein the obtaining the control association relation of the control indexes in the control index set comprises:

sequentially extracting control indexes from the control index set, and judging whether the control index set has associated control indexes of the control indexes;

if the control index set does not have the associated control index of the control index, the control index is removed from the control index set, and the step of sequentially extracting the control index in the control index set is returned;

if the control index set contains the associated control index of the control index, acquiring a control association relation between the associated control index and the control index;

Summarizing the control association relation between all the control indexes and the associated control indexes to obtain the control association relation between every two control indexes.

3. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 2, wherein the constructing an initial control index association map by using the control index set according to the control association relation of the control indexes comprises:

sequentially extracting control indexes from the control index set, and taking the control indexes as control index entities;

extracting a control association relation set of the control index entity from the control association relation of the control indexes;

and identifying an associated control index entity set of the control index entity, and correspondingly connecting the control index entity with the associated control index entity set by utilizing the control associated relation set to obtain the initial control index associated map.

4. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 1, wherein the performing association degree calibration on the control association relationship in the initial control index association map by using the association degree to obtain a target control index association map comprises:

Sequentially extracting control association relations from the initial control index association map to obtain association degrees of the control association relations;

extracting a control relation line segment corresponding to the control association relation;

regulating the thickness of the control relation line segment according to the association degree to obtain the target control index association map.

5. The method for implementing data collection of the electric vehicle controller based on the internet of things terminal according to claim 1, wherein the step of obtaining current index monitoring data by using the pre-constructed internet of things terminal comprises the following steps:

acquiring a current monitoring control index set and determining initial data acquisition frequency;

acquiring a current control index value corresponding to each current monitoring control index in the current monitoring control index set according to the initial data acquisition frequency by using a pre-constructed electric vehicle controller;

and uploading the current control index value to the terminal of the Internet of things to obtain the current index monitoring data.

6. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 1, wherein the determining whether the current index monitoring data has an abnormal monitoring value according to the target control index association map includes:

Sequentially extracting current control index values from the current index monitoring data;

extracting a current association index value set of the current control index value according to the target control index association map;

sequentially extracting current associated index values from the current associated index value set;

extracting a current control association relation between the current association index value and the current control index value from the target control index association map;

judging whether the current association index value and the current control index value accord with the current control association relation or not;

if the current association index value and the current control index value do not accord with the current control association relation, judging that abnormal monitoring values exist in the current index monitoring data;

if the current association index value and the current control index value accord with the current control association relation, judging whether the current association index value is extracted;

if the current associated index value is not extracted, returning to the step of sequentially extracting the current associated index value in the current associated index value set;

if the current associated index value is extracted, returning to the step of sequentially extracting the current control index value from the current index monitoring data until the current control index value is extracted;

Judging whether the current index monitoring data has a current control index value which does not accord with the current control association relation or not;

if the current index monitoring data does not have the current control index value which does not accord with the current control association relation, the current index monitoring data does not have an abnormal monitoring value;

if the current index monitoring data has the current control index value which does not accord with the current control association relation, the current index monitoring data has an abnormal monitoring value.

7. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 1, wherein before calculating the abnormal fluctuation value of the abnormal monitoring value, the method further comprises:

extracting an instruction input numerical value from the current index monitoring data according to a preset instruction input index;

sequentially extracting control index entities from the target control index association map;

calculating a standard control index numerical range of the control index entity according to the target control index association map by utilizing the instruction input numerical value;

summarizing all the standard control index numerical ranges to obtain a standard control index numerical range set;

Sequentially extracting current control index values from the current index monitoring data;

extracting the corresponding index value range of the current control index value in the standard control index value range set;

judging whether the current control index value belongs to the corresponding index value range or not;

if the current control index value does not belong to the corresponding index value range, taking the current control index value as an abnormal monitoring value;

and if the current control index value belongs to the corresponding index value range, not taking the current control index value as an abnormal monitoring value.

8. The method for implementing data collection of the electric vehicle controller based on the internet of things terminal according to claim 7, wherein the calculating the abnormal fluctuation value of the abnormal monitoring value comprises:

according to the standard control index numerical range and the abnormal monitoring numerical value, calculating the abnormal fluctuation value by using a pre-constructed abnormal fluctuation value formula, wherein the abnormal fluctuation value formula is as follows:

wherein,abnormal fluctuation value indicating the ith abnormal control index,/->Minimum value of standard control index value range representing the ith abnormal control index, +. >Maximum value of standard control index value range representing the ith abnormal control index, +.>An abnormality monitoring value indicating an ith abnormality control index.

9. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 1, wherein the extracting the abnormality association index set of the abnormality control index comprises:

extracting a control association relation set of the abnormal control index from the target control index association map;

sequentially extracting control association relations from the control association relation set, and identifying undetermined control indexes corresponding to the control association relations;

identifying an index monitoring value of the undetermined control index, and judging whether the index monitoring value is an abnormal monitoring value or not;

if the index monitoring value is not the abnormality monitoring value, the undetermined control index is not an abnormality association index;

if the index monitoring value is the abnormality monitoring value, the undetermined control index is an abnormality association index;

and summarizing all the abnormal associated indexes to obtain the abnormal associated index set.

10. The method for implementing data collection of an electric vehicle controller based on an internet of things terminal according to claim 1, wherein the performing abnormal data collection on the abnormal control index according to the target data sampling frequency to obtain a refined abnormal monitoring value set includes:

Sequentially extracting direct-connection abnormal control index pairs from the target control index association map;

simultaneously executing abnormal data acquisition on two abnormal control indexes in the direct-connection abnormal control index pair according to the target data sampling frequency to obtain a refined abnormal monitoring numerical value pair set;

summarizing all the obtained refined abnormal monitoring value pair sets, and de-duplicating the refined abnormal monitoring value pair sets to obtain the refined abnormal monitoring value sets.