CN115309739B - Vehicle-mounted data retrieval method and device, electronic equipment, medium and product - Google Patents
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Abstract
The embodiment of the disclosure discloses a vehicle-mounted data retrieval method, a vehicle-mounted data retrieval device, electronic equipment, media and products. One embodiment of the method comprises the following steps: sequentially retrieving firmware data in the firmware data sequence; for each target index information in the sequence of target index information, the following processing steps are performed: determining an index position included in the target index information as a traversal start position; traversing the firmware data in the firmware data sequence from the initial position; determining a first target interval to obtain a first target interval sequence; generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence; and determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval. According to the implementation mode, the retrieval efficiency and the retrieval accuracy of the vehicle-mounted data are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a vehicle-mounted data retrieval method, a vehicle-mounted data retrieval device, electronic equipment, media and products.
Background
The vehicle-mounted data retrieval is a technology for retrieving data in the vehicle-mounted firmware to obtain vehicle diagnosis data. Currently, in the case of retrieving vehicle-mounted data, the following methods are generally adopted: first, a storage area in which diagnostic data is located is determined. Then, the vehicle diagnostic data in the storage area is determined manually.
However, the inventors found that when the above-described manner is adopted to retrieve the vehicle-mounted data, there are often the following technical problems:
firstly, determining vehicle diagnosis data in a storage area in a manual mode, wherein the situation of manual judgment errors possibly exists, so that the retrieval accuracy of vehicle data is low;
secondly, when the storage area where the diagnostic data is located is determined, a plurality of candidate storage areas may be obtained, and the actual storage area where the diagnostic data of the vehicle is located is determined from the plurality of candidate storage areas manually, so that the retrieval efficiency is low.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose vehicle-mounted data retrieval methods, apparatus, electronic devices, media, and products to address one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a vehicle-mounted data retrieval method, the method including: sequentially retrieving firmware data in a firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data is data of vehicle-mounted firmware corresponding to a target vehicle to be detected, and the target index information comprises index positions which represent positions of the firmware data corresponding to the target index information in the firmware data sequence; for each target index information in the target index information sequence, the following processing steps are performed: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the initial position to determine a target score value and a characteristic position corresponding to the target index information; determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence; generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence; and determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval.
Optionally, determining the first target interval according to the obtained target score value sequence and the feature position sequence to obtain a first target interval sequence includes: based on the target score value sequence and the characteristic position sequence, the following screening steps are performed: generating an updated candidate score value sequence and an updated candidate feature position sequence according to the target score value sequence and the feature position sequence; updating the target score value meeting the target condition in the target score value sequence to the first target value to obtain an updated target score value sequence, wherein the target condition is that the target score value is the same as any updated candidate score value in the updated candidate score value sequence; adding 1 to the cycle count, wherein the initial value of the cycle count is 0; determining the updated candidate feature position sequence as a target feature position sequence in response to determining that the cycle count value is greater than the preset number of times; in response to determining that the cycle count value is less than or equal to the preset number of times, taking the updated candidate score value sequence as a candidate score value sequence, taking the updated candidate feature position sequence as a candidate feature position sequence, and taking the updated target score value sequence as a target score value sequence, again executing the screening step; and determining a first target interval corresponding to each target characteristic position in the target characteristic position sequence to obtain the first target interval sequence, wherein the first target interval is an interval between the target characteristic position and a corresponding traversal starting position.
Optionally, the generating the updated candidate score value sequence and the updated candidate feature position sequence according to the target score value sequence and the feature position sequence includes: according to the target score value sequence and the characteristic position sequence, the following substeps are executed: determining a target score value of a target position in the target score value sequence as a candidate score value; in response to determining that the candidate score value is greater than the second preset score value, determining the candidate score value as an updated second preset score value, wherein an initial value of the second preset score value is 0; removing the target score value at the preset position in the target score value sequence to obtain an updated target score value sequence; in response to determining that the updated target score value sequence is empty, determining an updated second preset score value as an updated candidate score value in the updated candidate score value sequence, and determining a feature position corresponding to the candidate score value as an updated candidate feature position in the updated candidate feature position sequence; and in response to determining that the updated target score value sequence is not null, taking the updated target score value sequence as the target score value sequence and taking the updated second preset score value as the second preset score value, and executing the substep again.
In a second aspect, some embodiments of the present disclosure provide an in-vehicle data retrieval apparatus, the apparatus comprising: the information retrieval unit is configured to sequentially retrieve firmware data in the firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data are data of vehicle-mounted firmware corresponding to a target vehicle to be detected, the target index information comprises index positions, and the index positions represent positions of the firmware data corresponding to the target index information in the firmware data sequence; an information processing unit configured to perform the following processing steps for each target index information in the target index information sequence described above: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the initial position to determine a target score value and a characteristic position corresponding to the target index information; the first interval determining unit is configured to determine a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence; an information generating unit configured to generate feature matching information based on the first target interval sequence, the firmware data sequence, and a pre-trained firmware data matching model, to obtain a feature matching information sequence; and a second section determining unit configured to determine a first target section corresponding to the feature matching information satisfying the feature screening condition in the feature matching information sequence as a second target section.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
In a fifth aspect, some embodiments of the present disclosure provide a computer program product comprising a computer program which, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: according to the vehicle-mounted data retrieval method, the retrieval efficiency and the retrieval accuracy of the vehicle-mounted data can be improved. Specifically, the reason why the retrieval efficiency and the retrieval accuracy of the in-vehicle data are low is that: first, by determining the vehicle diagnosis data in the storage area manually, there may be a case of a manual judgment error, so that the retrieval accuracy of the vehicle data is low. Secondly, when the storage area where the diagnostic data is located is determined, a plurality of candidate storage areas may be obtained, and the actual storage area where the diagnostic data of the vehicle is located is determined from the plurality of candidate storage areas manually, so that the retrieval efficiency is low. Based on this, in the vehicle-mounted data retrieval method of some embodiments of the present disclosure, first, the firmware data in the firmware data sequence is sequentially retrieved to generate the target index information, so as to obtain the target index information sequence. The firmware data are data of vehicle-mounted firmware corresponding to a target vehicle to be detected, the target index information comprises an index position, and the index position represents the position of the firmware data corresponding to the target index information in the firmware data sequence. Since there may be a plurality of candidate storage areas in the firmware data sequence, it is necessary to sequentially search the firmware data in the firmware data sequence, thereby determining the index position of each of the plurality of candidate storage areas. Then, for each target index information in the target index information sequence described above, the following processing steps are performed: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the initial position to determine a target score value and a characteristic position corresponding to the target index information. Thus, the target score value and the characteristic position corresponding to the index position of each candidate storage area are obtained so as to screen the plurality of storage areas. And then, determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence. Therefore, screening of candidate storage areas and feature positions is completed according to the target score value sequence, and the problem of low retrieval efficiency of vehicle-mounted data caused by the fact that a plurality of candidate storage areas exist is avoided. And generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence. Therefore, according to the pre-trained firmware data matching model, further identification of the firmware data in the first target interval is completed, and the retrieval accuracy of the vehicle-mounted data is improved. And finally, determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval. Therefore, the actual storage area where the vehicle diagnosis data are is located is obtained by searching and screening the data of the vehicle-mounted firmware, the problems of low efficiency and low accuracy caused by adopting a manual mode are avoided, and the searching efficiency and the searching accuracy of the vehicle-mounted data are improved.
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The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of an in-vehicle data retrieval method according to the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of an in-vehicle data retrieval device according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flow 100 of some embodiments of an in-vehicle data retrieval method according to the present disclosure. The vehicle-mounted data retrieval method comprises the following steps:
In some embodiments, an execution body (e.g., a computing device) of the vehicle-mounted data retrieval method may sequentially retrieve the firmware data in the firmware data sequence to generate the target index information, so as to obtain the target index information sequence. The firmware data may be data of vehicle-mounted firmware corresponding to a target vehicle to be subjected to vehicle detection. The on-vehicle firmware may be control firmware corresponding to a function included in the target vehicle. The target index information may include an index position. The index position represents the position of the firmware data corresponding to the target index information in the firmware data sequence. For example, the above-described in-vehicle firmware may be firmware including a TCU (Transmission Control Unit, automatic transmission control unit).
As an example, the execution body may determine, as the index position, a position of the firmware data identical to the traversal start identifier in the firmware data sequence, thereby determining the target index information, and obtain the target index information sequence. Wherein, the traversal start identifier may be 10.
The computing device may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
It should be understood that the number of computing devices described above is merely illustrative. There may be any number of computing devices, as desired for an implementation.
in step 1021, the index position included in the target index information is determined as the traversal start position.
In some embodiments, the execution body may determine an index position included in the target index information as a traversal start position.
In some embodiments, the execution body may traverse the firmware data in the firmware data sequence from the traversal start position to determine the target score value and the feature position corresponding to the target index information. The target score value may represent a probability that a section between the corresponding feature position and the traversal start position is a section where the target database is located. The feature location may be the location in the target database where the tail data is located. For example, the target database may be a UDS (Unified Diagnostic Services, unified diagnostic service) database.
In some optional implementations of some embodiments, the executing body may traverse the firmware data in the firmware data sequence from the traversal start position to determine a target score value and a feature position corresponding to the target index information, and may include the following steps:
first, for the above traversal start position, the following processing steps are performed:
and a first processing step of determining the firmware data at the target position in the firmware data sequence as first candidate data.
The target location may characterize a location in the firmware data sequence where the currently traversed firmware data is located. The initial value corresponding to the target position may be 1.
And a second processing step of determining the sum of the initial score value and the first preset score value as a candidate target score value in response to determining that the target sample identifier exists in the initial sample identifier set, and updating the initial characteristic position by utilizing the target position to obtain the updated initial characteristic position.
The initial score value may be an initial value of a target score set in advance. The initial feature position may be an initial position of firmware data set in advance. The first preset score value may be a value preset for calculating the initial score value. The initial sample identification may be an identification for screening firmware data. The target sample identity is the same as the first candidate data and is not equal to the first target value. For example, the initial value of the initial score value may be 0. The initial value corresponding to the initial feature position may be 1. The first target value may be 0. The first preset fraction value may be 1. The initial sample identity may be a service identity of the UDS database.
And a third processing step of updating the numerical value of the target sample identifier in the initial sample identifier set to the first target value to obtain an updated sample identifier set.
As an example, the execution body may determine the value of the target sample identifier in the initial sample identifier set to be 0, to obtain an updated sample identifier set.
And a fourth processing step, adding the value corresponding to the target position with the second target value to obtain the updated target position.
The second target value may be a preset value for iterating the target position during each cycle. For example, the second target value may be 2.
And a fifth processing step of determining, in response to determining that the value corresponding to the updated target position is greater than the third target value, the candidate target score value as the target score value corresponding to the target index information, and the updated initial feature position as the feature position corresponding to the target index information.
The third target value may be a value set in advance for defining the size of the traversed area. For example, the third target value may be 512.
And a second step of executing the processing step again with the updated target position as the target position, the updated sample identification set as the initial sample identification set, the candidate target score value as the initial score value, and the updated initial feature position as the initial feature position in response to determining that the value corresponding to the updated target position is equal to or less than the third target value.
Optionally, after the first processing step, the executing body may further execute the following steps:
and a first step of adding a value corresponding to the target position to the second target value to obtain an updated target position in response to determining that the target sample identifier does not exist in the initial sample identifier set.
The second target value may be a preset value for iterating the target position during each cycle. For example, the second target value may be 2.
And a second step of determining, in response to determining that the updated value corresponding to the target position is greater than the third target value, an initial score value as a target score value corresponding to the target index information, and an initial feature position as a feature position corresponding to the target index information.
The third target value may be a value set in advance for defining the size of the traversed area. For example, the third target value may be 512.
And step 103, determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence.
In some embodiments, the executing body may determine the first target interval according to the obtained target score value sequence and the feature position sequence through various manners, so as to obtain the first target interval sequence.
In some optional implementations of some embodiments, the executing body determines a first target interval according to the obtained target score value sequence and the feature position sequence, to obtain the first target interval sequence, and may include the following steps:
first, based on the target score value sequence and the characteristic position sequence, the following screening steps are executed:
and a first screening step, namely generating an updated candidate score value sequence and an updated candidate feature position sequence according to the target score value sequence and the feature position sequence.
The updated candidate score value may be the highest-value target score value selected from the target score value sequence. The updated candidate feature locations may be feature locations that correspond to updated candidate score values that are screened from the sequence of feature locations.
And a second screening step, namely updating the target score value meeting the target condition in the target score value sequence into the first target value to obtain an updated target score value sequence.
The target condition is that the target score value is the same as any updated candidate score value in the updated candidate score value sequence.
As an example, the target score value sequence may be [21, 22], and the updated candidate score value sequence may be [22]. And determining the target score value which is the same as any candidate score value in the updated candidate score value sequence as 0 in the target score value sequence, wherein the obtained updated target score value sequence is [21,0].
And a third screening step, namely adding 1 to the cycle count value.
Wherein the cycle count is used to calculate the number of cycles. For example, the initial value of the cycle count may be 0.
And a fourth screening step of determining the updated candidate feature position sequence as the target feature position sequence in response to determining that the cycle count value is greater than the preset number of times.
The preset number of times may be a preset maximum number of cycles. For example, the preset number of times may be 10 times.
And a second step of executing the filtering step again with the updated candidate score sequence as the candidate score sequence, the updated candidate feature position sequence as the candidate feature position sequence, and the updated target score sequence as the target score sequence in response to determining that the cycle count value is less than or equal to the preset number.
And thirdly, determining a first target interval corresponding to each target characteristic position in the target characteristic position sequence, and obtaining the first target interval sequence.
The first target interval is an interval between the target characteristic position and the corresponding traversal starting position.
As an example, the execution subject may determine a section between the target feature position and the corresponding traversal start position as the first target section.
Optionally, the generating, by the executing body, the updated candidate score value sequence and the updated candidate feature position sequence according to the target score value sequence and the feature position sequence may include the following steps:
a first step of executing the following sub-steps according to the target score value sequence and the characteristic position sequence:
and a first sub-step of determining the target score value at a preset position in the target score value sequence as a candidate score value.
Wherein the preset position may be a first position from a head in the target score value sequence.
And a second sub-step of determining the candidate score value as an updated second preset score value in response to determining that the candidate score value is greater than the second preset score value.
Wherein the second preset score value may be a score threshold. For example, the initial value of the second preset score value may be 0.
As an example, the candidate score value may be 21. The second preset fraction value may be 10. The candidate score value is determined to be an updated second preset score value, which may be 21.
And a third sub-step of removing the target score value at the preset position in the target score value sequence to obtain an updated target score value sequence.
As an example, the target score value sequence may be [21, 18, 22], and the target score value in the first head in the target score value sequence is removed, resulting in an updated target score value sequence of [18, 22].
And a fourth sub-step of determining, in response to determining that the updated target score value sequence is empty, the updated second preset score value as an updated candidate score value in the updated candidate score value sequence, and determining a feature position corresponding to the candidate score value as an updated candidate feature position in the updated candidate feature position sequence.
As an example, the updated second preset score value may be 21. The feature location corresponding to the candidate score value may be the 100 th bit. In response to determining that the updated sequence of target score values is empty, the updated second preset score value 21 is determined to be an updated candidate score value. And determining the 100 th bit of the feature position corresponding to the candidate score value as the updated candidate feature position.
And a second step of, in response to determining that the updated target score value sequence is not null, taking the updated target score value sequence as the target score value sequence and taking the updated second preset score value as the second preset score value, and executing the sub-steps again.
Optionally, after the first substep, the executing body may further execute the following steps:
and a first step of removing the target score value at the preset position in the target score value sequence to obtain an updated target score value sequence in response to the fact that the candidate score value is smaller than or equal to a second preset score value.
Wherein the preset position may be a first position from a head in the target score value sequence.
And a second step of determining a second preset score value as an updated candidate score value in the updated candidate score value sequence and determining a feature position corresponding to the second preset score value as an updated candidate feature position in the updated candidate feature position sequence in response to determining that the updated target score value sequence is empty.
The step 103 is an invention point of the embodiment of the present disclosure, and solves the second technical problem mentioned in the background section, namely that a plurality of candidate storage areas may be obtained when the storage area where the diagnostic data is located is determined, and the actual storage area where the diagnostic data of the vehicle is located is determined from the plurality of candidate storage areas manually, so that the search efficiency is low. Based on this, first, based on the target score value sequence and the characteristic position sequence, the following screening steps are performed: and a first screening step, namely generating an updated candidate score value sequence and an updated candidate feature position sequence according to the target score value sequence and the feature position sequence. And a second screening step, namely updating the target score value meeting the target condition in the target score value sequence into the first target value to obtain an updated target score value sequence. And a third screening step, namely adding 1 to the cycle count value. And a fourth screening step of determining the updated candidate feature position sequence as the target feature position sequence in response to determining that the cycle count value is greater than the preset number of times. Therefore, screening of the characteristic positions is completed through the target score value sequence, screening of storage areas corresponding to the characteristic positions is further achieved, and the retrieval efficiency of vehicle-mounted data is improved. Then, in response to determining that the cycle count value is equal to or less than the preset number of times, the filtering step is performed again with the updated candidate score sequence as the candidate score sequence, the updated candidate feature position sequence as the candidate feature position sequence, and the updated target score sequence as the target score sequence. Thus, by a plurality of cycles, a storage area having a high possibility of having vehicle diagnostic data is selected from the plurality of storage areas. And finally, determining a first target interval corresponding to each target characteristic position in the target characteristic position sequence to obtain the first target interval sequence. Therefore, through the circulation processing of the target score value sequence and the characteristic position sequence, a storage area with high possibility of vehicle diagnosis data is obtained, the problem of low retrieval efficiency when the actual storage area where the vehicle diagnosis data is located is determined from a plurality of candidate storage areas in a manual mode is avoided, and the retrieval efficiency of vehicle-mounted data is further improved.
And 104, generating feature matching information based on the first target interval sequence, the firmware data sequence and the pre-trained firmware data matching model to obtain a feature matching information sequence.
In some embodiments, the execution body may generate feature matching information based on the first target interval sequence, the firmware data sequence, and the pre-trained firmware data matching model, to obtain the feature matching information sequence. The firmware data matching model may be a text similarity matching model. For example, the firmware data matching model may be a BM25 model.
In some optional implementations of some embodiments, the executing body generates feature matching information based on the first target interval sequence, the firmware data sequence, and the pre-trained firmware data matching model, to obtain the feature matching information sequence, and may include the following steps:
a first step of executing the following data processing steps for each first target section in the first target section sequence:
and a first data processing step of determining the firmware data in the first target interval in the firmware data sequence as candidate firmware data to obtain a candidate firmware data set.
And a second data processing step of inputting the candidate firmware data in the candidate firmware data set into the pre-trained firmware data matching model to generate feature matching information corresponding to the candidate firmware data set.
The feature matching information includes a candidate firmware data set and a probability value corresponding to the candidate firmware data set.
As an example, first, the execution body may input the candidate firmware data in the candidate firmware data set into the pre-trained firmware data matching model to obtain a probability value corresponding to the candidate firmware data set. Then, the execution subject may determine the candidate firmware data set and the corresponding probability value as feature matching information.
And 105, determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval.
In some embodiments, the executing body may determine a first target interval corresponding to the feature matching information satisfying the feature screening condition in the feature matching information sequence as the second target interval. The feature filtering condition may be that the probability value included in the feature matching information is equal to the target probability value. The target probability value is the maximum value of probability values included in each feature matching information in the feature matching information sequence.
Alternatively, the in-vehicle firmware may be control firmware of an in-vehicle gas sensor included in the target vehicle. The vehicle-mounted gas sensor can be used for detecting gas in a target vehicle. For example, the gas sensor may be an alcohol sensor.
In some optional implementations of some embodiments, the executing body may further execute the following steps:
and a first step of determining the firmware data in the second target interval in the firmware data sequence as target data to obtain a target data set.
Wherein the target data in the target data set includes a feature identifier. The feature identification may be a service identification of the UDS database. For example, the signature may be 11.
And secondly, sending target data, wherein the characteristic identifiers of the target data sets are consistent with the target identifiers, to display equipment so that a target user can carry out fault diagnosis on the vehicle-mounted gas sensor corresponding to the vehicle-mounted firmware.
The display device may be a device having a display function. The target identifier may be an identifier included in target data in which vehicle diagnostic data is stored. For example, the target identifier may be 19.
The above embodiments of the present disclosure have the following advantages: according to the vehicle-mounted data retrieval method, the retrieval efficiency and the retrieval accuracy of the vehicle-mounted data can be improved. Specifically, the reason why the retrieval efficiency and the retrieval accuracy of the in-vehicle data are low is that: first, by determining the vehicle diagnosis data in the storage area manually, there may be a case of a manual judgment error, so that the retrieval accuracy of the vehicle data is low. Secondly, when the storage area where the diagnostic data is located is determined, a plurality of candidate storage areas may be obtained, and the actual storage area where the diagnostic data of the vehicle is located is determined from the plurality of candidate storage areas manually, so that the retrieval efficiency is low. Based on this, in the vehicle-mounted data retrieval method of some embodiments of the present disclosure, first, the firmware data in the firmware data sequence is sequentially retrieved to generate the target index information, so as to obtain the target index information sequence. The firmware data are data of vehicle-mounted firmware corresponding to a target vehicle to be detected, the target index information comprises an index position, and the index position represents the position of the firmware data corresponding to the target index information in the firmware data sequence. Since there may be a plurality of candidate storage areas in the firmware data sequence, it is necessary to sequentially search the firmware data in the firmware data sequence, thereby determining the index position of each of the plurality of candidate storage areas. Then, for each target index information in the target index information sequence described above, the following processing steps are performed: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the initial position to determine a target score value and a characteristic position corresponding to the target index information. Thus, the target score value and the characteristic position corresponding to the index position of each candidate storage area are obtained so as to screen the plurality of storage areas. And then, determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence. Therefore, screening of candidate storage areas and feature positions is completed according to the target score value sequence, and the problem of low retrieval efficiency of vehicle-mounted data caused by the fact that a plurality of candidate storage areas exist is avoided. And generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence. Therefore, according to the pre-trained firmware data matching model, further identification of the firmware data in the first target interval is completed, and the retrieval accuracy of the vehicle-mounted data is improved. And finally, determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval. Therefore, the actual storage area where the vehicle diagnosis data are is located is obtained by searching and screening the data of the vehicle-mounted firmware, the problems of low efficiency and low accuracy caused by adopting a manual mode are avoided, and the searching efficiency and the searching accuracy of the vehicle-mounted data are improved.
With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of an in-vehicle data retrieval apparatus, which correspond to those method embodiments shown in fig. 1, and which are particularly applicable in various electronic devices.
As shown in fig. 2, the in-vehicle data retrieval device 200 of some embodiments includes: an information retrieval unit 201, an information processing unit 202, a first section determination unit 203, an information generation unit 204, and a second section determination unit 205. The information retrieval unit 201 is configured to sequentially retrieve firmware data in a firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data is data of vehicle-mounted firmware corresponding to a target vehicle to be detected, the target index information comprises an index position, and the index position represents the position of the firmware data corresponding to the target index information in the firmware data sequence; the information processing unit 202 is configured to perform the following processing steps for each target index information in the above-described target index information sequence: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the traversing initial position to determine a target score value and a characteristic position corresponding to the target index information; the first interval determining unit 203 is configured to determine a first target interval according to the obtained target score value sequence and the feature position sequence, so as to obtain a first target interval sequence; the information generating unit 204 is configured to generate feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model, and obtain a feature matching information sequence; the second section determining unit 205 is configured to determine, as a second target section, a first target section corresponding to feature matching information satisfying the feature screening condition in the feature matching information sequence.
It will be appreciated that the elements described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.
Referring now to fig. 3, a schematic diagram of an electronic device (e.g., computing device) 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: sequentially retrieving firmware data in a firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data is data of vehicle-mounted firmware corresponding to a target vehicle to be detected, and the target index information comprises index positions which represent positions of the firmware data corresponding to the target index information in the firmware data sequence; for each target index information in the target index information sequence, the following processing steps are performed: determining an index position included in the target index information as a traversal starting position; traversing the firmware data in the firmware data sequence from the initial position to determine a target score value and a characteristic position corresponding to the target index information; determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence; generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence; and determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an information retrieval unit, an information processing unit, a first section determination unit, an information generation unit, and a second section determination unit. The names of the units are not limited to the unit itself in some cases, and for example, the second section determining unit may be described as a "unit for determining the first target section corresponding to the feature matching information satisfying the feature screening condition in the feature matching information sequence as the second target section".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Some embodiments of the present disclosure also provide a computer program product comprising a computer program which, when executed by a processor, implements any of the above-described vehicle data retrieval methods.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (8)
1. A vehicle-mounted data retrieval method, comprising:
sequentially retrieving firmware data in a firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data are data of vehicle-mounted firmware corresponding to a target vehicle to be detected, and the target index information comprises index positions which represent positions of the firmware data corresponding to the target index information in the firmware data sequence;
for each target index information in the sequence of target index information, performing the following processing steps:
Determining an index position included in the target index information as a traversal starting position;
traversing the firmware data in the firmware data sequence from the traversing initial position to determine a target score value and a characteristic position corresponding to the target index information; determining a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence;
generating feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence;
determining a first target interval corresponding to the feature matching information meeting the feature screening conditions in the feature matching information sequence as a second target interval;
wherein the traversing the firmware data in the firmware data sequence from the traversing starting position to determine the target score value and the feature position corresponding to the target index information includes:
for the traversal start position, the following processing steps are performed:
determining firmware data at a target position in the firmware data sequence as first candidate data, wherein an initial value corresponding to the target position is 1;
In response to determining that a target sample identifier exists in the initial sample identifier set, determining the sum of an initial score value and a first preset score value as a candidate target score value, and updating an initial characteristic position by using a target position to obtain an updated initial characteristic position, wherein the initial value of the initial score value is 0, the initial value corresponding to the initial characteristic position is 1, and the first candidate data is the same as the target sample identifier and is not equal to a first target value;
updating the numerical value of the target sample identifier in the initial sample identifier set to the first target value to obtain an updated sample identifier set;
adding the value corresponding to the target position with the second target value to obtain an updated target position;
in response to determining that the value corresponding to the updated target position is greater than a third target value, determining a candidate target score value as a target score value corresponding to the target index information, and determining an updated initial feature position as a feature position corresponding to the target index information;
and in response to determining that the value corresponding to the updated target position is less than or equal to the third target value, performing the processing step again with the updated target position as the target position, the updated sample identification set as the initial sample identification set, the candidate target score value as the initial score value, and the updated initial feature position as the initial feature position.
2. The method of claim 1, wherein the onboard firmware is control firmware of an onboard gas sensor included with the target vehicle; and
the method further comprises the steps of:
determining firmware data in the second target interval in the firmware data sequence as target data to obtain a target data set, wherein the target data in the target data set comprises a feature identifier;
and sending target data, wherein the characteristic identifiers of the target data sets are consistent with the target identifiers, to display equipment so that a target user can perform fault diagnosis on the vehicle-mounted gas sensor corresponding to the vehicle-mounted firmware.
3. The method of claim 2, wherein after the determining the firmware data at the target location in the sequence of firmware data as the first candidate data, the method further comprises:
in response to determining that the target sample identifier does not exist in the initial sample identifier set, adding a value corresponding to the target position to the second target value to obtain an updated target position;
in response to determining that the updated value corresponding to the target position is greater than the third target value, determining an initial score value as the target score value corresponding to the target index information, and determining an initial feature position as the feature position corresponding to the target index information.
4. The method of claim 3, wherein the generating feature matching information based on the first target interval sequence, the firmware data sequence, and a pre-trained firmware data matching model, resulting in a feature matching information sequence, comprises:
for each first target interval in the sequence of first target intervals, performing the following data processing steps:
determining the firmware data in the first target interval in the firmware data sequence as candidate firmware data to obtain a candidate firmware data set;
and inputting the candidate firmware data in the candidate firmware data set into the pre-trained firmware data matching model to generate feature matching information corresponding to the candidate firmware data set.
5. An in-vehicle data retrieval device comprising:
the information retrieval unit is configured to sequentially retrieve firmware data in the firmware data sequence to generate target index information to obtain a target index information sequence, wherein the firmware data are data of vehicle-mounted firmware corresponding to a target vehicle to be detected, the target index information comprises index positions, and the index positions represent positions of indexes corresponding to the target index information in the firmware data sequence;
An information processing unit configured to perform, for each target index information in the target index information sequence, the following processing steps:
determining an index position included in the target index information as a traversal starting position;
traversing the firmware data in the firmware data sequence from the traversing starting position to determine a target score value and a characteristic position corresponding to the target index information, wherein the traversing the firmware data in the firmware data sequence from the traversing starting position to determine the target score value and the characteristic position corresponding to the target index information comprises the following steps: for the traversal start position, the following processing steps are performed: determining firmware data at a target position in the firmware data sequence as first candidate data, wherein an initial value corresponding to the target position is 1; in response to determining that a target sample identifier exists in the initial sample identifier set, determining the sum of an initial score value and a first preset score value as a candidate target score value, and updating an initial characteristic position by using a target position to obtain an updated initial characteristic position, wherein the initial value of the initial score value is 0, the initial value corresponding to the initial characteristic position is 1, and the first candidate data is the same as the target sample identifier and is not equal to a first target value; updating the numerical value of the target sample identifier in the initial sample identifier set to the first target value to obtain an updated sample identifier set; adding the value corresponding to the target position with the second target value to obtain an updated target position; in response to determining that the value corresponding to the updated target position is greater than a third target value, determining a candidate target score value as a target score value corresponding to the target index information, and determining an updated initial feature position as a feature position corresponding to the target index information; in response to determining that the value corresponding to the updated target position is less than or equal to the third target value, the processing step is performed again with the updated target position as the target position, the updated sample identification set as the initial sample identification set, the candidate target score value as the initial score value, and the updated initial feature position as the initial feature position;
The first interval determining unit is configured to determine a first target interval according to the obtained target score value sequence and the characteristic position sequence to obtain a first target interval sequence;
the information generation unit is configured to generate feature matching information based on the first target interval sequence, the firmware data sequence and a pre-trained firmware data matching model to obtain a feature matching information sequence;
and the second interval determining unit is configured to determine a first target interval corresponding to the feature matching information meeting the feature screening condition in the feature matching information sequence as a second target interval.
6. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 4.
7. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1 to 4.
8. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-4.
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