Vehicle end data acquisition preprocessing method and system based on signal abstraction

Abstract

The invention discloses a vehicle end data acquisition preprocessing method based on signal abstraction. The method comprises the following steps: acquiring an original signal; splitting the business scene, and performing expression processing to generate an expression group associated with the business scene; screening and judging the original signals according to the utilization rate, determining the original signals with the utilization rate higher than a preset first threshold value as high-frequency original signals, and determining the original signals with the utilization rate lower than the preset first threshold value as low-frequency original signals; performing signal abstraction on the high-frequency original signal in combination with the expression group to obtain a generated abstract signal; and storing the low-frequency original signal and the abstract signal. According to the technical scheme, compared with the prior art, the processing efficiency of the original signal can be improved, the utilization rate of the vehicle end data processing computing power is improved, and the cloud end data processing pressure is reduced. In addition, the invention also relates to a technical scheme of a vehicle end data acquisition and preprocessing system based on signal abstraction, which also has the above beneficial effects.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle data processing, and more specifically, to a vehicle-side data acquisition preprocessing method based on signal abstraction. In addition, the present invention also relates to a vehicle-side data acquisition preprocessing system based on signal abstraction. Background Art

[0002] With the rise of intelligent connected cars and the trend of data assetization, collecting car data has become a general trend. As the scale of vehicles increases, the amount of on-board data is also expanding, which puts higher requirements on the effectiveness of vehicle data.

[0003] like Figure 2 As shown in the figure, the currently commonly used method is that the vehicle-side equipment collects the original signals from the vehicle-side, uploads all the original data to the cloud platform at a certain frequency, and relies on the computing power of the cloud platform to analyze and process the original data from the vehicle-side. This centralized data processing method is overly dependent on cloud computing power.

[0004] Therefore, how to provide a vehicle-side data acquisition preprocessing method based on signal abstraction, which can improve the processing efficiency of the original signal, improve the utilization rate of the vehicle-side data processing computing power, and reduce the cloud data processing pressure, has become a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a vehicle-side data acquisition preprocessing method based on signal abstraction, which can improve the processing efficiency of the original signal, improve the utilization rate of the vehicle-side data processing computing power, and reduce the cloud data processing pressure. In addition, the present invention also relates to a series of technical solutions for a vehicle-side data acquisition preprocessing system and a vehicle data processing method based on signal abstraction, which also have the above beneficial effects.

[0006] The technical solution provided by the present invention is as follows:

[0007] The present invention provides a technical solution for a vehicle-side data collection preprocessing method based on signal abstraction. The method comprises the following steps: collecting original signals; splitting business scenarios, and performing expression processing to generate expression groups associated with business scenarios; screening and judging original signals according to utilization, defining original signals with utilization rates higher than a preset first threshold as high-frequency original signals, and defining original signals with utilization rates lower than the preset first threshold as low-frequency original signals; performing signal abstraction on the high-frequency original signals in combination with the expression group to generate abstract signals; and storing the low-frequency original signals and the abstract signals.

[0008] Furthermore, in a preferred embodiment of the present invention, the "splitting the business scenario, performing expression processing, and generating an expression group associated with the business scenario" specifically includes the following steps: analyzing the business scenario, confirming the acquisition condition content associated with the business scenario and the corresponding acquisition data object; matching the first original signal according to the acquisition condition content, and matching the second original signal according to the acquisition data object; in the associated business scenario, redescribe the business scenario with the first original signal as the smallest unit to generate a conditional expression for the business scenario acquisition condition, and redescribe the business scenario with the second original signal as the smallest unit to generate a collection expression for the business scenario acquisition data; the conditional expression and the collection expression constitute the expression group.

[0009] Furthermore, in a preferred embodiment of the present invention, the "analyzing the business scenario to confirm the collection condition content and the corresponding collection data object associated with the business scenario" specifically includes the following steps: numbering the business scenario to obtain the scenario number; analyzing the data object to be collected under the business scenario to obtain the collection data object; analyzing the triggering conditions of the business scenario around the collection data object to obtain the collection condition content associated with the business scenario; storing the collection condition content as a collection condition item in association with the scenario number; and storing the collection data object as a collection data item in association with the scenario number.

[0010] Further, in a preferred embodiment of the present invention, the step of "matching the first original signal according to the acquisition condition content, and matching the second original signal according to the acquisition data object" specifically includes the following steps: matching the first original signal corresponding to the acquisition condition content according to the acquisition condition content; matching the second original signal corresponding to the acquisition data object according to the acquisition data object; storing the first original signal as a conditional signal item in association with the scene number; and storing the second original signal as a data signal item in association with the scene number.

[0011] Further, in a preferred embodiment of the present invention, the "in an associated business scenario, redescribe the business scenario with the first original signal as the smallest unit to generate a conditional expression for the business scenario acquisition conditions, and redescribe the business scenario with the second original signal as the smallest unit to generate a collection expression for the business scenario acquisition data" specifically includes the following steps: in combination with preset grammar rules, redescribe the business scenario with the first original signal as the smallest unit to obtain a conditional expression, and redescribe the business scenario with the second original signal as the smallest unit to obtain a collection expression; the preset grammar rules include: conditional keywords, operation keywords, and logical operators.

[0012] Furthermore, in a preferred embodiment of the present invention, the method of "screening and judging the original signal according to the utilization rate, defining the original signal having a utilization rate higher than a preset first threshold as a high-frequency original signal, and defining the original signal having a utilization rate lower than the preset first threshold as a low-frequency original signal" specifically includes the following steps: analyzing the utilization rate of the original signal based on three indicators, namely, multiplexing degree, difference degree, and coupling degree, to obtain the utilization rate value of the original signal; numerically comparing the utilization rate value corresponding to the original signal with the preset first threshold; classifying the original signal having a utilization rate value higher than the preset first threshold as a high-frequency original signal; and classifying the original signal having a utilization rate value lower than the preset first threshold as a low-frequency original signal.

[0013] Furthermore, in a preferred embodiment of the present invention, the utilization value = multiplexing * difference * (1-coupling degree); wherein the multiplexing is related to the number of business scenarios involved in the original signal; the difference is related to the number of types of conditional expressions and / or acquisition expressions involved in the original signal; and the coupling degree is related to the degree of correlation between the original signal and other signals.

[0014] Furthermore, in a preferred embodiment of the present invention, the "combining the expression group to perform signal abstraction on the high-frequency original signal to generate an abstract signal" specifically includes the following steps: obtaining the high-frequency original signal; traversing and obtaining conditional expressions and acquisition expressions associated with the high-frequency original signal; identifying the minimum correlation expression of the high-frequency original signal; and abstracting the high-frequency original signal in combination with the minimum correlation expression to obtain the abstract signal.

[0015] The present invention also provides a technical solution for a vehicle-side data acquisition preprocessing system based on signal abstraction. The system executes the above-mentioned vehicle-side data acquisition preprocessing method based on signal abstraction; the system includes: a first acquisition module, the first acquisition module is used to collect original signals; a first execution module, the first execution module is used to split the business scenario, and perform expression processing to generate an expression group associated with the business scenario; a second execution module, the second execution module is used to screen and judge the original signal according to the utilization rate, and define the original signal with a utilization rate higher than the preset first threshold as a high-frequency original signal, and define the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal; a third execution module, the third execution module is used to perform signal abstraction on the high-frequency original signal in combination with the expression group to generate an abstract signal; a fourth execution module, the fourth execution module is used to store the low-frequency original signal and the abstract signal.

[0016] The present invention also provides a technical solution for a method for processing automobile data. The processing method comprises the following steps: the above-mentioned vehicle-side data acquisition preprocessing method based on signal abstraction; renumbering and storing the low-frequency original signal and the abstract signal through a data pool; uploading the data in the data pool to the platform for processing through a data upload module.

[0017] In addition, the present invention also provides a technical solution for an electronic device, including: a computer program, which is used to execute the above-mentioned vehicle-side data acquisition preprocessing method based on signal abstraction; a memory, which is used to store the computer program; and a processor, which is used to execute the computer program.

[0018] The present invention provides a vehicle-side data acquisition and preprocessing method based on signal abstraction. Compared with the prior art, after collecting the original signal, the high-frequency original signal that is screened out is used to abstract the original signal associated with the business scenario using an expression group to obtain an abstract signal, and the abstract signal is stored together with the low-frequency original signal to complete the acquisition and preprocessing operations. Furthermore, the high-frequency original signal has a high degree of repetition and a large amount of data. The high-frequency original signal is abstracted to obtain an abstract signal. The data amount of the abstract signal is small, which is conducive to subsequent data storage and data processing; improving data processing efficiency. The technical solution provided by the present invention can improve the processing efficiency of the original signal, improve the utilization rate of the vehicle-side data processing computing power, and reduce the pressure of cloud data processing. In addition, the present invention also relates to a series of technical solutions for a vehicle-side data acquisition and preprocessing system and an automobile data processing method based on signal abstraction, which also have the above-mentioned beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flow chart of the main steps of the vehicle-side data acquisition preprocessing method based on signal abstraction provided in an embodiment of the present invention;

[0021] Figure 2 This is a flow chart of vehicle data processing in the prior art;

[0022] Figure 3 A process node diagram for scene analysis of raw signal data, signal abstraction, ID allocation and storage provided by an embodiment of the present invention;

[0023] Figure 4A schematic diagram of the structure of a vehicle-side data acquisition and preprocessing system based on signal abstraction provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that when an element is referred to as being "fixed on" or "set on" another element, it can be directly on the other element or indirectly set on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0026] It should be understood that the terms "length", "width", "up", "down", "front", "back", "first", "second", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0027] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" and "several" is two or more, unless otherwise clearly and specifically defined.

[0028] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0029] like Figures 1 to 4As shown, the technical solution of the vehicle-side data collection and preprocessing method based on signal abstraction provided by an embodiment of the present invention. The method includes the following steps: collecting original signals; splitting business scenarios, and performing expression processing to generate expression groups associated with business scenarios; screening and judging original signals according to utilization, defining original signals with utilization rates higher than a preset first threshold as high-frequency original signals, and defining original signals with utilization rates lower than a preset first threshold as low-frequency original signals; performing signal abstraction on the high-frequency original signals in combination with the expression group to generate abstract signals; and storing the low-frequency original signals and the abstract signals.

[0030] The present invention provides a technical solution for a vehicle-side data acquisition and preprocessing method based on signal abstraction. After the original signal is collected, the high-frequency original signal that is screened out is used to abstract the original signal associated with the business scenario using an expression group to obtain an abstract signal, and the abstract signal is stored together with the low-frequency original signal to complete the acquisition and preprocessing operations. Furthermore, the high-frequency original signal has a high degree of repetition and a large amount of data. The high-frequency original signal is abstracted to obtain an abstract signal. The data volume of the abstract signal is small, which is conducive to subsequent data storage and data processing, and improves data processing efficiency. The technical solution provided by the present invention can improve the processing efficiency of the original signal, improve the utilization rate of the vehicle-side data processing computing power, and reduce the pressure of cloud data processing.

[0031] Specifically, in an embodiment of the present invention, the "splitting the business scenario, performing expression processing, and generating an expression group associated with the business scenario" specifically includes the following steps: analyzing the business scenario, confirming the collection condition content and the corresponding collection data object associated with the business scenario; matching the first original signal according to the collection condition content, and matching the second original signal according to the collection data object; in the associated business scenario, redescribe the business scenario with the first original signal as the smallest unit to generate a conditional expression for the business scenario collection condition, and redescribe the business scenario with the second original signal as the smallest unit to generate a collection expression for the business scenario collection data; the conditional expression and the collection expression constitute the expression group.

[0032] It should be noted that, through conditional expressions and collection expressions, the business scenario can be accurately re-described to obtain reliable abstract signals and improve the quality of data preprocessing.

[0033] Specifically, in an embodiment of the present invention, the "analyzing the business scenario to confirm the collection condition content associated with the business scenario and the corresponding collection data object" specifically includes the following steps: numbering the business scenario to obtain a scenario number; analyzing the data objects to be collected under the business scenario to obtain the collection data object; analyzing the triggering conditions of the business scenario around the collection data object to obtain the collection condition content associated with the business scenario; storing the collection condition content as a collection condition item in association with the scenario number; and storing the collection data object as a collection data item in association with the scenario number.

[0034] Specifically, in an embodiment of the present invention, the "matching the first original signal according to the acquisition condition content, and matching the second original signal according to the acquisition data object" specifically includes the following steps: matching the first original signal corresponding to the acquisition condition content according to the acquisition condition content; matching the second original signal corresponding to the acquisition data object according to the acquisition data object; storing the first original signal as a conditional signal item in association with the scene number; and storing the second original signal as a data signal item in association with the scene number.

[0035] It should be noted that by setting the scene number, collection condition item, collection data item, conditional signal item, and collection signal item, the collection conditions, collection data, and related original signals related to the business scenario can be accurately sorted out to improve the efficiency of subsequent abstract processing.

[0036] Specifically, in an embodiment of the present invention, the "in an associated business scenario, redescribe the business scenario with the first original signal as the smallest unit to generate a conditional expression for the business scenario acquisition conditions, and redescribe the business scenario with the second original signal as the smallest unit to generate a collection expression for the business scenario acquisition data" specifically includes the following steps: in combination with preset grammar rules, redescribe the business scenario with the first original signal as the smallest unit to obtain a conditional expression, and redescribe the business scenario with the second original signal as the smallest unit to obtain a collection expression; the preset grammar rules include: conditional keywords, operation keywords, and logical operators.

[0037] It should be noted that the conditional keywords, operation keywords, and logical operators are all preset grammatical rules, as shown in the following embodiments.

[0038] Specifically, in an embodiment of the present invention, the "screening and judging the original signal according to the utilization rate, defining the original signal with a utilization rate higher than a preset first threshold as a high-frequency original signal, and defining the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal" specifically includes the following steps: based on the three indicators of multiplexing, difference, and coupling, the utilization rate of the original signal is analyzed to obtain the utilization value of the original signal; the utilization value corresponding to the original signal is numerically compared with the preset first threshold; the original signal with a utilization value higher than the preset first threshold is classified as a high-frequency original signal; the original signal with a utilization value lower than the preset first threshold is classified as a low-frequency original signal.

[0039] Specifically, in an embodiment of the present invention, the utilization value = multiplexing * difference * (1-coupling degree); wherein the multiplexing is related to the number of business scenarios involved in the original signal; the difference is related to the number of types of conditional expressions and / or acquisition expressions involved in the original signal; and the coupling degree is related to the degree of correlation between the original signal and other signals.

[0040] Specifically, in an embodiment of the present invention, the "combining the expression group to perform signal abstraction on the high-frequency original signal to generate an abstract signal" specifically includes the following steps: obtaining the high-frequency original signal; traversing and obtaining conditional expressions and acquisition expressions associated with the high-frequency original signal; identifying the minimum correlation expression of the high-frequency original signal; and abstracting the high-frequency original signal in combination with the minimum correlation expression to obtain the abstract signal.

[0041] In addition, the present invention also provides a technical solution for a vehicle-side data acquisition and preprocessing system based on signal abstraction. The system executes the above-mentioned vehicle-side data acquisition and preprocessing method based on signal abstraction; the system includes: a first acquisition module, the first acquisition module is used to acquire original signals; a first execution module, the first execution module is used to split the business scenario, and perform expression processing to generate an expression group associated with the business scenario; a second execution module, the second execution module is used to screen and judge the original signal according to the utilization rate, and define the original signal with a utilization rate higher than the preset first threshold as a high-frequency original signal, and define the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal; a third execution module, the third execution module is used to perform signal abstraction on the high-frequency original signal in combination with the expression group to generate an abstract signal; a fourth execution module, the fourth execution module is used to store the low-frequency original signal and the abstract signal. The technical solution of a vehicle-side data acquisition and preprocessing system based on signal abstraction provided by the present invention also has the beneficial effects of the above-mentioned technical solution.

[0042] In addition, the present invention also provides a technical solution for a method for processing automobile data. The processing method includes the following steps: the above-mentioned vehicle-side data acquisition preprocessing method based on signal abstraction; renumbering and storing the low-frequency original signal and the abstract signal through a data pool; uploading the data in the data pool to the platform for processing through a data upload module. The technical solution for a method for processing automobile data provided by the present invention also has the beneficial effects of the above-mentioned technical solution.

[0043] In addition, the present invention also provides a technical solution for an electronic device, comprising: a computer program, the computer program is used to execute the above-mentioned vehicle-side data acquisition preprocessing method based on signal abstraction; a memory, the memory is used to store the computer program; and a processor is used to execute the computer program. The technical solution for an electronic device provided by the present invention also has the beneficial effects of the above-mentioned technical solution.

[0044] It should be added that Figure 2 As shown, the existing technology has the following problems: First, the centralized data processing method is overly dependent on cloud computing power. According to the changes in the number of online vehicles in different time periods, the platform computing power requirements will also change greatly; Second, the characteristics of periodic upload will contain a lot of invalid and repeated information, consuming a lot of bandwidth and traffic; Third, due to the limitations of network bandwidth and traffic costs, the cycle value in the existing cycle scheme cannot be set too small, which will make some scenarios that rely on high-frequency data impossible to achieve.

[0045] It should be added that the use examples of the technical solution of the present invention are:

[0046] The technical solution provided by the present invention comprises the following steps:

[0047] First, the business scenario needs to be split and reorganized from the operational logic level in the event-oriented abstraction solution. The business scenario will be split into collection condition items and collection data items.

[0048] The collection condition items and collection data items are further split into signals. Finally, according to the business logic, a set of logical grammars are used to redefine the business scenarios. The grammatical elements used include conditional keywords, logical operators, and operation keywords, as shown in the following table:

[0049] Table 1: Condition keywords, logical operators, and operation keywords examples

[0050]

[0051]

[0052]

[0053] Among them, the original signal, conditional keywords and logical operators can constitute the acquisition condition items; the original signal, operation keywords and logical operators constitute the acquisition data items.

[0054] The above process is divided into three steps, which can be analyzed according to the following table tool:

[0055] 1: Split the business scenario into collection condition items and collection data items;

[0056]

[0057] 2. Based on the analysis results in the first step, the original signals associated with the acquisition condition item and the acquisition data item are identified respectively;

[0058]

[0059] 3: Based on the analysis results of the second step, the scene is re-described using the specified grammatical rules, taking the original signal as the smallest unit.

[0060]

[0061]

[0062] It should be noted that after the business scenarios are split, signal abstract judgment is performed.

[0063] Specifically, the purpose of signal abstraction is to reduce the frequency of data upload and save bandwidth without reducing the amount of information. In order to balance the computing power of the vehicle, traffic demand, and cloud load, the following descriptive attributes and corresponding measurement criteria are introduced for each signal to determine whether a signal needs to be abstracted:

[0064] Reuse degree: This attribute describes the degree to which a signal is reused in all scenarios. For example, if a signal has a low reuse degree, that is, it is only used in very few scenarios, then periodically uploading this data is a huge waste of bandwidth and needs to be abstracted in combination with the scenario. On the contrary, if a signal has a high reuse degree, it is cost-effective to spend a certain amount of bandwidth to repeatedly upload data.

[0065] The statistical value calculation method of multiplexing is defined as: the number of scenes where a specific original signal appears / the total number of scenes * 100%

[0066] Difference: This attribute describes how many different logical processing methods a signal has in all scenarios. For example, if a signal only focuses on the situation when it is a certain value in all scenarios, it is necessary to abstract this signal as a new signal for a certain value, so as to avoid uploading data with the signal equal to other values, so as to save bandwidth and traffic. On the contrary, if a signal needs to be processed in many logical ways in different scenarios, it may increase the computing pressure of the vehicle-side processor.

[0067] The statistical value calculation method of the difference is defined as: the logic type used by the specific original signal in all scenes / all logic types * 100%

[0068] Coupling degree: This attribute describes the degree of association between a signal and other signals. For example, if a signal always appears with other signals in a conditional expression or a collection item expression, it means that the amount of information contained in the signal when transmitted alone is limited, and frequent periodic transmission may waste bandwidth, so it is inclined to abstract the signal and other signals into a new signal. On the contrary, if a signal appears independently in most scenarios, it means that the amount of information in the signal is large, and it is cost-effective to spend a certain amount of bandwidth to transmit it.

[0069] The calculation method of the statistical value of the coupling degree is defined as: 1-(the number of times the specific original data appears alone in the conditional expression + the number of times the specific original data appears alone in the operation expression) / the total number of times the specific original data appears*100%.

[0070] Combining the above three attributes, we can calculate the utilization rate of a signal: the degree to which the original signal is utilized. If the utilization rate of a signal is low, it means that the probability that the signal data is effectively applied to the scene is smaller, and the signal with a lower value should be more inclined to be re-abstracted.

[0071] The calculation method of the utilization value is defined as = multiplexing degree * difference degree * (1-coupling degree).

[0072] The calculation example of the original signal utilization value is as follows:

[0073] Original signal Reuse Difference Coupling Utilization Signal S1 20％ 30％ 30％ 4.2％ Signal S2 20％ 40％ 70％ 2.4％ Signal S3 80％ 60％ 40％ 28.8％ Signal S4 60％ 70％ 60％ 16.8％

[0074] The utilization rate of each signal can be calculated according to the above calculation method. According to the actual project and historical experience, a threshold can be set to define which signals are suitable for abstraction.

[0075] It should be noted that after the abstract judgment of the original signal is completed, the conditional expression and the collection item expression in the scene splitting table are combined to abstract the minimum expression related to the specified signal (high-frequency original signal) into a new signal.

[0076] For example, if the S2 signal is judged to need to be abstracted, then find the expressions related to S2: Equal(S1, 4) AND Great(S2, 6) AND Change(S3). The minimum related expression is Great(S2, 6), that is, the event that the S2 signal value is greater than 6 will be abstracted into a signal S10. Only when the value of S2 changes from less than 6 to greater than 6 will the signal S10 be sent to the platform.

[0077] Another related conditional expression: During(Great(S2, 6), Less(S2, 4)) also needs to be abstracted. Less(S2, 4) will be abstracted into a new signal S11 (abstract signal), that is, signal S11 will be sent to the platform only when the value of S2 changes from greater than 4 to less than 4.

[0078] It should be noted that if Figure 3 As shown, the abstracted signals and the unabstracted signals are stored. A vehicle-side data pool is established. In this solution, the vehicle-side data pool includes two categories: unabstracted element signals; and new signals after abstraction. We need to renumber all data lines according to the following steps to facilitate data pool management:

[0079] Finally Figure 4 As shown, it should be noted that, compared with the prior art solution, in the solution of the present invention, a data pool and a data preprocessing module are added to the vehicle end. The acquisition module stores the collected data in the data pool; the data preprocessing module is responsible for generating new data items from the original signal collected by the acquisition module and storing them in the data pool. The data upload module is responsible for uploading the data in the data pool to the platform.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle-side data acquisition preprocessing method based on signal abstraction, characterized in that: The following steps are involved: Collect the original signal; Split the business scenario and perform expression processing to generate an expression group associated with the business scenario; Screening and judging the original signal according to the utilization rate, defining the original signal with a utilization rate higher than a preset first threshold as a high-frequency original signal, and defining the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal; Performing signal abstraction on the high-frequency original signal in combination with the expression group to generate an abstract signal; The low-frequency original signal and the abstract signal are stored.

2. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 1 is characterized in that: The "splitting the business scenario, performing expression processing, and generating an expression group associated with the business scenario" specifically includes the following steps: Analyze the business scenarios and confirm the collection conditions and corresponding collection data objects associated with the business scenarios; According to the acquisition condition content, matching a first original signal; According to the collected data object, matching a second original signal; In the associated business scenario, the business scenario is re-described with the first original signal as the smallest unit to generate a conditional expression of the business scenario acquisition condition; the business scenario is re-described with the second original signal as the smallest unit to generate an acquisition expression of the business scenario acquisition data; The conditional expression and the acquisition expression constitute the expression group.

3. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 2 is characterized in that: The "analyzing the business scenario and confirming the collection conditions and corresponding collection data objects associated with the business scenario" specifically includes the following steps: Number the business scenarios to obtain scenario numbers; Analyze the data objects that need to be collected in the business scenario and obtain the collected data objects; Analyze the triggering conditions of the business scenario around the collected data object to obtain the collection condition content associated with the business scenario; storing the acquisition condition content as an acquisition condition item in association with the scene number; The collected data object is stored as a collected data item in association with the scene number.

4. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 2 is characterized in that: The "matching the first original signal according to the acquisition condition content, and matching the second original signal according to the acquisition data object" specifically includes the following steps: According to the acquisition condition content, matching a first original signal corresponding to the acquisition condition content; According to the collected data object, matching a second original signal corresponding to the collected data object; storing the first original signal as a conditional signal item in association with the scene number; The second original signal is stored as a data signal item in association with the scene number.

5. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 2 is characterized in that: The "under the associated business scenario, re-describing the business scenario with the first original signal as the smallest unit to generate a conditional expression of the business scenario acquisition condition; The method of re-describing the business scenario by taking the second original signal as the smallest unit and generating a collection expression of the business scenario collection data specifically includes the following steps: In combination with preset grammar rules, the service scenario is re-described with the first original signal as the smallest unit to obtain a conditional expression, and the service scenario is re-described with the second original signal as the smallest unit to obtain a collection expression; The preset grammar rules include: conditional keywords, operation keywords, and logical operators.

6. The vehicle-side data acquisition preprocessing method based on signal abstraction according to any one of claims 1 to 5, characterized in that: The "screening and judging the original signal according to the utilization rate, defining the original signal with a utilization rate higher than the preset first threshold as a high-frequency original signal, and defining the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal" specifically includes the following steps: Based on the three indicators of multiplexing, difference and coupling, the utilization of the original signal is analyzed to obtain the utilization value of the original signal; Compare the utilization value corresponding to the original signal with a preset first threshold; Classifying the original signal whose utilization value is higher than the preset first threshold as a high-frequency original signal; The original signal whose utilization value is lower than the preset first threshold is classified as a low-frequency original signal.

7. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 6 is characterized in that: The utilization value = reuse degree * difference degree * (1-coupling degree) The multiplexing degree is related to the number of service scenarios involved in the original signal; The difference is related to the number of types of conditional expressions and / or acquisition expressions involved in the original signal; The coupling degree is related to the degree of correlation between the original signal and other signals.

8. The vehicle-side data acquisition preprocessing method based on signal abstraction according to claim 6 is characterized in that: The "combining the expression group to perform signal abstraction on the high-frequency original signal to generate an abstract signal" specifically includes the following steps: Acquire the high-frequency original signal; Traversing and acquiring conditional expressions and acquisition expressions associated with the high-frequency original signal; Identifying a minimum correlation expression of the high frequency original signal; The high-frequency original signal is abstracted in combination with the minimum correlation expression to obtain the abstract signal.

9. The vehicle-side data acquisition preprocessing system based on signal abstraction is characterized by: The system executes the vehicle-side data acquisition preprocessing method based on signal abstraction as described in any one of claims 1 to 8; The system includes: A first acquisition module, wherein the first acquisition module is used to acquire original signals; A first execution module, wherein the first execution module is used to split the business scenario, perform expression processing, and generate an expression group associated with the business scenario; A second execution module, the second execution module is used to screen and judge the original signal according to the utilization rate, and define the original signal with a utilization rate higher than the preset first threshold as a high-frequency original signal, and define the original signal with a utilization rate lower than the preset first threshold as a low-frequency original signal; A third execution module, the third execution module is used to perform signal abstraction on the high-frequency original signal in combination with the expression group to generate an abstract signal, thereby generating an abstract signal; A fourth execution module, wherein the fourth execution module is used to store the low-frequency original signal and the abstract signal.

10. A method for processing automobile data, characterized in that: The processing method comprises the following steps: The vehicle-side data acquisition preprocessing method based on signal abstraction as described in any one of claims 1 to 8; Renumbering and storing the low-frequency original signal and the abstract signal in a data pool; The data in the data pool is uploaded to the platform for processing through the data upload module.

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