Data processing method and device, equipment and storage medium

Abstract

The invention relates to the field of data processing, and discloses a data processing method, device and equipment and a storage medium, and the method comprises the steps: for each first time interval, collecting a target parameter in the first time interval in real time to obtain each piece of to-be-processed data, and based on a preset blocking rule, carrying out the blocking counting of each piece of to-be-processed data, obtaining a count value corresponding to each target block; according to the count value corresponding to each target block, the block median of each target block and a preset statistical model, determining each statistical value in the first time interval; and analyzing and controlling the vehicle based on each statistical value in the first time interval. Through the technical scheme of the invention, lightweight data processing and transmission are realized, the processing data volume is effectively reduced, and the data analysis processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a data processing method, apparatus, device, and storage medium. Background Art

[0002] The communication data traffic demand of the internal bus of vehicle-mounted controllers is increasing. Performance problems caused by various data communication blockages and resource consumption have gradually become the focus of controller design.

[0003] However, not all of the increasing amounts of data are effectively utilized. With the growth of vehicle control domain functions, the demand for real-time data transmission has been greatly released, and a large amount of vehicle-end data has been transmitted and stored. However, the problem of effective data utilization has emerged. In fact, not every real-time piece of data is utilized, but only some data is selected for use based on characteristics. Due to the characteristics of vehicle control, especially intelligent driving control, the data reporting speed at 1s intervals is still too slow to reflect the real-time state of the vehicle-end; furthermore, if the data is transmitted at the minimum time interval that meets the vehicle-end data analysis, such as transmitting data at 0.1s intervals, it will lead to a significant increase in the amount of transmitted data, and many data cannot be effectively utilized, which will also increase communication resource consumption and even cause communication blockages.

[0004] In view of this, the present invention is specifically proposed. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a data processing method, apparatus, device, and storage medium, which realizes lightweight data processing and transmission, effectively reduces the amount of processed data, and improves the data analysis and processing efficiency.

[0006] An embodiment of the present invention provides a data processing method, which includes:

[0007] For each first time interval, target parameters within the first time interval are collected in real time to obtain each piece of data to be processed, and based on a preset chunking rule, each piece of data to be processed is chunked and counted to obtain the count value corresponding to each target chunk;

[0008] According to the count value corresponding to each target chunk, the chunk median of each target chunk, and a preset statistical model, each statistical value within the first time interval is determined;

[0009] Based on each statistical value within the first time interval, the vehicle is analyzed and controlled.

[0010] On the basis of the above method, after analyzing and controlling the vehicle based on each statistical value within the first time interval, it further includes:

[0011] Store the count values within each first time interval corresponding to each first time interval in a target storage space;

[0012] According to a second time interval, determine the target time intervals included in the second time interval from each first time interval;

[0013] From the target storage space, determine the count values within each target time interval according to each target time interval;

[0014] Based on the count values within each target time interval, determine the statistical values within the second time interval based on the preset statistical model, and perform long-term analysis and control of the vehicle based on the statistical values within the second time interval;

[0015] Wherein, the time span of the second time interval is a target multiple of the first time interval span, and the target multiple is a positive integer greater than 1.

[0016] Through the above method, the count values of each first time interval are transmitted and stored, and the count values of each first time interval are used for statistical analysis under a preset statistical model, improving the efficiency of long-term analysis and control of the vehicle.

[0017] An embodiment of the present invention provides a data processing device, which includes:

[0018] A block counting module, configured to, for each first time interval, collect target parameters within the first time interval in real time to obtain each data to be processed, and perform block counting on each data to be processed based on a preset block rule to obtain the count values corresponding to each target block;

[0019] A statistical analysis module, configured to determine the statistical values within the first time interval according to the count values corresponding to each target block, the median value of each target block, and a preset statistical model;

[0020] A first analysis and control module, configured to perform analysis and control on the vehicle based on the statistical values within the first time interval.

[0021] An embodiment of the present invention provides an electronic device, which includes:

[0022] A processor and a memory;

[0023] The processor is configured to execute the steps of the data processing method described in any embodiment by calling a program or instruction stored in the memory.

[0024] An embodiment of the present invention provides a computer-readable storage medium, which stores a program or instructions, and the program or instructions cause a computer to execute the steps of the data processing method described in any one of the embodiments.

[0025] The embodiment of the present invention has the following technical effects:

[0026] By collecting the target parameters within each first time interval in real time for each first time interval, obtaining each data to be processed, and performing block counting on each data to be processed based on a preset block division rule to obtain the count values corresponding to each target block, so as to perform block counting on the data to be processed, reduce the data transmission volume and the data processing difficulty. Furthermore, according to the count values corresponding to each target block, the block median values of each target block, and a preset statistical model, each statistical value within the first time interval is determined, so as to convert the statistical model into a preset statistical model for block data analysis for statistical analysis. Based on each statistical value within the first time interval, the vehicle is analyzed and controlled, realizing lightweight data processing and transmission, effectively reducing the amount of data to be processed, and improving the data analysis and processing efficiency. Description of the Drawings

[0027] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 is a flowchart of a data processing method provided by an embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of the count values of each target block, normal distribution fitting, and quadratic curve fitting provided by an embodiment of the present invention;

[0030] Figure 3 is a flowchart of another data processing method provided by an embodiment of the present invention;

[0031] Figure 4 is a schematic diagram of the count values of each target block, normal distribution fitting, and quadratic curve fitting corresponding to continuous low load provided by an embodiment of the present invention;

[0032] Figure 5 is a schematic diagram of the count values of each target block, normal distribution fitting, and quadratic curve fitting corresponding to variable load provided by an embodiment of the present invention;

[0033] Figure 6It is a schematic diagram of the count values, normal distribution fitting, and quadratic curve fitting of each target block corresponding to the continuous high load provided by the embodiment of the present invention;

[0034] Figure 7 It is a schematic diagram of the count values, normal distribution fitting, and quadratic curve fitting of each target block corresponding to the stable medium load provided by the embodiment of the present invention;

[0035] Figure 8 It is a schematic structural diagram of a data processing device provided by the embodiment of the present invention;

[0036] Figure 9 It is a schematic structural diagram of an electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope protected by the present invention.

[0038] The data processing method provided by the embodiment of the present invention is mainly applicable to the situation of transmitting effective and lightweight data volumes in the unit of statistical analysis, while reducing the data processing volume and transmission volume while ensuring the effectiveness of the analysis results. The data processing method provided by the embodiment of the present invention can be executed by a data processing device or an electronic device.

[0039] Figure 1 It is a flowchart of a data processing method provided by the embodiment of the present invention. Refer to Figure 1 , and the data processing method specifically includes:

[0040] S110. For each first time interval, collect the target parameters in real time within the first time interval to obtain each data to be processed, and perform block counting on each data to be processed based on a preset block rule to obtain the count value corresponding to each target block.

[0041] Among them, the first time interval is a time period determined according to the requirements of the target unit, which can be understood as the minimum time interval required for the target unit to perform control applications. Usually, it is between 1 second and 30 minutes. The target unit is a unit based on statistical analysis, rather than a unit for real-time control. The target parameter is a parameter required for the target unit to perform statistical analysis and control. The data to be processed is the data obtained after collecting and processing the target parameter. The preset chunking rule is a preset chunking rule for the target parameter, which can be a uniform chunking rule or a non-uniform chunking rule. Usually, it can include the total number of target chunks, the numerical range of each target chunk. The target chunk is each chunk divided by the preset chunking rule. The count value is the number of times the data to be processed falls into each target chunk.

[0042] It can be understood that the target variable applicable to this data processing method is selected by studying the application mode of the target unit. The actual application corresponding to the selected parameter does not require real-time control of each current data point, but uses some characteristics of the data for calculation. For example, the driving evaluation only understands the general range of the throttle, load, vehicle speed, etc., and at the same time pays attention to the data change in a certain area, rather than using each data point for real-time control. The data transmitted to the application unit with a similar control method can be processed using mathematical statistical laws, that is, the technical solution of this embodiment can be applied.

[0043] Specifically, periodically within each first time interval, for the required target parameter, collect and process to obtain the data to be processed within the first time interval. Then, according to the preset chunking rule, divide it into multiple target chunks. Further, perform chunk counting on the data to be processed based on each target chunk, and the count value corresponding to each target chunk can be obtained by counting.

[0044] S120. Determine the statistical values within the first time interval according to the count values corresponding to each target chunk, the median value of each target chunk, and the preset statistical model.

[0045] Among them, the median value is the average of the upper limit value and the lower limit value of the target chunk. The preset statistical model is a statistical model selected for analyzing the target parameter. For example, it can be a normal distribution model, a quadratic curve distribution model, etc., or various existing statistical models. The statistical value is a calculated value that can effectively describe the preset statistical model.

[0046] Specifically, in combination with the calculation method of each statistical value in the preset statistical model, use the count value corresponding to each target chunk and the median value of each target chunk for calculation, and the statistical values within the first time interval can be obtained.

[0047] It can be understood that by combining the statistical law mathematical model with the control software operation, the original statistical law mathematical model can be converted into a block statistical data model based on counting (preset statistical model), which can effectively reduce the computing requirements and resource occupancy.

[0048] Based on the above example, if the preset statistical model includes a normal distribution model, the statistical values include the block mean and the block variance; therefore, the following method can be used to determine the statistical values within the first time interval according to the count values corresponding to each target block, the block median of each target block, and the preset statistical model:

[0049] Determine the block mean and the block variance within the first time interval through the following formula:

[0050]

[0051] where, is the block mean within the first time interval, σ is the block variance within the first time interval, is the block median of the i-th target block, k i is the count value corresponding to the i-th target block, m is the total amount of data to be processed, and n is the number of blocks of each target block.

[0052] Exemplarily, taking the normal distribution model as an example, during the original data collection, the vehicle-end controller statistically calculates the mean and variance of the collected data, periodically selects a small period of time (the first time interval) of data, and transmits the calculation results. During this process, for example, the vehicle-end controller uses a data sampling method with an interval of 100 ms to perform model statistics on the engine load data, and transmits the statistical results every 3 minutes. By comparing the data transmission volumes of directly transmitted data and data transmitted after block statistics, it can be found that within 3 minutes, the data volume decreases from 1800 floating-point numbers to 2 floating-point numbers, and the data transmission volume drops significantly. However, for the data operations using this calculation method, a large number of floating-point number accumulations and square operations are required, with a large amount of computing work and many floating-point operations. At the same time, since the result data only contains the final statistical result of the normal distribution, it loses all the data detail information of the original data and cannot support extended analysis. Therefore, it is necessary to transform the mathematical method into a model to achieve data lightweighting while reducing the computational amount and retaining sufficient data detail information. By transforming the mathematical model, a statistical model solution based on block distribution (preset statistical model) is achieved. First, based on the actual value range of each different target parameter, its data range is divided into blocks and transformed into a unit that is easier to unify, and within 1 statistical cycle (the first time interval), the count values of the data to be statistically analyzed within each target block are accumulated and recorded. Through this process, the floating-point numbers of the measured values (data to be statistically analyzed) are transformed into count values representing block distribution, that is, in integer form. Then, the transformed data of the current statistical cycle is communicated and transmitted for analysis and processing. Finally, after receiving the data, statistical operations are performed to generate the statistical values within the current cycle. At the same time, the count values of each current target block can be stored to further complete various subsequent extended statistical applications. This algorithm not only retains enough original data information to support further extended analysis, but also significantly reduces the computational workload, and directly converts the data into integers before transmission, facilitating unified storage and subsequent operations in the background. At the same time, the intuitiveness of the background data analysis is high, which is extremely beneficial for the control of function utilization.

[0053] Exemplarily, compared with the data transmission volume of the same case in the above example, taking the number of target blocks as 10 for illustration, the statistical model solution based on block distribution (preset statistical model) will increase the data volume to 10 integers every 3 minutes for transmitting the count of block weight distribution. Although the data transmission volume increases slightly compared to 2, it contains the true weight distribution of the data segments corresponding to each target block. During subsequent data processing, the block weight counts of each data segment can be accumulated to obtain the statistical results of various extended time periods, significantly increasing the accuracy of the data model and supporting subsequent extended analysis.

[0054] Based on the above example, if the preset statistical model includes a quadratic curve distribution model, the statistical value includes the square value of the trend line radius; therefore, the following method can be used to determine the statistical values within the first time interval according to the count values corresponding to each target block, the block median of each target block, and the preset statistical model:

[0055] Construct a fitting trend line according to the count values corresponding to each target block and the block median of each target block, and determine the fitting value corresponding to each target block according to the block median of each target block and the fitting trend line;

[0056] Determine the block mean within the first time interval according to the count values corresponding to each target block, the block median of each target block, and the total amount of data to be processed;

[0057] Determine the square value of the trend line radius within the first time interval through the following formula:

[0058]

[0059] where, R 2 is the square value of the trend line radius within the first time interval, n is the number of divided blocks of each target block, f i is the fitting value corresponding to the i-th target block, k i is the count value corresponding to the i-th target block, is the block mean within the first time interval.

[0060] Among them, the fitting trend line is a trend line obtained by performing quadratic curve fitting with the block median of each target block as the independent variable and the count value corresponding to each target block as the dependent variable. The fitting value is the value obtained by substituting the block median of each target block into the fitting trend line. The calculation method of the block mean is the same as that in the above example and will not be elaborated here.

[0061] Figure 2 is a schematic diagram of the count values of each target block, normal distribution fitting, and quadratic curve fitting.

[0062] It can be understood that the count values of each target block can be transmitted according to requirements for analysis and calculation to obtain each statistical value for analysis and control, greatly reducing the data transmission volume and the calculation amount of statistical value calculation.

[0063] S130. Analyze and control the vehicle based on the statistical values within the first time interval.

[0064] Specifically, using the statistical values within the first time interval can replace the statistical values in the original mathematical model solution to perform periodic analysis and control on the vehicle, while reducing the data transmission volume and processing amount and ensuring the control effect.

[0065] It can be understood that between S110 and S120 and / or between S120 and S130, if different steps are implemented by different modules or units, when data is transmitted between the modules or units, the transmitted data is the count value and / or the statistical value, which can effectively reduce the amount of data transmission.

[0066] The present invention has the following technical effects: for each first time interval, the target parameters within the first time interval are collected in real time to obtain the data to be processed, and based on the preset chunking rule, the data to be processed is chunked and counted to obtain the count value corresponding to each target chunk, so as to perform chunking and counting of the data to be processed, reduce the amount of data transmission and the difficulty of data processing. Furthermore, according to the count value corresponding to each target chunk, the median value of each target chunk, and the preset statistical model, each statistical value within the first time interval is determined, so as to convert the statistical model into a preset statistical model for chunk data analysis for statistical analysis. Based on each statistical value within the first time interval, the vehicle is analyzed and controlled, realizing lightweight data processing and transmission, effectively reducing the amount of processed data, and improving the efficiency of data analysis and processing.

[0067] Figure 3 It is a flowchart of another data processing method provided by an embodiment of the present invention. Refer to Figure 3 , and the data processing method specifically includes:

[0068] S210. For each first time interval, the target parameters within the first time interval are collected in real time to obtain the collected data.

[0069] Among them, the collected data is the data collected in real time and has not been processed.

[0070] Specifically, based on sensors, etc., within each first time interval, the data of the target parameters is collected in real time to obtain the collected data.

[0071] S220. For each collected data, according to the valid value range of the target parameter, judge the validity of the collected data; the collected data with valid validity within the first time interval is used as the data to be processed.

[0072] Among them, the valid value range is the data value range that the target parameter can use in subsequent applications. Validity is a description used to describe whether the collected data can be used subsequently, and can include valid and invalid.

[0073] Specifically, for each collected data, judge whether the collected data is within the valid value range of the target parameter. If so, determine that the validity of the collected data is valid; otherwise, determine that the validity of the collected data is invalid. Furthermore, in order to ensure the accuracy of subsequent data processing, the collected data with valid validity within the first time interval is used as the data to be processed.

[0074] S230. Determine the block data range of each target block according to the preset block division rule and the valid value range of the target parameter, and initialize the count value of each target block.

[0075] Among them, the block data range is the data range included in each target block. It can be understood that the first and last positions of the block data ranges of each target block are connected to form the valid value range.

[0076] Specifically, through the preset block division rule, the valid value range of the target parameter is divided into multiple target blocks, and the block data range corresponding to each target block is obtained. Moreover, the count value of each target block is initialized, that is, cleared.

[0077] Based on the above example, the block data range of each target block can be determined according to the preset block division rule and the valid value range of the target parameter in the following way:

[0078] Determine the upper limit value of the parameter working value and the lower limit value of the parameter working value according to the valid value range of the target parameter;

[0079] For each target block, determine the upper limit value of the block and the lower limit value of the block according to the number of divided blocks, the position serial number of the target block, the upper limit value of the parameter working value, and the lower limit value of the parameter working value, and determine the block data range of the target block according to the upper limit value of the block and the lower limit value of the block.

[0080] Among them, the number of divided blocks is the total number of divided blocks preset in the preset block division rule. The upper limit value of the parameter working value and the lower limit value of the parameter working value are the right boundary and the left boundary of the valid value range. The upper limit value of the block and the lower limit value of the block are the right boundary and the left boundary of the block data range. The position serial number is used to describe the sorting position of the target block sorted in ascending order according to the data size covered.

[0081] Specifically, take the right boundary of the valid value range of the target parameter as the upper limit value of the parameter working value, and take the left boundary of the valid value range of the target parameter as the lower limit value of the parameter working value. For each target block, calculate according to the number of divided blocks, the position serial number of the target block, the upper limit value of the parameter working value, and the lower limit value of the parameter working value using the calculation method of uniform block division to obtain the upper limit value of the block and the lower limit value of the block of each target block. Determine the range between each group of upper limit values of the block and lower limit values of the block as the block data range of each target block.

[0082] It can be understood that the requirement of the preset block division rule is to divide each target parameter into multiple interval blocks (target blocks) within its range (valid value range), so that the data to be processed of each target parameter can be labeled with target blocks. If uniform block division is adopted, the interval (block data range) expression of each target block is shown in the following formula:

[0083]

[0084] Among them, q i is the i-th target block, represented in the format of an upper and lower interval range. q max and q min are respectively the upper limit value and the lower limit value of the parameter working range of the target parameter. i is the position serial number of the target block, and n is the number of divided blocks of each target block.

[0085] Of course, according to the characteristics of the target parameter, uneven block division can also be performed as the preset block division rule.

[0086] S240. For each data to be processed, use the block data range corresponding to the data to be processed as the target range, and increment the count value of the target block corresponding to the target range by one.

[0087] Among them, the target range is the block data range where the data to be processed falls.

[0088] Specifically, for each data to be processed, determine which block data range the data to be processed falls into, use this block data range as the target range, and count the count value of the target block corresponding to the target range, that is, perform an increment operation.

[0089] S250. Determine each statistical value within the first time interval according to the count value corresponding to each target block, the block median value of each target block, and the preset statistical model.

[0090] S260. Based on each statistical value within the first time interval, perform analysis and control on the vehicle.

[0091] Exemplarily, in the application of a hybrid vehicle, since the engine operating time accounts for a low proportion, the engine particulate trap may experience non-regeneration until the particulate trap is blocked when continuously operating under special working conditions. The main working conditions for the above phenomena include continuous low-temperature cold start operation, extremely high and extremely low load operations, rapid alternating load and other working conditions. Although the possibility of these working conditions continuously occurring during normal use is very low, once they occur, they will cause the particulate trap to be blocked and induce serious complaints such as engine torque limitation or breakdown. Therefore, it is very important for the vehicle control system to perform statistical judgment on the operating conditions and driving habits for the state prediction of the particulate trap and the early warning of regeneration failure. Since many data and parameters for judging the working conditions change at high speed and the real-time data volume is very large, but not every collected data needs to be used in actual applications, so it has practical significance to apply data lightweighting to it, and the technical solution of this example can be applied.

[0092] By selecting appropriate vehicle control system control parameters (target parameters) as the analysis input of driving behavior and working conditions, the data to be processed is counted in blocks, then used and transmitted, thereby supporting the vehicle control application to predict and give early warnings about the status and regeneration of the engine particulate trap.

[0093] First, the target parameters that have a greater impact on the particulate trap function are listed and classified. For the data such as a sharp increase in particulate matter caused by too fast a rate of increase in engine load, or the inability to perform particulate matter model detection due to too low or rapidly changing engine load, the computing accuracy requirements need to reach the millisecond level.

[0094] Table 1 Target parameters affecting the particulate trap function

[0095]

[0096] Secondly, as shown in Table 2, 0.1 s is selected as the period for data reading and operation. According to the agreed uniform block division method, the data to be processed is judged and divided into blocks, and the count is put into the corresponding target blocks. The block count will be obtained every 3 minutes for each target block. It is transmitted once every 3 minutes, and the total number of valid data points counted (the data volume of the data to be processed) is 1800. In the operation, the block median values of 10 target blocks are defined as 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 respectively, further reducing the amount of calculation. Among them, the load / throttle change rate is expressed by the load difference in 0.1 s.

[0097] Table 2 Block setting of target parameters

[0098]

[0099]

[0100] Subsequently, the real-time normal distribution parameter calculation is carried out on the count values of each target block, and the results are used for real-time judgment. Figures 4 - 7 Shows the count values of each target block corresponding to the load, the normal distribution fitting and the quadratic curve fitting, which are the analysis schematic diagrams of continuous low load, variable load, continuous high load, and stable medium load in turn. Combining with the load change rate, the change characteristics of the load can be further confirmed.

[0101] Finally, combining the cross-statistical results of each target parameter, the working condition judgment conditions are designed. For example: if the particulate trap is already close to high load and continuously operates under working conditions that are not conducive to regeneration such as too low load, rapidly alternating load, and continuously too high load, targeted reminders will be given at the user end and the background to ensure the safety of the vehicle and the user.

[0102] Examples of Judgment Conditions for Statistical Results in Table 3

[0103]

[0104] Through the transmission method of block counting by adopting a preset block division rule and statistical analysis based on a preset statistical model, after processing the data to be processed of applicable high-speed signals, the data transmission volume is greatly reduced, achieving the purpose of efficient data transmission, storage, and utilization.

[0105] S270. Store each count value within each first time interval and the corresponding first time interval in the target storage space.

[0106] S280. According to the second time interval, determine each target time interval included in the second time interval from each first time interval; from the target storage space, determine each count value within each target time interval according to each target time interval.

[0107] S290. Based on each count value within each target time interval and a preset statistical model, determine each statistical value within the second time interval, and based on each statistical value within the second time interval, perform long-term analysis and control on the vehicle.

[0108] Among them, the target storage space is a storage space for storing the basic data and count values of the target blocks required for long-term analysis and control. The basic data can be understood as the block upper limit value, block lower limit value, block median value, etc. The second time interval is the time interval required for long-term analysis and control. The time span of the second time interval is a target multiple of the first time interval span, and the target multiple is a positive integer greater than 1. The target time interval is at least two first time intervals covered and included within the second time interval.

[0109] Specifically, store each count value within each first time interval and the corresponding first time interval in the target storage space. According to the required second time interval, determine each first time interval included in the second time interval from each first time interval as each target time interval. Further, from the target storage space, find each count value within each target time interval stored in advance. Add up each count value within each target time interval according to the corresponding target block to obtain the count value corresponding to each target block within the second time interval. Based on a preset statistical model, perform statistical analysis on the count value corresponding to each target block within the second time interval to obtain each statistical value within the second time interval. Finally, long-term analysis and control of the vehicle can be performed through each statistical value within the second time interval.

[0110] It is understandable that data storage is required for data recording or long-term statistical data analysis and control. Therefore, the data of the current period (the first time interval) needs to be stored for subsequent use. Long-term analysis and control refer to the corresponding response to the comprehensive performance of a period of historical data (data within the second time interval). After storing the most basic block count value sequence (the count values of each target block within each first time interval), a new block count value sequence (the count values of each target block within the second time interval) can be generated by simply adding multiple basic block count sequences, and the same mathematical model (preset statistical model) can be used for statistical analysis to support the data requirements of longer-term analysis and control.

[0111] Continuing the analysis with the above example, a set of data is generated for transmission every 3 minutes. If long-term analysis and control require longer-term data statistics, such as based on the physical characteristics of the particulate filter, even when the loading is not high, if it works under harsh conditions for about 30 minutes continuously, it will lead to inaccurate estimation of the loading and may cause overloading or blockage. Based on this physical characteristic, the statistical data of the historical block count values of 8 three-minute intervals (the second time interval) can be extracted. By simply adding the count values of each target block, new count values of each target block within the historical 24-minute period, the current regeneration cycle, and the current non-regeneration cycle can be generated. This data (the new count values of each target block) can be used to calculate the target statistical value again using a preset statistical model (such as the normal distribution model), achieving the purpose of real-time statistical analysis of the characteristics of long-term working conditions. At the same time, based on the generated statistical results of the working condition status, warnings and adjustments to the operating conditions can be made through the vehicle control application interface to avoid problems.

[0112] It is understandable that the balance between information data lightweight and data detail retention is explained by using the normal distribution model in the above examples. Through the mathematical transformation of the normal distribution model, a regular analysis model of block count statistics is established, further reducing the computing load of the controller. Other types of preset statistical models can refer to the analysis method of the normal distribution model for data processing, which will not be elaborated here.

[0113] Through the data statistics method for applicable target parameters in the above examples, very little communication data volume can be used to obtain effective features of vehicle control data, achieving the purpose of high-speed data lightweighting. Moreover, by adopting the block counting statistics scheme, the data volume can be significantly reduced, and at the same time, the true weight distribution (counting distribution) of the data segments where the current target blocks are located is retained, which can support subsequent extended analysis and keep the data model with sufficient accuracy. Through the data transmission method after counting statistics, the data has the significance of real-time application requirements, can be very conveniently used for control and visual inspection, and reduces the workload of data download and subsequent analysis. Furthermore, for the data transmitted after counting statistics, its statistical results can participate in the analysis and control of the current state in real time, and can also be used for long-term feature analysis and application support of subsequent data through storage and accumulation.

[0114] The present invention has the following technical effects: By collecting the target parameters in each first time interval in real time for each first time interval, obtaining each piece of collected data, and for each piece of collected data, judging the validity of the collected data according to the effective value range of the target parameter, and taking the collected data with valid validity in each first time interval as each piece of data to be processed, so as to ensure the validity of the data to be processed used subsequently, improve the accuracy of subsequent analysis and control. Furthermore, by determining the block data range of each target block according to the preset block division rule and the effective value range of the target parameter, and initializing the count value of each target block, for each piece of data to be processed, taking the block data range corresponding to the data to be processed as the target range, and adding 1 to the count value of the target block corresponding to the target range, so as to improve the processing efficiency of data block division, and achieve the effect of reducing the processing volume, transmission volume and storage volume of data while improving data utilization rate.

[0115] Figure 8 It is a schematic structural diagram of a data processing device provided by an embodiment of the present invention. As Figure 8 shown, the device includes: a block counting module 310, a statistical analysis module 320, and a first analysis and control module 330.

[0116] Among them, the block counting module 310 is used to collect the target parameters in the first time interval in real time for each first time interval, obtain each piece of data to be processed, and perform block counting on each piece of data to be processed based on the preset block division rule to obtain the count value corresponding to each target block; the statistical analysis module 320 is used to determine each statistical value in the first time interval according to the count value corresponding to each target block, the block median of each target block, and the preset statistical model; the first analysis and control module 330 is used to perform analysis and control on the vehicle based on each statistical value in the first time interval.

[0117] Based on the above example, optionally, the block counting module 310 is further configured to determine the block data range of each target block according to a preset block rule and the valid value range of the target parameter, and initialize the count value of each target block; for each data to be processed, use the block data range corresponding to the data to be processed as the target range, and increment the count value of the target block corresponding to the target range by one.

[0118] Based on the above example, optionally, the block counting module 310 is further configured to determine the upper limit value of the parameter working value and the lower limit value of the parameter working value according to the valid value range of the target parameter; for each target block, determine the upper limit value of the block and the lower limit value of the block of the target block according to the number of blocks, the position serial number of the target block, the upper limit value of the parameter working value, and the lower limit value of the parameter working value, and determine the block data range of the target block according to the upper limit value of the block and the lower limit value of the block.

[0119] Based on the above example, optionally, the preset statistical model includes a normal distribution model, and the statistical values include the block mean and the block variance; the statistical analysis module 320 is further configured to determine the block mean and the block variance within the first time interval through the following formula:

[0120]

[0121] Wherein, is the block mean within the first time interval, σ is the block variance within the first time interval, is the block median of the i-th target block, k i is the count value corresponding to the i-th target block, m is the total amount of data of each data to be processed, and n is the number of blocks of each target block.

[0122] Based on the above example, optionally, the preset statistical model includes a quadratic curve distribution model, and the statistical value includes the square value of the radius of the trend line; the statistical analysis module 320 is further configured to construct a fitting trend line according to the count value corresponding to each target block and the block median of each target block, and determine the fitting value corresponding to each target block according to the block median of each target block and the fitting trend line;

[0123] Determine the block mean within the first time interval according to the count value corresponding to each target block, the block median of each target block, and the total amount of data of each data to be processed;

[0124] Determine the square value of the radius of the trend line within the first time interval through the following formula:

[0125]

[0126] Wherein, R 2is the square value of the trend line radius within the first time interval, n is the number of divided blocks of each target block, f i is the fitting value corresponding to the i-th target block, k i is the count value corresponding to the i-th target block, is the block mean value within the first time interval.

[0127] Based on the above example, optionally, after analyzing and controlling the vehicle based on the statistical values within the first time interval, it further includes: a second analysis and control module, configured to store the count values within each first time interval corresponding to each first time interval into a target storage space; determine, according to the second time interval, each target time interval included in the second time interval from each first time interval; determine, from the target storage space, the count values within each target time interval according to each target time interval; determine the statistical values within the second time interval based on the preset statistical model according to the count values within each target time interval, and perform long-term analysis and control on the vehicle based on the statistical values within the second time interval; wherein, the time span of the second time interval is a target multiple of the time span of the first time interval, and the target multiple is a positive integer greater than 1.

[0128] It can be understood that both the first analysis and control module 330 and the second analysis and control module are used for analysis and control. Described by function, the above two modules can be implemented in the same or different software and / or hardware.

[0129] Based on the above example, optionally, the block counting module 310 is further configured to collect the target parameters in real time within the first time interval to obtain each collected data; for each collected data, determine the validity of the collected data according to the effective value range of the target parameter; and use the collected data with valid validity within the first time interval as each data to be processed.

[0130] For the convenience of description, when describing the above device, it is divided into various modules according to functions for separate description. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0131] The device in the above embodiment is used to implement the corresponding data processing method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0132] Figure 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 9 shown, the electronic device 400 includes one or more processors 401 and a memory 402.

[0133] The processor 401 can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device 400 to perform desired functions.

[0134] The memory 402 can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage media, and the processor 401 can run the program instructions to implement the calibration method of the in-vehicle BSD camera in any embodiment of the present invention described above and / or other desired functions. Various contents such as initial external parameters, thresholds, etc. can also be stored in the computer-readable storage media.

[0135] In one example, the electronic device 400 can further include: an input device 403 and an output device 404, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown). The input device 403 can include, for example, a keyboard, a mouse, etc. The output device 404 can output various information to the outside, including warning prompt information, braking force, etc. The output device 404 can include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0136] Of course, for simplicity, Figure 9 only some of the components related to the present invention in the electronic device 400 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 400 can further include any other appropriate components.

[0137] In addition to the above methods and devices, an embodiment of the present invention can also be a computer program product, which includes computer program instructions that, when run by a processor, cause the processor to execute the steps of the calibration method of the in-vehicle BSD camera provided in any embodiment of the present invention.

[0138] The computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present invention. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0139] In addition, an embodiment of the present invention may also be a computer-readable storage medium storing computer program instructions, which, when run by a processor, cause the processor to execute the steps of the calibration method of the in-vehicle BSD camera provided by any embodiment of the present invention.

[0140] The computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but not be limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0141] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or also includes elements inherent to such process, method, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, or device comprising the element.

[0142] It should also be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. Unless otherwise clearly specified and defined, terms such as "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method, characterized in that: include: For each first time interval, the target parameter within the first time interval is collected in real time to obtain each data to be processed, and based on a preset block rule, each data to be processed is counted in blocks to obtain a count value corresponding to each target block; Determine each statistical value within the first time interval according to the count value corresponding to each target block, the block median value of each target block and a preset statistical model; The vehicle is analyzed and controlled based on the statistical values ​​within the first time interval.

2. The method according to claim 1, characterized in that The method of counting blocks of each data to be processed based on a preset block rule to obtain a count value corresponding to each target block includes: According to the preset block division rule and the valid value range of the target parameter, the block data range of each target block is determined, and the count value of each target block is initialized; For each data to be processed, a block data range corresponding to the data to be processed is used as a target range, and a count value of a target block corresponding to the target range is increased by one.

3. The method according to claim 2, characterized in that The step of determining the block data range of each target block according to the preset block division rule and the valid value range of the target parameter includes: Determine the working upper limit value and the working lower limit value of the parameter according to the effective value range of the target parameter; For each target block, the block upper limit value and the block lower limit value of the target block are determined according to the number of blocks, the position serial number of the target block, the parameter working upper limit value and the parameter working lower limit value, and the block data range of the target block is determined according to the block upper limit value and the block lower limit value.

4. The method according to claim 1, characterized in that: The preset statistical model includes a normal distribution model, and the statistical value includes a block mean and a block variance; The determining of each statistical value within the first time interval according to the count value corresponding to each target block, the block median value of each target block and a preset statistical model includes: The block mean and block variance within the first time interval are determined by the following formula: in, is the block mean in the first time interval, σ is the block variance in the first time interval, is the block median of the i-th target block, k i is the count value corresponding to the i-th target block, m is the total amount of data to be processed, and n is the number of blocks of each target block.

5. The method according to claim 1, characterized in that The preset statistical model includes a quadratic curve distribution model, and the statistical value includes a square value of a trend line radius; The determining of each statistical value within the first time interval according to the count value corresponding to each target block, the block median value of each target block and a preset statistical model includes: Constructing a fitting trend line according to the count value corresponding to each target block and the block median of each target block, and determining the fitting value corresponding to each target block according to the block median of each target block and the fitting trend line; Determine the block mean value within the first time interval according to the count value corresponding to each target block, the block median value of each target block and the total amount of data to be processed; The square value of the radius of the trend line in the first time interval is determined by the following formula: Among them, R 2 is the square value of the radius of the trend line in the first time interval, n is the number of blocks of each target block, and f i is the fitting value corresponding to the i-th target block, k i is the count value corresponding to the i-th target block, is the block mean in the first time interval.

6. The method according to claim 1, characterized in that After analyzing and controlling the vehicle based on the statistical values ​​within the first time interval, the method further includes: storing each count value within each first time interval in correspondence with each first time interval in the target storage space; According to the second time interval, determining each target time interval included in the second time interval from each first time interval; Determining, from the target storage space, each count value within each target time interval according to each target time interval; According to each count value in each target time interval, based on the preset statistical model, each statistical value in the second time interval is determined, and based on each statistical value in the second time interval, long-term analysis control is performed on the vehicle; The time span of the second time interval is a target multiple of the time span of the first time interval, and the target multiple is a positive integer greater than 1.

7. The method according to claim 1, characterized in that The real-time acquisition of the target parameter within the first time interval to obtain various data to be processed includes: Collecting target parameters within the first time interval in real time to obtain various collected data; For each piece of collected data, judging the validity of the collected data according to the valid value range of the target parameter; The collected data with valid validity within the first time interval are used as the data to be processed.

8. A data processing device, characterized in that: include: A block counting module is used to collect the target parameters within each first time interval in real time to obtain each data to be processed, and count the blocks of each data to be processed based on a preset block rule to obtain a count value corresponding to each target block; A statistical analysis module, used to determine each statistical value within the first time interval according to the count value corresponding to each target block, the block median value of each target block and a preset statistical model; The first analysis and control module is used to analyze and control the vehicle based on the statistical values ​​within the first time interval.

9. An electronic device, characterized in that: The electronic device comprises: Processor and memory; The processor is used to execute the steps of the data processing method according to any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, wherein the program or the instruction enables a computer to execute the steps of the data processing method according to any one of claims 1 to 7.

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