Distributed processing system of big data on cloud platform

Abstract

The invention discloses a distributed processing system for big data on a cloud platform, which comprises an information collection module, a cleaning analysis processing module and a distributed processing control module, and is characterized in that the information collection module is used for collecting the vehicle condition of a current washed vehicle and the data related to the vehicle use condition; the cleaning analysis processing module is used for analyzing the cleaning condition of the dirty position of a vehicle in the vehicle body cleaning process, the distributed processing control module is used for controlling the water pressure of a cleaning spraying head in a distributed mode according to the cleaning analysis processing result, and the information collection module is electrically connected with the cleaning analysis processing module. The cleaning analysis processing module is electrically connected with the distributed processing control module, and the system has the advantages of being high in processing efficiency and high in practicability.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a distributed processing system for big data on a cloud platform. Background Art

[0002] Now is an era when family cars are owned in large numbers, and car washing has become an indispensable industry in every city. With the development of the Internet of Things, car washing has gradually entered a fully automatic mode. Among them, the contactless fully automatic car washing machine is very popular in the market because of its 360-degree continuous rotation, high cleaning efficiency, and no damage to the car paint.

[0003] Existing contactless fully automatic car washers need to have sufficient water pressure to achieve high cleanliness without wiping. However, several groups of high-pressure spray heads need to be arranged on the spray rod at the same time to wash the car body in all directions at the same time. Because of its multiple water outlets and high water pressure requirements, the total water pressure needs to be quite high. When facing stubborn stains on the car body, its water pressure is difficult to meet the flushing requirements of local stubborn stains, resulting in poor car washing effect and requiring manual secondary cleaning. Therefore, it is very necessary to design a distributed processing system for big data on a cloud platform with high processing efficiency and strong practicality. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed processing system for big data on a cloud platform to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a distributed processing system of big data on a cloud platform, comprising an information collection module, a cleaning analysis and processing module and a distributed processing control module, wherein the information collection module is used to collect data related to the condition and usage of the current car wash vehicle, the cleaning analysis and processing module is used to analyze the cleaning status of dirty positions of the vehicle during the cleaning process, the distributed processing control module is used to distribute control the water pressure of the cleaning spray head according to the cleaning analysis and processing results, the information collection module is electrically connected to the cleaning analysis and processing module, and the cleaning analysis and processing module is electrically connected to the distributed processing control module.

[0006] According to the above technical solution, the information collection module includes a cleaning record database, a vehicle environment classification module and a monitoring and acquisition module. The cleaning record database is used to identify the historical vehicle cleaning data stored in the current vehicle, the vehicle environment classification module is used to collect the main vehicle usage environment of the registered owner, and the monitoring and acquisition module is used to synchronously collect and shoot monitoring images during the automatic spray cleaning process.

[0007] According to the above technical scheme, the cleaning analysis and processing module includes a position calibration module, a special area marking module, a cleaning mode analysis and judgment module and a monitoring screen analysis module. The position calibration module is used to calibrate the position of the vehicle in the monitoring collection screen with the position of each sprinkler head flushing point. The special area marking module is used to mark the special area of ​​the monitoring screen after calibration according to the historical cleaning data. The cleaning mode analysis and judgment module is used to analyze and judge the mode selection for global cleaning of the vehicle. The monitoring screen analysis module is used to analyze the specific monitoring screen during the cleaning process.

[0008] According to the above technical solution, the distributed processing control module includes a cleaning mode selection module and a nozzle pressurizing module. The cleaning mode selection module is used to control the selection of the automatic car washing cleaning mode, and the nozzle pressurizing module is used to control the pressurization of the sprinkler head in real time according to the analysis and processing signal.

[0009] According to the above technical solution, the monitoring screen analysis module further includes an image grayscale processing submodule, a feature area signal feedback submodule and a judgment submodule. The image grayscale processing submodule is used to grayscale the monitoring screen image and output the grayscale value in real time. The feature area signal feedback submodule is used to obtain feature area screen signal feedback and analyze the change status of the feature area. The judgment submodule is used to identify and judge the feature area based on the analysis results.

[0010] According to the above technical solution, the operation method of the distributed processing system includes the following steps:

[0011] Step S1: When the vehicle is parked and the fully automatic car wash machine is started, the information collection module starts to collect and retrieve the historical car wash data, vehicle use environment and vehicle images of the vehicle;

[0012] Step S2: The cleaning analysis processing module starts to read the system acquisition data;

[0013] Step S3: Processing the collected data, analyzing and determining the cleaning mode and the monitoring screen during the cleaning process;

[0014] Step S4: the distributed processing control module selects the car washing mode of the full-automatic car washing machine in real time according to the analysis and judgment results and pressurizes the spray heads that need to be pressurized locally.

[0015] According to the above technical solution, step S1 further includes the following steps:

[0016] Step S11: by identifying the license plate information, calling the cleaning record database of the vehicle in the system, and obtaining the last recorded cleaning time, historical cleaning mode selection and the location of the historical marking area in the vehicle body through the cleaning record database;

[0017] Step S12: Then, the main vehicle use environment category manually selected by the user is obtained through the vehicle use environment classification module, wherein the main vehicle use environment categories include: urban road commuting, national highway / highway use, and township / complex environment use;

[0018] Step S13: Finally, the monitoring and acquisition module starts to run and collects the overall and detailed images.

[0019] According to the above technical solution, step S3 further includes the following steps:

[0020] Step S31: When the vehicle starts to be fully automatically cleaned, the top, bottom and middle points on the center line of the vehicle body are calibrated through the position calibration module, and then the mobile device is controlled so that the top, bottom and middle points in the global image coincide with the calibrated positions;

[0021] Step S32: obtaining the position of the historical marking area in the vehicle body in the information collection module, and then marking the position of the historical marking area in the vehicle body and the corresponding position of the monitoring screen according to the calibrated position of the current vehicle body and the monitoring screen;

[0022] Step S33: The cleaning mode analysis and judgment module is started, and the data of the information collection module is retrieved. According to the analysis, the judgment results of the first-level cleaning mode, the second-level cleaning mode and the third-level cleaning mode are respectively given, wherein the water pressure increases step by step;

[0023] Step S34: Finally, according to the judgment result, the electrical signal is output to the distributed processing control module, and the distributed processing control module selects the global cleaning mode through the cleaning mode selection module, and starts the fully automatic car washing machine to start cleaning according to the selected global cleaning mode;

[0024] Step S35: The monitoring image analysis module analyzes the images during cleaning in real time, and also transmits the analysis result electric signal to the distributed processing control module, and the distributed processing control module dynamically adjusts the corresponding nozzles through the nozzle pressurizing module to increase the spraying water pressure;

[0025] Among them, in step S34, when the selected global cleaning mode is the three-level cleaning mode, the distributed processing control module controls all the spray heads to open at intervals and performs an additional round of flushing. The second round complementarily opens only the spray heads that were not opened in the first round.

[0026] According to the above technical solution, step S33 further includes the following steps:

[0027] Step S331: Obtain historical cleaning modes, and select the cleaning mode with the most historical cleaning modes used as the preliminary analysis result output;

[0028] Step S332: The system sets cleaning intervals for the primary cleaning mode, the secondary cleaning mode, and the tertiary cleaning mode, which correspond to (0, T), [T, 2T], and (2T, +∞), and then obtains the current cleaning interval t according to the last cleaning time recorded in the cleaning record database, and compares it with the cleaning interval set by the system. After determining that the interval falls into the corresponding cleaning mode, the current corresponding cleaning mode is output as the secondary analysis result;

[0029] Step S333: Finally, the vehicle use environment category currently selected by the user is retrieved. When the vehicle use environment is urban road commuting, it is analyzed and determined as the first-level cleaning mode. When the vehicle use environment is national highway / highway use, it is analyzed and determined as the second-level cleaning mode. When the vehicle use environment is rural / complex environment use, it is determined as the third-level cleaning mode.

[0030] Step S334: In steps S331-S333, the cleaning mode with the highest level among the analysis output results is selected as the total analysis and judgment result output.

[0031] According to the above technical solution, step S35 further includes the following steps:

[0032] Step S351: grayscale processing is performed on the detail image being washed, and the grayscale value is output in real time;

[0033] Step S352: After the local flushing is completed, when the error between the identified gray value and the gray value of the historical data of the vehicle body is greater than a preset number r, the identified area is captured as a feature area, wherein when the feature area and the historical marking area coincide with each other in the vehicle body, the determination of the feature area is cancelled;

[0034] Step S353: For the characteristic area, the electrical signal controls the nozzle pressurizing module to pressurize and spray the nozzle corresponding to the position of the characteristic area after position calibration;

[0035] Step S354: During the local pressurized flushing, the characteristic area image analysis is repeated. When the area of ​​the characteristic area signal feedback continues to get smaller, the pressurized flushing spray is continued. When the area of ​​the characteristic area signal feedback does not change after the pressurized flushing time threshold, the judgment submodule determines that the area is a new marked area and stores it in the cleaning record database.

[0036] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention can determine the whole vehicle washing water pressure demand through big data analysis during the fully automated washing process, and when the whole vehicle washing water pressure demand is met, further monitor the washing conditions of the vehicle body details, and determine whether it is necessary to carry out targeted pressure washing of local stubborn stains on the vehicle body based on the monitoring and analysis results, ultimately realizing distributed analysis and processing of the car washing process and controlling the car washing program, achieving the effect of efficient cleaning and satisfying the deep cleaning water pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 The present invention provides a technical solution: a distributed processing system of big data on a cloud platform, including an information collection module, a cleaning analysis processing module and a distributed processing control module. The information collection module is used to collect data related to the vehicle condition and vehicle usage of the current car wash. The cleaning analysis processing module is used to analyze the cleaning status of the dirty positions of the vehicle during the process of cleaning the car body. The distributed processing control module is used to control the water pressure of the cleaning spray head in a distributed manner according to the cleaning analysis processing results. The information collection module is electrically connected to the cleaning analysis processing module, and the cleaning analysis processing module is electrically connected to the distributed processing control module.

[0041] The information collection module includes a cleaning record database, a vehicle environment classification module and a monitoring and acquisition module. The cleaning record database is used to identify the historical vehicle cleaning data stored in the current vehicle. The vehicle environment classification module is used to collect the main vehicle usage environment of the registered owner. The monitoring and acquisition module is used to synchronously collect and shoot monitoring images during the automatic spray cleaning process.

[0042] The cleaning analysis and processing module includes a position calibration module, a special area marking module, a cleaning mode analysis and judgment module and a monitoring screen analysis module. The position calibration module is used to calibrate the position of the vehicle in the monitoring collection screen with the position of each sprinkler head flushing point. The special area marking module is used to mark the special area of ​​the monitoring screen after calibration according to the historical cleaning data. The cleaning mode analysis and judgment module is used to analyze and judge the mode selection for the global cleaning of the vehicle. The monitoring screen analysis module is used to analyze the specific monitoring screen during the cleaning process.

[0043] The distributed processing control module includes a cleaning mode selection module and a nozzle pressurizing module. The cleaning mode selection module is used to control the selection of the automatic car washing cleaning mode, and the nozzle pressurizing module is used to control the pressurization of the sprinkler head in real time according to the analysis and processing signal.

[0044] The monitoring screen analysis module further includes an image grayscale processing submodule, a feature area signal feedback submodule and a judgment submodule. The image grayscale processing submodule is used to grayscale the monitoring screen image and output the grayscale value in real time. The feature area signal feedback submodule is used to obtain feature area screen signal feedback and analyze the change status of the feature area. The judgment submodule is used to identify and judge the feature area based on the analysis results.

[0045] The method for operating the distributed processing system includes the following steps:

[0046] Step S1: When the vehicle is parked and the fully automatic car wash machine is started, the information collection module starts to collect and retrieve the historical car wash data, vehicle use environment and vehicle images of the vehicle;

[0047] Step S2: The cleaning analysis processing module starts to read the system acquisition data;

[0048] Step S3: Processing the collected data, analyzing and determining the cleaning mode and the monitoring screen during the cleaning process;

[0049] Step S4: the distributed processing control module selects the car washing mode of the full-automatic car washing machine in real time according to the analysis and judgment results and pressurizes the spray heads that need to be pressurized locally.

[0050] Step S1 further comprises the following steps:

[0051] Step S11: by identifying the license plate information, calling the cleaning record database of the vehicle in the system, and obtaining the last recorded cleaning time, historical cleaning mode selection and the location of the historical marking area in the vehicle body through the cleaning record database;

[0052] Step S12: Then, the main vehicle use environment category manually selected by the user is obtained through the vehicle use environment classification module, wherein the main vehicle use environment categories include: urban road commuting, national highway / highway use, and township / complex environment use;

[0053] Step S13: Finally, the monitoring and acquisition module starts to run and collects the overall and detailed images.

[0054] Step S3 further comprises the following steps:

[0055] Step S31: When the vehicle starts to be fully automatically cleaned, the top, bottom and middle points on the center line of the vehicle body are calibrated through the position calibration module, and then the mobile device is controlled to make the top, bottom and middle points in the global image coincide with the calibration positions; thereby laying the foundation for distributed analysis and control of the pressurized spraying of the sprinkler head;

[0056] Step S32: obtaining the position of the historical marking area in the vehicle body in the information collection module, and then marking the position of the historical marking area in the vehicle body and the corresponding position of the monitoring screen according to the calibrated position of the current vehicle body and the monitoring screen;

[0057] Step S33: The cleaning mode analysis and judgment module is started, and the data of the information collection module is retrieved. According to the analysis, the judgment results of the first-level cleaning mode, the second-level cleaning mode and the third-level cleaning mode are respectively given, wherein the water pressure increases step by step;

[0058] Step S34: Finally, according to the judgment result, the electrical signal is output to the distributed processing control module, and the distributed processing control module selects the global cleaning mode through the cleaning mode selection module, and starts the fully automatic car washing machine to start cleaning according to the selected global cleaning mode;

[0059] Step S35: The monitoring image analysis module analyzes the images during cleaning in real time, and also transmits the analysis result electric signal to the distributed processing control module, and the distributed processing control module dynamically adjusts the corresponding nozzles through the nozzle pressurizing module to increase the spraying water pressure;

[0060] Among them, in step S34, when the selected global cleaning mode is the three-level cleaning mode, the distributed processing control module controls all the spray heads to open at intervals and performs an additional round of flushing. In the second round, only the spray heads that were not opened in the first round are complementarily opened; thereby achieving that under the global full load condition, in order to ensure that the total water pressure can still provide sufficient water pressure for the subsequent local pressurized treatment process, it is subjected to intermittent pressure-divided flushing, thereby achieving maximum efficient operation and guarantee of deep cleaning capability.

[0061] Step S33 further includes the following steps:

[0062] Step S331: Obtain historical cleaning modes, and select the cleaning mode with the most historical cleaning modes used as the preliminary analysis result output;

[0063] Step S332: The system sets cleaning intervals for the primary cleaning mode, the secondary cleaning mode, and the tertiary cleaning mode, which correspond to (0, T), [T, 2T], and (2T, +∞), and then obtains the current cleaning interval t according to the last cleaning time recorded in the cleaning record database, and compares it with the cleaning interval set by the system. After determining that the interval falls into the corresponding cleaning mode, the current corresponding cleaning mode is output as the secondary analysis result;

[0064] Step S333: Finally, the vehicle use environment category currently selected by the user is retrieved. When the vehicle use environment is urban road commuting, it is analyzed and judged as the first-level cleaning mode. When the vehicle use environment is national highway / highway use, it is analyzed and judged as the second-level cleaning mode. When the vehicle use environment is rural / complex environment, it is judged as the third-level cleaning mode. Among them, when the user's vehicle use environment is mainly urban road commuting, because the urban roads are generally clean and tidy, the dirt analysis of the vehicle body is mainly dust adsorption, and its adhesion is relatively small, and the smaller water pressure of the first-level cleaning mode can ensure cleaning; and when When the user's vehicle is mainly used on national highways / highways, the road conditions are uneven and the vehicle speed is generally high, which will splash water stains on the vehicle body. The dirt on the vehicle body is mainly dust, a small amount of mud and sewage stains, etc., and the cleaning difficulty is moderate, so the water pressure is increased to improve the flushing intensity to ensure the cleanliness of the vehicle's overall flushing; finally, when the vehicle is used in a rural / complex environment, the dirt on the vehicle body is mainly a complex mixture of stubborn stains such as dust, a large amount of mud, bird droppings, and leaf secretions. The cleaning difficulty is uncontrollable and generally difficult, so the three-level cleaning mode is analyzed and judged to be used for overall cleaning;

[0065] Step S334: In steps S331-S333, the cleaning mode with the highest level in each analysis output result is selected as the total analysis and judgment result output; through the above steps, the vehicle flushing water pressure requirements of multiple categories of scenarios can be distributedly processed, and the global flushing mode can be selected intelligently and automatically. Under the premise of fully meeting the cleaning cleanliness, the total water pressure can be intelligently guaranteed not to be reduced due to excessive demand, so as to achieve the effect of efficient cleaning.

[0066] Step S35 further includes the following steps:

[0067] Step S351: grayscale processing is performed on the detail image being washed, and the grayscale value is output in real time;

[0068] Step S352: After the local flushing is completed, when the error between the identified grayscale value and the grayscale value of the historical data of the vehicle body is greater than the preset number r, the identified area is captured as the feature area, wherein when the feature area and the historical marking area coincide with each other in the vehicle body, the determination of the feature area is cancelled; by setting the historical marking area, the damaged points of the vehicle itself, the painting positions and other areas that are prone to cause misanalysis in the cleaning analysis process can be quickly screened out, reducing the need for repeated analysis of the area and the necessity for local nozzle pressure flushing in the modified area, thereby achieving the role of efficient analysis and processing;

[0069] Step S353: For the characteristic area, the electrical signal controls the nozzle pressurizing module to pressurize and spray the nozzle corresponding to the position of the characteristic area after position calibration;

[0070] Step S354: During the local pressurized flushing, the characteristic area image analysis is repeated. When the area of ​​the characteristic area signal feedback continues to get smaller, the pressurized flushing spray is continued. When the area of ​​the characteristic area signal feedback does not change after the pressurized flushing time threshold, the judgment submodule determines that the area is a new marked area and stores it in the cleaning record database. The whole vehicle flushing water pressure requirement can be judged through big data analysis during the fully automated flushing process. When the whole vehicle flushing water pressure requirement is met, the vehicle body detail flushing situation is further monitored. According to the monitoring and analysis results, it is determined whether targeted pressurized flushing is required for local stubborn stains on the vehicle body. Finally, the distributed analysis and processing of the car washing process and the control of the car washing program are realized to achieve the effect of efficient cleaning and deep cleaning water pressure.

[0071] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including 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, article or device.

[0072] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed processing system for big data on a cloud platform, characterized by: The system includes an information collection module, a cleaning analysis and processing module and a distributed processing control module. The information collection module is used to collect data related to the vehicle condition and vehicle usage of the current car wash. The cleaning analysis and processing module is used to analyze the cleaning status of dirty positions of the vehicle during the process of cleaning the car body. The distributed processing control module is used to distributedly control the water pressure of the cleaning spray head according to the cleaning analysis and processing results. The information collection module is electrically connected to the cleaning analysis and processing module, and the cleaning analysis and processing module is electrically connected to the distributed processing control module.

2. The distributed processing system for big data on a cloud platform according to claim 1, characterized in that: The information collection module includes a cleaning record database, a vehicle environment classification module and a monitoring and acquisition module. The cleaning record database is used to identify the historical vehicle cleaning data stored in the current vehicle. The vehicle environment classification module is used to collect the main vehicle usage environment of the registered owner. The monitoring and acquisition module is used to synchronously collect and shoot monitoring images during the automatic spray cleaning process.

3. The distributed processing system for big data on a cloud platform according to claim 2, characterized in that: The cleaning analysis and processing module includes a position calibration module, a special area marking module, a cleaning mode analysis and judgment module and a monitoring screen analysis module. The position calibration module is used to calibrate the position of the vehicle in the monitoring collection screen with the position of each sprinkler head flushing point. The special area marking module is used to mark the special area of ​​the monitoring screen after calibration according to the historical cleaning data. The cleaning mode analysis and judgment module is used to analyze and judge the mode selection for global cleaning of the vehicle. The monitoring screen analysis module is used to analyze the specific monitoring screen during the cleaning process.

4. The distributed processing system for big data on a cloud platform according to claim 3, characterized in that: The distributed processing control module includes a cleaning mode selection module and a nozzle pressurizing module. The cleaning mode selection module is used to control the selection of the cleaning mode of the automatic car wash, and the nozzle pressurizing module is used to control the pressurization of the spray head in real time according to the analysis and processing signal.

5. The distributed processing system for big data on a cloud platform according to claim 4, characterized in that: The monitoring screen analysis module further includes an image grayscale processing submodule, a feature area signal feedback submodule and a judgment submodule. The image grayscale processing submodule is used to grayscale the monitoring screen image and output the grayscale value in real time. The feature area signal feedback submodule is used to obtain feature area screen signal feedback and analyze the change status of the feature area. The judgment submodule is used to distinguish and judge the feature area based on the analysis results.

6. The distributed processing system for big data on a cloud platform according to claim 5, characterized in that: The operating method of the distributed processing system comprises the following steps: Step S1: When the vehicle is parked and the fully automatic car wash machine is started, the information collection module starts to collect and retrieve the historical car wash data, vehicle use environment and vehicle images of the vehicle; Step S2: The cleaning analysis processing module starts to read the system acquisition data; Step S3: Processing the collected data, analyzing and determining the cleaning mode and the monitoring screen during the cleaning process; Step S4: the distributed processing control module selects the car washing mode of the full-automatic car washing machine in real time according to the analysis and judgment results and pressurizes the spray heads that need to be pressurized locally.

7. The distributed processing system for big data on a cloud platform according to claim 6, characterized in that: The step S1 further comprises the following steps: Step S11: by identifying the license plate information, calling the cleaning record database of the vehicle in the system, and obtaining the last recorded cleaning time, historical cleaning mode selection and the location of the historical marking area in the vehicle body through the cleaning record database; Step S12: Then, the main vehicle use environment category manually selected by the user is obtained through the vehicle use environment classification module, wherein the main vehicle use environment categories include: urban road commuting, national highway / highway use, and township / complex environment use; Step S13: Finally, the monitoring and acquisition module starts to run and collects the overall and detailed images.

8. The distributed processing system for big data on a cloud platform according to claim 7, characterized in that: The step S3 further comprises the following steps: Step S31: When the vehicle starts to be fully automatically cleaned, the top, bottom and middle points on the center line of the vehicle body are calibrated through the position calibration module, and then the mobile device is controlled so that the top, bottom and middle points in the global image coincide with the calibrated positions; Step S32: obtaining the position of the historical marking area in the vehicle body in the information collection module, and then marking the position of the historical marking area in the vehicle body and the corresponding position of the monitoring screen according to the calibrated position of the current vehicle body and the monitoring screen; Step S33: The cleaning mode analysis and judgment module is started, and the data of the information collection module is retrieved. According to the analysis, the judgment results of the first-level cleaning mode, the second-level cleaning mode and the third-level cleaning mode are respectively given, wherein the water pressure increases step by step; Step S34: Finally, according to the judgment result, the electrical signal is output to the distributed processing control module, and the distributed processing control module selects the global cleaning mode through the cleaning mode selection module, and starts the fully automatic car washing machine to start cleaning according to the selected global cleaning mode; Step S35: The monitoring image analysis module analyzes the images during cleaning in real time, and also transmits the analysis result electric signal to the distributed processing control module, and the distributed processing control module dynamically adjusts the corresponding nozzles through the nozzle pressurizing module to increase the spraying water pressure; Among them, in step S34, when the selected global cleaning mode is the three-level cleaning mode, the distributed processing control module controls all the spray heads to open at intervals and performs an additional round of flushing. The second round complementarily opens only the spray heads that were not opened in the first round.

9. The distributed processing system for big data on a cloud platform according to claim 8, characterized in that: The step S33 further comprises the following steps: Step S331: Obtain historical cleaning modes, and select the cleaning mode with the most historical cleaning modes used as the preliminary analysis result output; Step S332: The system sets cleaning intervals for the primary cleaning mode, the secondary cleaning mode, and the tertiary cleaning mode, which correspond to (0, T), [T, 2T], and (2T, +∞), and then obtains the current cleaning interval t according to the last cleaning time recorded in the cleaning record database, and compares it with the cleaning interval set by the system. After determining that the interval falls into the corresponding cleaning mode, the current corresponding cleaning mode is output as the secondary analysis result; Step S333: Finally, the vehicle use environment category currently selected by the user is retrieved. When the vehicle use environment is urban road commuting, it is analyzed and determined as the first-level cleaning mode. When the vehicle use environment is national highway / highway use, it is analyzed and determined as the second-level cleaning mode. When the vehicle use environment is rural / complex environment use, it is determined as the third-level cleaning mode. Step S334: In steps S331-S333, the cleaning mode with the highest level among the analysis output results is selected as the total analysis and judgment result output.

10. The distributed processing system for big data on a cloud platform according to claim 9, characterized in that: The step S35 further comprises the following steps: Step S351: grayscale processing is performed on the detail image being washed, and the grayscale value is output in real time; Step S352: After the local flushing is completed, when the error between the identified gray value and the gray value of the historical data of the vehicle body is greater than a preset number r, the identified area is captured as a feature area, wherein when the feature area and the historical marking area coincide with each other in the vehicle body, the determination of the feature area is cancelled; Step S353: For the characteristic area, the electrical signal controls the nozzle pressurizing module to pressurize and spray the nozzle corresponding to the position of the characteristic area after position calibration; Step S354: During the local pressurized flushing, the characteristic area image analysis is repeated. When the area of ​​the characteristic area signal feedback continues to get smaller, the pressurized flushing spray is continued. When the area of ​​the characteristic area signal feedback does not change after the pressurized flushing time threshold, the judgment submodule determines that the area is a new marked area and stores it in the cleaning record database.

Images