Unmanned car washer car washing environment intelligent monitoring system based on big data

Abstract

The invention discloses an unmanned car washer car washing environment intelligent monitoring system based on big data, which relates to the technical field of unmanned car washer monitoring and comprises a data acquisition module, an early warning module, an analysis module and a control module. The data acquisition module is used for acquiring contour data of a vehicle and operation data generated by parts in the operation process of the vehicle washing machine; the analysis module is used for analyzing the operation data of the car washer and judging whether early warning is needed or not according to the operation data of car washer components; the early warning module is used for giving an early warning to prompt that the car washer needs to be overhauled; the control module is used for controlling the vehicle washing machine to wash the vehicle; the states of the parts are monitored according to the comprehensive loss degree of the car washing machine and the operation parameters of the car washing machine parts, the self-checking capacity of the car washing machine can be enhanced, the car washing machine has the safety early warning function, and the maintenance efficiency of the car washing machine parts is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned car wash machine monitoring, and in particular to an intelligent monitoring system for car wash environments of unmanned car wash machines based on big data. Background Art

[0002] Unmanned car wash machine is an automated car washing equipment. It uses sensors, image recognition technology, automated mechanical arms and intelligent management systems to realize automatic identification, cleaning, rinsing and air drying of vehicles. The whole process does not require human intervention. This intelligent car washing method can not only provide 24-hour uninterrupted service, but also greatly improve the efficiency and quality of car washing. The internal environment of the car washing machine has a significant impact on the efficiency and quality of car washing. Due to the lack of human intervention, the wear and tear of the internal parts of the car washing machine cannot be known in time, and the parts of the car washing machine cannot be repaired in time. For example, the passivation of the car brush drive wheel may affect the car washing effect, but its wear and tear cannot be known in time. In addition, there are differences in the models and sizes of vehicles using the car washing machine, and the wear and tear caused to the car washing machine is not the same. Therefore, how to improve the maintenance efficiency of the car washing machine parts has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent monitoring system for the car washing environment of an unmanned car washing machine based on big data to solve the problems raised in the prior art.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent monitoring system for car washing environment of an unmanned car wash machine based on big data, comprising: a data acquisition module, an early warning module, an analysis module and a control module; the output end of the data acquisition module is connected to the input end of the analysis module, for obtaining the contour data of the vehicle and the operation data generated by the components during the operation of the car wash machine; the output end of the analysis module is connected to the input end of the early warning module, for analyzing the operation data of the car wash machine, and judging whether an early warning is needed according to the operation data of the car wash machine components, and if necessary, issuing an instruction to the early warning module; if not, keeping monitoring the operation data of the car wash machine components; the early warning module is used to respond to the instruction of the analysis module and issue an early warning to prompt that the car wash machine needs to be repaired; the control module is used to control the car wash machine to wash the vehicle.

[0005] The data acquisition module also includes an image acquisition unit and a sensor unit. The image acquisition unit is used to obtain the contour data of the vehicle; the sensor unit is used to obtain the operating data of the car washing machine components. The analysis module also includes: a vehicle model size database, a data storage unit, a comprehensive wear calculation unit, a vehicle size determination unit and an early warning analysis unit; the vehicle model size database is used to store the vehicle model, size and cleaning area data; the data storage unit is used to store the operating data of the car washing machine components; the comprehensive wear calculation unit is used to determine the wear of the car washing machine; the early warning analysis unit analyzes the operating status of the car washing machine components according to the wear of the car washing machine and the operating data of the current components of the car washing machine; the vehicle size determination unit is used to measure the size parameters of the vehicle.

[0006] The vehicle model size database is established by the following steps:

[0007] Obtain historical car wash data of a car wash machine, determine the models of vehicles using the car wash machine from the historical car wash data, and extract the dimensions of typical vehicles from vehicles of different models;

[0008] For the extracted typical vehicles, the washing area of ​​the typical vehicles is determined, where the washing area is the surface area of ​​the typical vehicles; the minimum value of the washing area of ​​the typical vehicles is set as the washing area unit, and the washing areas of other washing vehicles are represented by area units; SQmin is set as the washing area unit, SQ represents the washing area of ​​other washing vehicles, the ratio k of SQ and SQmin is calculated, and the washing areas of other washing vehicles are represented as k×SQmin; the vehicle models, sizes and washing areas are stored, and a vehicle model size database is established.

[0009] Get instant vehicle dimensions via:

[0010] When brushing the car body, the brush of the car washing machine is controlled to refresh the vehicle along the direction of the vehicle, and the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the length information of the vehicle; when the car washing machine brushes the head or tail of the vehicle, when the horizontal movement distance information begins to change, the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the width information of the vehicle, and when the vertical movement distance information begins to change, the vertical movement distance information of the brush is obtained. When the vertical movement distance information stops changing, the current vertical movement distance information is the height information of the vehicle; when brushing the tire, when the horizontal movement distance information begins to change, the first maximum value of the horizontal movement distance information of the brush is obtained. When the vertical movement distance information begins to change, the second maximum value of the vertical movement distance information of the brush is obtained. The larger value between the first maximum value and the second maximum value is taken to obtain the wheel diameter information of the vehicle.

[0011] The comprehensive loss calculation unit determines the comprehensive loss of the car washing machine through the following steps:

[0012] Let t represent the usage time of the car washing machine, num represent the number of times the car washing machine is used, sq represent the sum of the cleaning areas of vehicles using the car washing machine, then the comprehensive loss degree L of the car washing machine is calculated by the following formula: L = W1×t+W2×num+W3×sq, where W1, W2 and W3 are weights. When a vehicle uses the car washing machine for a car washing operation, the comprehensive loss degree is updated once.

[0013] The washing area of ​​a vehicle will affect the loss of the car washing machine. When the washing area of ​​a vehicle is large, the contact surface between the vehicle and the car washing machine is larger during washing, and the brushes, nozzles and other parts of the car washing machine will wear faster. Washing a vehicle with a large surface area requires greater water pressure, longer washing time and more car washing agent, which will increase the operating load of the car washing machine, put additional pressure on the pump and injection system of the car washing machine, and accelerate the loss of car washing machine parts. The number of car washings by the car washing machine affects the number of starts and stops of the car washing machine parts, which in turn causes the loss of car washing machine parts. The size of the vehicle is subject to clear requirements, and the size similarity of vehicles under the same model is high. At this time, some representative vehicles are selected as typical vehicles. When a vehicle is washed by a car washing machine, the closest typical vehicle is determined according to the vehicle model and size. The surface area of ​​the vehicle and the typical vehicle is close, and the loss to the car washing machine is also close. There is no need to obtain detailed surface area data of the vehicle separately, and the impact of the vehicle's surface area on the loss of car washing machine parts is not ignored.

[0014] The vehicle wash area is determined by the following steps:

[0015] In the historical car washing data of the car washing machine, the washing area of ​​all vehicles is represented by the washing area of ​​a typical vehicle, and the difference between other vehicles and the typical vehicle is calculated. Where a, b, c and r represent the length, width, height and wheel diameter of the vehicle, a d , b d 、c d and r d The length, width, height and wheel diameter of a typical vehicle are indicated, and the typical vehicle with the smallest difference from the vehicle is determined, and the washing area of ​​the typical vehicle with the smallest difference is used as the washing area of ​​the vehicle;

[0016] In the process of instantly acquiring the vehicle's size information, the data acquisition module acquires the vehicle's contour data to determine the model of the current vehicle; acquires the brush's movement data, and determines the length, width, height and wheel diameter of the current vehicle based on the brush's movement data; calculates the difference between the current vehicle and a typical vehicle of the same model, and determines the typical vehicle with the smallest difference. The washing area of ​​the typical vehicle with the smallest difference is the washing area suitable for the current vehicle.

[0017] The warning analysis unit determines whether a safety warning needs to be issued through the following steps:

[0018] S31, obtaining the operation data distribution of the car washing machine of the typical vehicle corresponding to the current vehicle within the current comprehensive loss degree interval; let L0 represent the current comprehensive loss degree, LB1, LB2, ..., LBm represent the comprehensive loss degrees less than L0 within the current comprehensive loss degree interval, LA1, LA2, ..., LAn represent the comprehensive loss degrees greater than L0 within the current comprehensive loss degree interval; m is the number of comprehensive loss degrees less than L0 within the current comprehensive loss degree interval, and n is the number of comprehensive loss degrees greater than L0 within the current comprehensive loss degree interval;

[0019] S32, express L0 through LB1 and LA1, L0=u1×LB1+v1×LA1, u1 and v1 are coefficients, let the operating data in the operating data distribution under the comprehensive loss degree LB1 and LA1 be BX1 and AX1, convert it into the operating data X0 under the comprehensive loss degree L0, X0=u1×BX1+v1×AX1, and obtain an operating data distribution under the comprehensive loss degree L0; calculate the mean absolute error sum between LB1 and LA1 and L0, and obtain the mean absolute error under the operating data distribution; for LA2, ..., LAn, respectively combine with LB1, perform the same operation as LA1; obtain n operating data distributions and the mean absolute error sum under the comprehensive loss degree L0; for LB2, ..., LBm, perform the same operation as LB1; obtain n×m operating data distributions and the mean absolute error sum under the comprehensive loss degree L0;

[0020] S33, obtaining the operation data of the current car washing machine, performing cluster analysis on the operation data of the current vehicle and the distribution of n×m operation data under the comprehensive loss degree L0, and judging whether it is necessary to issue an early warning according to the clustering result; if there is no outlier in the operation data of the current car washing machine, proceeding to step S34; if there is an outlier in the operation data of the current car washing machine, issuing an early warning and ending;

[0021] S34, merge the n×m operating data distributions under the comprehensive loss degree L0 into a standard operating data distribution, Y=p 1 ×Y1+p 2 ×Y2+…+p mn ×Ymn, where Y1, Y2, ..., Ymn are the operating data in the operating data distribution under the comprehensive loss degree L0, p 1 、p 2 and p mn is the weight, determined by the mean absolute error and; let MAE i represents the mean absolute error of the i-th running data distribution, then the weight p of the i-th running data distribution i =MAE i / ∑MAE i ;

[0022] S35, compare the curve of the current car wash machine operation data with the standard operation data distribution, and calculate the error between the two. If the error does not exceed the set threshold, it is determined that the car wash machine is in a normal state and no warning is required; if the error exceeds the set threshold, a warning is issued and the process ends.

[0023] From the time the vehicle enters the car wash machine to the time the car wash machine completes the car wash, the parameters of the car wash machine change according to a certain trend; compare the current vehicle operation data with the operation data of each component of the car wash machine under normal conditions to determine whether the car wash machine is normal during the current vehicle washing. The parameter change pattern of the car wash machine will change with the wear and tear of the car wash machine components. It is necessary to obtain the parameter change pattern under the current wear and tear of the car wash machine, and then analyze the status of the car wash machine components.

[0024] The operation data distribution of the car washing machine under different comprehensive loss degrees is obtained through the following steps:

[0025] The operation data of the car washing machine is obtained during the car washing process. Each car washing process corresponds to the model size and wear degree of a typical vehicle. The operation data of the car washing machine under different wear degrees of the same typical vehicle are integrated and stored to obtain the operation data distribution of the car washing machine under different comprehensive wear degrees of the typical vehicle.

[0026] The comprehensive loss range is determined by the following steps:

[0027] The historical car washing data of car wash machines with the same vehicle model size are analyzed, and the amplitude of the deviation between the operating data distributions of the two car wash machines under different comprehensive loss degrees is calculated, and the amplitude of the deviation is compared with the set deviation range; the difference e between the comprehensive loss degrees when it is less than the set deviation range is obtained, and combined with the current comprehensive loss degree L0, from L0 to, a comprehensive loss degree interval with L0 as the center and a length of 2e is obtained.

[0028] Compared with the prior art, the beneficial effects of the present invention are: obtaining the contour and size information of the vehicle, monitoring the comprehensive wear and tear of the car washing machine, monitoring the status of the components according to the comprehensive wear and tear of the car washing machine and the operating parameters of the car washing machine components, which can enhance the self-inspection capability of the car washing machine, enable the car washing machine to have a safety warning function, and improve the maintenance efficiency of the car washing machine components. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a structural schematic diagram of the intelligent monitoring system of the car washing environment of an unmanned car washing machine based on big data of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] Example: Figure 1 As shown, the present invention provides a technical solution, an intelligent monitoring system for car washing environment of an unmanned car washing machine based on big data, comprising: a data acquisition module, an early warning module, an analysis module and a control module; the output end of the data acquisition module is connected to the input end of the analysis module, for acquiring the contour data of the vehicle and the operation data generated by the components during the operation of the car washing machine; the output end of the analysis module is connected to the input end of the early warning module, for analyzing the operation data of the car washing machine, judging whether an early warning is needed according to the operation data of the car washing machine components, and if necessary, issuing an instruction to the early warning module; if not, maintaining monitoring of the operation data of the car washing machine components; the early warning module is used to respond to the instruction of the analysis module, and issue an early warning to prompt that the car washing machine needs to be repaired; the control module is used to control the car washing machine to wash the vehicle.

[0032] The data acquisition module also includes an image acquisition unit and a sensor unit. The image acquisition unit is used to obtain the contour data of the vehicle; the sensor unit is used to obtain the operating data of the car washing machine components. The analysis module also includes: a vehicle model size database, a data storage unit, a comprehensive wear calculation unit, a vehicle size determination unit and an early warning analysis unit; the vehicle model size database is used to store the vehicle model, size and cleaning area data; the data storage unit is used to store the operating data of the car washing machine components; the comprehensive wear calculation unit is used to determine the wear of the car washing machine; the early warning analysis unit analyzes the operating status of the car washing machine components according to the wear of the car washing machine and the operating data of the current components of the car washing machine; the vehicle size determination unit is used to measure the size parameters of the vehicle.

[0033] The vehicle model size database is established by the following steps:

[0034] Obtain historical car wash data of a car wash machine, determine the models of vehicles using the car wash machine from the historical car wash data, and extract the dimensions of typical vehicles from vehicles of different models;

[0035] For the extracted typical vehicles, the washing area of ​​the typical vehicles is determined, where the washing area is the surface area of ​​the typical vehicles; the minimum value of the washing area of ​​the typical vehicles is set as the washing area unit, and the washing areas of other washing vehicles are represented by area units; SQmin is set as the washing area unit, SQ represents the washing area of ​​other washing vehicles, the ratio k of SQ and SQmin is calculated, and the washing areas of other washing vehicles are represented as k×SQmin; the vehicle models, sizes and washing areas are stored, and a vehicle model size database is established.

[0036] When brushing the car body, the brush of the car washing machine is controlled to refresh the vehicle along the direction of the vehicle, and the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the length information of the vehicle; when the car washing machine brushes the head or tail of the vehicle, when the horizontal movement distance information begins to change, the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the width information of the vehicle, and when the vertical movement distance information begins to change, the vertical movement distance information of the brush is obtained. When the vertical movement distance information stops changing, the current vertical movement distance information is the height information of the vehicle; when brushing the tire, when the horizontal movement distance information begins to change, the first maximum value of the horizontal movement distance information of the brush is obtained. When the vertical movement distance information begins to change, the second maximum value of the vertical movement distance information of the brush is obtained. The larger value between the first maximum value and the second maximum value is taken to obtain the wheel diameter information of the vehicle.

[0037] Let t represent the usage time of the car washing machine, num represent the number of times the car washing machine is used, sq represent the sum of the cleaning areas of vehicles using the car washing machine, then the comprehensive loss degree L of the car washing machine is calculated by the following formula: L = W1×t+W2×num+W3×sq, where W1, W2 and W3 are weights. When a vehicle uses the car washing machine for a car washing operation, the comprehensive loss degree is updated once.

[0038] The vehicle wash area is determined by the following steps:

[0039] In the historical car washing data of the car washing machine, the washing area of ​​all vehicles is represented by the washing area of ​​a typical vehicle, and the difference between other vehicles and the typical vehicle is calculated. Where a, b, c and r represent the length, width, height and wheel diameter of the vehicle, a d , b d 、c d and r d The length, width, height and wheel diameter of a typical vehicle are indicated, and the typical vehicle with the smallest difference from the vehicle is determined, and the washing area of ​​the typical vehicle with the smallest difference is used as the washing area of ​​the vehicle;

[0040] In the process of instantly acquiring the vehicle's size information, the data acquisition module acquires the vehicle's contour data to determine the model of the current vehicle; acquires the brush's movement data, and determines the length, width, height and wheel diameter of the current vehicle based on the brush's movement data; calculates the difference between the current vehicle and a typical vehicle of the same model, and determines the typical vehicle with the smallest difference. The washing area of ​​the typical vehicle with the smallest difference is the washing area suitable for the current vehicle.

[0041] The warning analysis unit determines whether a safety warning needs to be issued through the following steps:

[0042] S31, obtaining the operation data distribution of the car washing machine of the typical vehicle corresponding to the current vehicle within the current comprehensive loss degree interval; let L0 represent the current comprehensive loss degree, LB1, LB2, ..., LBm represent the comprehensive loss degrees less than L0 within the current comprehensive loss degree interval, LA1, LA2, ..., LAn represent the comprehensive loss degrees greater than L0 within the current comprehensive loss degree interval; m is the number of comprehensive loss degrees less than L0 within the current comprehensive loss degree interval, and n is the number of comprehensive loss degrees greater than L0 within the current comprehensive loss degree interval;

[0043] S32, express L0 through LB1 and LA1, L0=u1×LB1+v1×LA1, u1 and v1 are coefficients, let the operating data in the operating data distribution under the comprehensive loss degree LB1 and LA1 be BX1 and AX1, convert it into the operating data X0 under the comprehensive loss degree L0, X0=u1×BX1+v1×AX1, and obtain an operating data distribution under the comprehensive loss degree L0; calculate the mean absolute error sum between LB1 and LA1 and L0, and obtain the mean absolute error under the operating data distribution; for LA2, ..., LAn, respectively combine with LB1, perform the same operation as LA1; obtain n operating data distributions and the mean absolute error sum under the comprehensive loss degree L0; for LB2, ..., LBm, perform the same operation as LB1; obtain n×m operating data distributions and the mean absolute error sum under the comprehensive loss degree L0;

[0044] S33, obtaining the operation data of the current car washing machine, performing cluster analysis on the operation data of the current vehicle and the distribution of n×m operation data under the comprehensive loss degree L0, and judging whether it is necessary to issue an early warning according to the clustering result; if there is no outlier in the operation data of the current car washing machine, proceeding to step S34; if there is an outlier in the operation data of the current car washing machine, issuing an early warning and ending;

[0045] S34, merge the n×m operating data distributions under the comprehensive loss degree L0 into a standard operating data distribution, Y=p 1 ×Y1+p 2 ×Y2+…+p mn×Ymn, where Y1, Y2, ..., Ymn are the operating data in the operating data distribution under the comprehensive loss degree L0, p 1 、p 2 and p mn is the weight, determined by the mean absolute error and; let MAE i represents the mean absolute error of the i-th running data distribution, then the weight p of the i-th running data distribution i =MAE i / ∑MAE i ;

[0046] S35, compare the curve of the current car wash machine operation data with the standard operation data distribution, and calculate the error between the two. If the error does not exceed the set threshold, it is determined that the car wash machine is in a normal state and no warning is required; if the error exceeds the set threshold, a warning is issued and the process ends.

[0047] The operating parameters of the components may include: vibration or noise of mechanical parts such as brushes, nozzles, chains, bearings, air pressure of air compressors in pneumatic systems, water pressure of water pumps in water systems, flow rate of water pipes, etc.; mechanical parts of car washers (such as brushes, nozzles, chains, bearings, etc.) will wear or be damaged after long-term use, which may lead to reduced car washing effect, increased noise or equipment shutdown; pneumatic system parts of car washers, such as air compressors or pneumatic nozzles, may have problems such as insufficient air pressure, air leakage or control failure, which will affect the water spraying and brushing effect of the car washers; the water system of car washers, including water pumps, water pipes, nozzles and filters, may be blocked, leaked or damaged, resulting in the car washers being unable to spray water normally or spraying water unevenly; as long as If an operating parameter is detected to be abnormal, an early warning signal will be issued; first, the abnormal value of the operating parameter is detected. When an abnormal value appears, an early warning message is issued in time. If there is no abnormal value, the overall trend of the operating data may be different from the normal situation. Then the overall trend of the operating data is analyzed; clustering is used to determine whether there are abnormal values ​​in the operating parameters. Kmeans, hierarchical clustering, density-based clustering, etc. can be selected to determine the abnormal value; when analyzing the overall trend of the operating data, the operating parameters change over time during the car washing process. According to the error between the curve of the current car washing machine's operating parameters changing over time and the standard operating data, it is determined whether to issue an early warning. The error can be selected from the root mean square error, mean absolute percentage error, absolute error, etc. According to the abnormal operating data, the components corresponding to the operating data are found, and the components that need to be repaired can be determined.

[0048] The operation data distribution of the car washing machine under different comprehensive loss degrees is obtained through the following steps:

[0049] The operation data of the car washing machine is obtained during the car washing process. Each car washing process corresponds to the model size and wear degree of a typical vehicle. The operation data of the car washing machine under different wear degrees of the same typical vehicle are integrated and stored to obtain the operation data distribution of the car washing machine under different comprehensive wear degrees of the typical vehicle.

[0050] The comprehensive loss range is determined by the following steps:

[0051] The historical car washing data of car wash machines with the same vehicle model size are analyzed, and the amplitude of the deviation between the operating data distributions of the two car wash machines under different comprehensive loss degrees is calculated, and the amplitude of the deviation is compared with the set deviation range; the difference e between the comprehensive loss degrees when it is less than the set deviation range is obtained, and combined with the current comprehensive loss degree L0, from L0 to, a comprehensive loss degree interval with L0 as the center and a length of 2e is obtained.

[0052] The amplitude of the deviation can be taken as the average percentage error, with f1, f2, and f3 representing the operating data on the operating data distribution under the comprehensive loss degree, and g1, g2, and g3 representing the operating data on the operating data distribution under another comprehensive loss degree. The percentage errors are |g1-f1| / f1, |g2-f2| / f2, and |g3-f3| / f3, respectively. The average value is taken to obtain the average percentage error; the set deviation range can be set to 5%, 10%, etc. according to needs;

[0053] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent monitoring system for the car washing environment of an unmanned car washing machine based on big data, characterized in that: include: Data acquisition module, early warning module, analysis module and control module; The output end of the data acquisition module is connected to the input end of the analysis module, and is used to obtain the contour data of the vehicle and the operation data generated by the components during the operation of the car washing machine; the output end of the analysis module is connected to the input end of the early warning module, and is used to analyze the operation data of the car washing machine, and determine whether an early warning is required according to the operation data of the components of the car washing machine, and if necessary, send a command to the early warning module; If not necessary, the operation data of the car washing machine components are kept under monitoring; the early warning module is used to respond to the instructions of the analysis module and issue an early warning to remind that the car washing machine needs to be repaired; the control module is used to control the car washing machine to wash the vehicle.

2. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 1 is characterized in that: The data acquisition module also includes an image acquisition unit and a sensor unit. The image acquisition unit is used to acquire the contour data of the vehicle; the sensor unit is used to acquire the operation data of the car washing machine components.

3. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 1 is characterized in that: The analysis module also includes: a vehicle model size database, a data storage unit, a comprehensive wear calculation unit, a vehicle size determination unit and an early warning analysis unit; the vehicle model size database is used to store the vehicle model, size and cleaning area data; the data storage unit is used to store the operating data of the car washing machine components; the comprehensive wear calculation unit is used to determine the wear degree of the car washing machine; the early warning analysis unit analyzes the operating status of the car washing machine components according to the wear degree of the car washing machine and the operating data of the current components of the car washing machine; the vehicle size determination unit is used to measure the size parameters of the vehicle.

4. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 3 is characterized in that: The vehicle model size database is established by the following steps: Obtain historical car wash data of a car wash machine, determine the models of vehicles using the car wash machine from the historical car wash data, and extract the dimensions of typical vehicles from vehicles of different models; For the extracted typical vehicles, determine the washing area of ​​the typical vehicles, where the washing area is the surface area of ​​the typical vehicles; let the minimum value of the washing area of ​​the typical vehicles be the washing area unit, and represent the washing areas of other washing vehicles by the area unit; Let SQmin represent the washing area unit, SQ represent the washing area of ​​other washing vehicles, calculate the ratio k of SQ and SQmin, and represent the washing area of ​​other washing vehicles as k×SQmin; store the vehicle model, size and washing area, and establish a vehicle model size database.

5. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 3 is characterized in that: Get instant vehicle dimensions via: When brushing the car body, the brush of the car washing machine is controlled to refresh the vehicle along the direction of the vehicle, and the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the length information of the vehicle; when the car washing machine brushes the head or tail of the vehicle, when the horizontal movement distance information begins to change, the horizontal movement distance information of the brush is obtained. When the horizontal movement distance information stops changing, the current horizontal movement distance information is the width information of the vehicle, and when the vertical movement distance information begins to change, the vertical movement distance information of the brush is obtained. When the vertical movement distance information stops changing, the current vertical movement distance information is the height information of the vehicle; when brushing the tire, when the horizontal movement distance information begins to change, the first maximum value of the horizontal movement distance information of the brush is obtained. When the vertical movement distance information begins to change, the second maximum value of the vertical movement distance information of the brush is obtained. The larger value between the first maximum value and the second maximum value is taken to obtain the wheel diameter information of the vehicle.

6. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 5 is characterized in that: The comprehensive loss calculation unit determines the comprehensive loss of the car washing machine through the following steps: Let t represent the usage time of the car washing machine, num represent the number of times the car washing machine is used, sq represent the sum of the cleaning areas of vehicles using the car washing machine, then the comprehensive loss degree L of the car washing machine is calculated by the following formula: L = W1×t+W2×num+W3×sq, where W1, W2 and W3 are weights. When a vehicle uses the car washing machine for a car washing operation, the comprehensive loss degree is updated once.

7. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 6 is characterized in that: The vehicle wash area is determined by the following steps: In the historical car washing data of the car washing machine, the washing area of ​​all vehicles is represented by the washing area of ​​a typical vehicle, and the difference between other vehicles and the typical vehicle is calculated. Where a, b, c and r represent the length, width, height and wheel diameter of the vehicle, a d , b d 、c d and r d The length, width, height and wheel diameter of a typical vehicle are indicated, and the typical vehicle with the smallest difference from the vehicle is determined, and the washing area of ​​the typical vehicle with the smallest difference is used as the washing area of ​​the vehicle; In the process of instantly acquiring the vehicle's size information, the data acquisition module acquires the vehicle's contour data to determine the model of the current vehicle; acquires the brush's movement data, and determines the length, width, height and wheel diameter of the current vehicle based on the brush's movement data; calculates the difference between the current vehicle and a typical vehicle of the same model, and determines the typical vehicle with the smallest difference. The washing area of ​​the typical vehicle with the smallest difference is the washing area suitable for the current vehicle.

8. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 7 is characterized in that: The warning analysis unit determines whether a safety warning needs to be issued through the following steps: S31, obtaining the operation data distribution of the car washing machine of the typical vehicle corresponding to the current vehicle within the current comprehensive loss degree range; let L0 represent the current comprehensive loss degree, LB1, LB2, ..., LBm represent the comprehensive loss degree less than L0 within the current comprehensive loss degree range, and LA1, LA2, ..., LAn represent the comprehensive loss degree greater than L0 within the current comprehensive loss degree range; m is the number of comprehensive loss degrees less than L0 in the current comprehensive loss degree interval, and n is the number of comprehensive loss degrees greater than L0 in the current comprehensive loss degree interval; S32, express L0 through LB1 and LA1, L0=u1×LB1+v1×LA1, u1 and v1 are coefficients, let the operating data in the operating data distribution under the comprehensive loss degree LB1 and LA1 be BX1 and AX1, convert it into the operating data X0 under the comprehensive loss degree L0, X0=u1×BX1+v1×AX1, and obtain an operating data distribution under the comprehensive loss degree L0; calculate the mean absolute error sum between LB1 and LA1 and L0, and obtain the mean absolute error under the operating data distribution; combine LA2, ..., LAn with LB1 respectively, and perform the same operation as LA1; obtain n operating data distributions and the mean absolute error sum under the comprehensive loss degree L0; perform the same operation as LB1 for LB2, ..., LBm; Get the distribution and mean absolute error of n×m running data under the comprehensive loss degree L0; S33, obtaining the current operation data of the car washing machine, performing cluster analysis on the current vehicle operation data and the n×m operation data distribution under the comprehensive loss degree L0, and determining whether it is necessary to issue an early warning according to the clustering result; If there is no outlier in the current operation data of the car washing machine, proceed to step S34; If there are outliers in the current car wash machine operation data, an early warning is issued and the process ends; S34, merge the n×m operating data distributions under the comprehensive loss degree L0 into a standard operating data distribution, Y=p1×Y1+p2×Y2+…+p mn ×Ymn, where Y1, Y2, ..., Ymn are the operating data in the operating data distribution under the comprehensive loss degree L0, p1, p2 and p mn is the weight, determined by the mean absolute error and; let MAE i represents the mean absolute error of the i-th running data distribution, then the weight p of the i-th running data distribution i =MAE i / ∑MAE i ; S35, compare the curve of the current car wash machine operation data with the standard operation data distribution, and calculate the error between the two. If the error does not exceed the set threshold, it is determined that the car wash machine is in a normal state and no warning is required; if the error exceeds the set threshold, a warning is issued and the process ends.

9. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 8 is characterized in that: The operation data distribution of the car washing machine under different comprehensive loss degrees is obtained through the following steps: The operation data of the car washing machine is obtained during the car washing process. Each car washing process corresponds to the model size and wear degree of a typical vehicle. The operation data of the car washing machine under different wear degrees of the same typical vehicle are integrated and stored to obtain the operation data distribution of the car washing machine under different comprehensive wear degrees of the typical vehicle.

10. The intelligent monitoring system for car washing environment of unmanned car washing machine based on big data according to claim 8 is characterized in that: The comprehensive loss range is determined by the following steps: The historical car washing data of car wash machines with the same vehicle model size are analyzed, and the amplitude of the deviation between the operating data distributions of the two car wash machines under different comprehensive loss degrees is calculated, and the amplitude of the deviation is compared with the set deviation range; the difference e between the comprehensive loss degrees when it is less than the set deviation range is obtained, and combined with the current comprehensive loss degree L0, from L0 to, a comprehensive loss degree interval with L0 as the center and a length of 2e is obtained.

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