Real-time Data Acquisition and Processing Method and System for Smart Park Based on Edge Computing

Abstract

The present invention discloses a real-time data collection and processing method and system for a smart park based on edge computing, which relates to the field of data collection technology and is used to improve the problem of different display effects caused by the use of a unified processing method for park images at different locations. The method comprises obtaining attribute data and point information of each collection device in the park, setting a collection force scoring mechanism to score the collection force of different collection devices using fuzzy reasoning, dividing the park into regions according to the collection force score and point information, performing real-time detection on the detection area and the number of faults of the collection equipment in each divided area and calculating the real-time coverage rate of each divided area using a polynomial regression algorithm, classifying each divided area, obtaining regional images of low real-time coverage areas for marking and collecting the amount of dust and humidity in the space, calculating the humidity transfer rate, and judging whether the corresponding area uses an enhanced edge computing algorithm to process the regional image by using a geometric mean method.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition technology, and more specifically, to a method and system for real-time data acquisition and processing in a smart park based on edge computing. Background Art

[0002] Data collection technology refers to the process of automatically or manually collecting, recording, and processing data. The application of data collection technology and real-time data collection in smart parks can improve the accuracy of data collection in smart parks. Combined with edge computing to process collected images, it can reduce park management resources.

[0003] The existing technology has the following deficiencies:

[0004] In the past, when collecting data for smart parks, collection equipment was installed at different locations in the park to obtain real-time data information from each location in the park. The data collection locations in the park were not distinguished, and real-time images were uniformly obtained for processing. As a result, the display effects of the processed images were inconsistent, which led to a decrease in the management efficiency of the smart park. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a real-time data acquisition and processing method for a smart park based on edge computing. The park is divided into regions by analyzing the acquisition power of acquisition equipment at different locations in the park, and image processing methods for different divided areas are selected according to the sensor status and environmental differences of different areas to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The real-time data collection and processing method of the smart park based on edge computing includes the following steps:

[0008] Step S1: Obtain attribute data and location information of each collection device in the park, set up a collection power scoring mechanism, and use the attribute data of the collection devices to score the collection power of different collection devices. The park is divided into regions based on the collection power score of each collection device and the location information;

[0009] Step S2: Perform real-time detection on the acquisition delay and the number of failures of the acquisition equipment in each divided area, calculate the regional real-time coverage rate of each divided area using a polynomial regression algorithm, and classify each divided area into a high real-time coverage area or a low real-time coverage area according to the regional real-time coverage rate;

[0010] Step S3: Mark the low real-time coverage area, obtain the regional image of the marked area, detect the amount of dust in each marked area, set the analysis time to collect the humidity of each marked area and calculate the humidity transfer rate;

[0011] Step S4: The spatial dust content and humidity transfer rate of each regional image are comprehensively analyzed and the geometric mean method is used to determine whether to use the enhanced edge computing algorithm to process the regional image.

[0012] In a preferred embodiment, in step S1, the attribute data and point information of each collection device are called in the local file system of the park. The point information is the coordinates of the collection device in the park, and the attribute data is the collection area and working time of each collection device.

[0013] In a preferred embodiment, in step S1, fuzzy reasoning is used to determine the collection efficiency scores of different collection devices based on the collection area and working time of each collection device. The specific steps are as follows:

[0014] Input processing: Use the Z-score standardization algorithm to process the collection area and working time of each collection device;

[0015] Input definition: The standardized results of the collection area and working hours of each collection device are defined as input variables and divided into different fuzzy sets;

[0016] Output definition: The collection force score of each collection device is defined as the output variable;

[0017] Formulate rules: formulate fuzzy rules to describe the impact of different inputs on outputs;

[0018] Perform fuzzy reasoning: set the collection ability score of the collection device to a high score or a low score according to the output result.

[0019] In a preferred embodiment, in step S1, the park is divided into regions according to the collection ability score of each collection device and the point information setting division rules as follows:

[0020] A division balance threshold is set, and n adjacent high-scoring collection devices and a corresponding number of low-scoring collection devices are randomly assigned to the same division area until the entire park is covered. The site scope of each division area is determined using the positioning information of each collection device.

[0021] In a preferred embodiment, in step S2, the time from data collection to data transmission to the host computer for different types of data collection devices is calculated, and the number of failures is obtained by performing statistical counting on the data collection devices in each divided area after fault determination. The specific steps are as follows:

[0022] In each divided area, the collection device stores the real-time uploaded data into a temporary database. When making a fault judgment, the amount of space occupied by the stored data in the temporary database is detected. When the amount of space occupied by the stored data stops increasing, the current time point is recorded to obtain the first time point. When the amount of space occupied by the stored data starts to increase, the current time point is recorded to obtain the second time point. The second time point is subtracted from the first time point to obtain the fault time. When the fault time of the collection device exceeds the preset fault threshold, the collection device is judged to be faulty, and a count is performed. The fault counts of all collection devices in each divided area are counted as the number of faults in the corresponding divided area.

[0023] In a preferred embodiment, in step S2, after obtaining the acquisition delay and the number of failures of the acquisition equipment in each divided area, a polynomial regression algorithm is used to calculate the regional real-time coverage of each divided area. The specific steps are as follows:

[0024] The acquisition delay and the number of failures of the acquisition equipment in each divided area are used as input to construct a polynomial regression model, and its formula is: Among them, L is the calculation result of the polynomial regression model, c is the adjustment parameter, a is the acquisition delay of the acquisition equipment in the divided area, and b is the number of faults in the divided area. and The input weights of the two inputs, namely, the collection delay of the preset collection equipment and the number of faults, are used as the regional real-time coverage rate of each divided area;

[0025] The average of the regional real-time coverage rates of all divided areas is calculated as the regional coverage threshold, and the divided areas whose real-time coverage rates exceed the regional coverage threshold are classified as high real-time coverage areas, otherwise, they are classified as low real-time coverage areas.

[0026] In a preferred embodiment, in step S3, a period of time is selected as the analysis time, and the humidity of each marked area is collected during the analysis time;

[0027] The analysis time is divided into N detection times. The humidity of the marked area is collected during each detection time. The maximum and minimum humidity values within the N detection times are selected for subtraction, and the subtraction result is used as the humidity transfer rate of the corresponding marked area.

[0028] In a preferred embodiment, in step S4, the spatial dust content and humidity transfer rate of each region image are processed using a logarithmic transformation normalization formula: , where x is the amount of dust or the humidity transfer rate of each regional image, and y is the result of the logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image. The logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image are used as the influencing parameters 1 and 2 of whether to adopt the enhanced edge computing algorithm.

[0029] The geometric mean method is used to comprehensively analyze the influencing parameters 1 and 2 to determine whether the current area image should be processed by the enhanced edge computing algorithm. The specific steps are as follows:

[0030] The geometric mean result is calculated by taking the influencing parameter 1 and the influencing parameter 2 as inputs through the geometric mean method: , where k is the influencing parameter 1, z is the influencing parameter 2, and h is the geometric mean result;

[0031] When the geometric mean result exceeds the preset enhancement threshold, the current area image is processed using the enhanced edge computing algorithm, otherwise, it is processed using the ordinary edge computing algorithm; random selection is made from the area images of all marked areas for judgment until all images are traversed.

[0032] The real-time data acquisition and processing system for smart parks based on edge computing is used to implement the above-mentioned real-time data acquisition and processing method for smart parks based on edge computing, including a data acquisition module, a collection power scoring module, a coverage analysis module, and an image processing module;

[0033] The data collection module is used to obtain the attribute data and point information of each collection device in the park and transmit it to the collection force scoring module; after receiving the divided area, the collection delay and fault number of the collection equipment in each divided area are detected in real time, and the detection results are transmitted to the coverage analysis module;

[0034] The collection capacity scoring module is used to receive the attribute data and point information of each collection device, score the collection capacity of different collection devices, divide the park into regions, and send each divided region to the data collection module;

[0035] The coverage analysis module calculates the real-time coverage of each divided area based on the acquisition delay and fault count of the acquisition equipment in each divided area, classifies the different divided areas, selects and marks the areas with low real-time coverage, collects regional images of the marked areas, and detects the dust content and humidity in the marked areas. The regional images of the marked areas and the detection results are transmitted to the image processing module;

[0036] The image processing module determines whether to use the enhanced edge computing algorithm to process the regional images of different marked areas based on the detection results.

[0037] The technical effects and advantages of the edge computing-based smart park real-time data collection and processing method and system of the present invention are as follows:

[0038] The present invention obtains the attribute data and point information of each collection device in the park, sets up a collection force scoring mechanism to score the collection force of different collection devices, divides the park into regions according to the collection force score and point information of each collection device, evaluates the collection force to screen out key detection areas, performs real-time detection on the detection area and the number of faults of the collection equipment in each divided area and calculates the regional real-time coverage rate of each divided area, classifies each divided area according to the regional real-time coverage rate, obtains regional images of low real-time coverage areas, which is used to reduce the judgment direction and improve processing efficiency, collects the amount of spatial dust in low real-time coverage areas, sets analysis time to collect the humidity of each regional image and calculates the humidity transfer rate, and determines whether the corresponding area uses an enhanced edge computing algorithm to process the regional image, thereby accurately locating the park data source and improving data collection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the real-time data collection and processing method for a smart park based on edge computing in the present invention.

[0040] Figure 2 This is a flow chart of the real-time data acquisition and processing system for smart parks based on edge computing in the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0042] The present invention obtains the attribute data and point information of each collection device in the park, sets up a collection force scoring mechanism to score the collection force of different collection devices, divides the park into regions according to the collection force score and point information of each collection device, performs real-time detection on the detection area and the number of faults of the collection device in each divided area and calculates the regional real-time coverage rate of each divided area, classifies each divided area according to the regional real-time coverage rate, obtains regional images of low real-time coverage areas, collects the amount of spatial dust in low real-time coverage areas, sets analysis time to collect the humidity of each regional image and calculates the humidity transfer rate, and determines whether the corresponding area uses an enhanced edge computing algorithm to process the regional image, thereby accurately locating the park data source and improving data collection efficiency.

[0043] Example 1, a real-time data collection and processing method for a smart park based on edge computing, such as Figure 1 As shown, the following steps are included:

[0044] Step S1: Obtain attribute data and location information of each collection device in the park, set up a collection power scoring mechanism, and use the attribute data of the collection devices to score the collection power of different collection devices. The park is divided into regions based on the collection power score of each collection device and the location information;

[0045] Step S2: Perform real-time detection on the acquisition delay and the number of failures of the acquisition equipment in each divided area, calculate the regional real-time coverage rate of each divided area using a polynomial regression algorithm, and classify each divided area into a high real-time coverage area or a low real-time coverage area according to the regional real-time coverage rate;

[0046] Step S3: Mark the low real-time coverage area, obtain the regional image of the marked area, detect the amount of dust in each marked area, set the analysis time to collect the humidity of each marked area and calculate the humidity transfer rate;

[0047] Step S4: The spatial dust content and humidity transfer rate of each regional image are comprehensively analyzed and the geometric mean method is used to determine whether to use the enhanced edge computing algorithm to process the regional image.

[0048] The specific implementation is as follows:

[0049] In step S1, the attribute data and location information of each collection device are called in the local file system of the park. The location information is the coordinates of the collection device in the park, and the attribute data is the collection area and working time of each collection device.

[0050] Fuzzy reasoning is used to determine the collection power scores of different collection devices based on the collection area and working hours of each collection device. The specific steps are as follows:

[0051] Input processing: Use the Z-score standardization algorithm to process the collection area and working time of each collection device: ,in, is the collection area or working time of the collection equipment, is the average value of the collection area or working time of all collection devices, is the standard deviation of the collection area or working time of all collection devices, The results are standardized for the collection area or working time of the collection equipment;

[0052] Input definition: Define the standardized results of the collection area and the standardized results of the working hours of each collection device as input variables and divide them into different fuzzy sets. For example, "Big" and "Small" correspond to the standardized results of the collection area, and "Long" and "Short" correspond to the standardized results of the working hours.

[0053] Output definition: Define the collection ability score of each collection device as the output variable. For example, "High" and "Low" correspond to the collection ability score of each collection device.

[0054] Formulate rules: Formulate fuzzy rules to describe the impact of different inputs on outputs. For example, mark the result after the collection area is standardized as M, mark the result after the working time is standardized as G, and mark the collection force score of the collection equipment as P, then you can define

[0055] Rule1: IF (M is Big) AND (G is Long) THEN (P is High)

[0056] Rule2: IF (M is Small) AND (G is Short) THEN (P is Low)

[0057] Perform fuzzy reasoning: Set the acquisition power score of the acquisition device to a high score or a low score based on the output result; when the output result is "High", the corresponding acquisition device is judged to have a high score; when the output result is "Low", the corresponding acquisition device is judged to have a low score.

[0058] It should be noted that the classification categories of fuzzy sets can be adjusted according to actual conditions. This example takes two fuzzy sets as an example. In actual operation, three or more fuzzy sets can be selected for classification. In addition, the high and low judgments of the input variables in this example can be judged by setting the accuracy threshold. For example, when the standardized result of the collection area of the collection device exceeds the average value of the standardized results of the collection area of all collection devices, the device will be calibrated as "Big". The accuracy threshold of the standardized result of the working time of the collection device is judged in the same way, and no further analysis will be given here.

[0059] The park is divided into areas according to the collection power score of each collection device and the point information setting division rules as follows:

[0060] A division balance threshold is set, and n adjacent high-scoring collection devices and a corresponding number of low-scoring collection devices are randomly assigned to the same division area until the entire park is covered. The site scope of each division area is determined using the positioning information of each collection device.

[0061] It should be noted that the number n of adjacent high-scoring collection devices and the corresponding number of low-scoring collection devices in each divided area is not unique. For example, n is set to 4, etc., which will not be described in detail here.

[0062] In step S2, the acquisition delay of the acquisition device is the time from the acquisition of data by different models of acquisition devices in the model database to the data transmission to the host computer. The number of faults is obtained by counting the acquisition devices in each divided area after fault determination. The specific steps are as follows:

[0063] In each divided area, the collection device stores the real-time uploaded data into a temporary database. When making a fault judgment, the amount of space occupied by the stored data in the temporary database is detected. When the amount of space occupied by the stored data stops increasing, the current time point is recorded to obtain the first time point. When the amount of space occupied by the stored data starts to increase, the current time point is recorded to obtain the second time point. The second time point is subtracted from the first time point to obtain the fault time. When the fault time of the collection device exceeds the preset fault threshold, the collection device is judged to be faulty, and a count is performed. The fault counts of all collection devices in each divided area are counted as the number of faults in the corresponding divided area.

[0064] It should be noted that the acquisition order must be followed when obtaining the first time point and the second time point. The acquisition mechanism of the second time point will be triggered only after the first time point is obtained and recorded. The above-mentioned fault threshold is set by professionals in this field and will not be analyzed here.

[0065] The longer the collection delay of the collection equipment in the divided area, the lower the real-time detection efficiency of the corresponding divided area, and the lower the regional real-time coverage rate of the divided area; the more the number of faults of the collection equipment in the divided area, the lower the real-time detection efficiency of the corresponding divided area, and the lower the regional real-time coverage rate of the divided area;

[0066] After obtaining the acquisition delay and fault count of the acquisition equipment in each divided area, the polynomial regression algorithm is used to calculate the real-time coverage of each divided area. The specific steps are as follows:

[0067] The acquisition delay and the number of failures of the acquisition equipment in each divided area are used as input to construct a polynomial regression model, and its formula is: Among them, L is the calculation result of the polynomial regression model, c is the adjustment parameter, a is the acquisition delay of the acquisition equipment in the divided area, and b is the number of faults in the divided area. and The input weights of the two inputs, namely, the acquisition delay and the number of failures of the acquisition equipment, are used as the regional real-time coverage of each divided area. It should be explained that since the higher the acquisition delay or failure rate of the acquisition equipment in the divided area, the lower the regional real-time coverage of the corresponding divided area, the input weights of the two inputs are negative. For example, and The parameters are set to -0.4 and -0.6 respectively, and are used to adjust the real-time coverage of the divided areas to between 0 and 1.

[0068] The average of the regional real-time coverage rates of all divided areas is calculated as the regional coverage threshold, and the divided areas whose real-time coverage rates exceed the regional coverage threshold are classified as high real-time coverage areas, and the divided areas whose real-time coverage rates are lower than the regional coverage threshold are classified as low real-time coverage areas.

[0069] In step S3, for areas with low real-time coverage, which require focused monitoring, the low real-time coverage areas are marked, and regional images of the marks and areas are acquired through a collection device. The spatial dust volume of different marked areas is received from the dust sensor, and a period of time is selected as the analysis time. The humidity of each marked area is collected during the analysis time.

[0070] The analysis time is divided into N detection times. The humidity of the marked area is collected during each detection time. The maximum and minimum humidity values within the N detection times are selected and subtracted. The subtraction result is used as the humidity transfer rate of the corresponding marked area.

[0071] It should be noted that the dust sensor is a device that detects and measures the concentration of dust particles in the air. In this example, it is used to collect the amount of spatial dust in the marked area. When the amount of spatial dust is high, the regional image of the corresponding marked area is more blurred during acquisition, the humidity transfer rate is greater, and the difference between the regional images of the corresponding marked area collected at different detection times is greater. The time interval of the detection time can be set according to actual conditions. For example, the time interval of the detection time is set to 3 seconds. When calculating the humidity transfer rate of the marked area, the humidity of the marked area is collected once within the time interval of each detection time.

[0072] In step S4, the spatial dust content and humidity transfer rate of each area image are normalized using the logarithmic transformation normalization formula: , where x is the amount of dust or the humidity transfer rate of each regional image, and y is the result of the logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image. The logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image are used as the influencing parameters 1 and 2 of whether to adopt the enhanced edge computing algorithm.

[0073] The geometric mean method is used to comprehensively analyze the influencing parameters 1 and 2 to determine whether the current area image should be processed by the enhanced edge computing algorithm. The specific steps are as follows:

[0074] The geometric mean result is calculated by taking the influencing parameter 1 and the influencing parameter 2 as inputs through the geometric mean method: , where k is the influencing parameter 1, z is the influencing parameter 2, and h is the geometric mean result. The geometric mean result is compared with the preset enhancement threshold to determine whether the current area image should be processed by the enhanced edge calculation algorithm;

[0075] When the geometric mean result exceeds the enhanced threshold, the current area image is processed using the enhanced edge computing algorithm, otherwise, it is processed using the ordinary edge computing algorithm; random selection is made from the area images of all marked areas for judgment until all images are traversed.

[0076] It should be noted that the image processing method based on the ordinary edge computing algorithm is an existing technology. The enhanced edge computing algorithm removes image noise and image redundant data on the basis of the ordinary edge computing algorithm. The specific processing steps are performed by professionals in this field.

[0077] Example 2, a real-time data acquisition and processing system for a smart park based on edge computing, such as Figure 2 As shown, it includes a data acquisition module, an acquisition force scoring module, a coverage analysis module and an image processing module;

[0078] The data collection module is used to obtain the attribute data and point information of each collection device in the park and transmit it to the collection force scoring module; after receiving the divided area, the collection delay and fault number of the collection equipment in each divided area are detected in real time, and the detection results are transmitted to the coverage analysis module;

[0079] The collection capacity scoring module is used to receive the attribute data and point information of each collection device, score the collection capacity of different collection devices, divide the park into regions, and send each divided region to the data collection module;

[0080] The coverage analysis module calculates the real-time coverage of each divided area based on the acquisition delay and fault count of the acquisition equipment in each divided area, classifies the different divided areas, selects and marks the areas with low real-time coverage, collects regional images of the marked areas, and detects the dust content and humidity in the marked areas. The regional images of the marked areas and the detection results are transmitted to the image processing module;

[0081] The image processing module determines whether to use the enhanced edge computing algorithm to process the regional images of different marked areas based on the detection results.

[0082] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0083] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0085] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0086] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time data collection and processing method for a smart park based on edge computing is characterized by: The following steps are included: Step S1: Obtain attribute data and location information of each collection device in the park, set up a collection power scoring mechanism, and use the attribute data of the collection devices to score the collection power of different collection devices. The park is divided into regions based on the collection power score of each collection device and the location information; Step S2: Perform real-time detection on the acquisition delay and the number of failures of the acquisition equipment in each divided area, calculate the regional real-time coverage rate of each divided area using a polynomial regression algorithm, and classify each divided area into a high real-time coverage area or a low real-time coverage area according to the regional real-time coverage rate; Step S3: Mark the low real-time coverage area, obtain the regional image of the marked area, detect the amount of dust in each marked area, set the analysis time to collect the humidity of each marked area and calculate the humidity transfer rate; Step S4: The spatial dust content and humidity transfer rate of each regional image are comprehensively analyzed and the geometric mean method is used to determine whether to use the enhanced edge computing algorithm to process the regional image.

2. The method for real-time data collection and processing in a smart park based on edge computing according to claim 1 is characterized in that: In step S1, the attribute data and point information of each collection device are called in the local file system of the park. The point information is the coordinates of the collection device in the park, and the attribute data is the collection area and working time of each collection device.

3. The method for real-time data collection and processing in a smart park based on edge computing according to claim 2 is characterized in that: In step S1, the collection area and working time of each collection device are comprehensively considered and fuzzy reasoning is used to determine the collection force scores of different collection devices. The specific steps are as follows: Input processing: Use the Z-score standardization algorithm to process the collection area and working time of each collection device; Input definition: The standardized results of the collection area and working hours of each collection device are defined as input variables and divided into different fuzzy sets; Output definition: The collection force score of each collection device is defined as the output variable; Formulate rules: formulate fuzzy rules to describe the impact of different inputs on outputs; Perform fuzzy reasoning: set the collection ability score of the collection device to a high score or a low score according to the output result.

4. The method for real-time data collection and processing in a smart park based on edge computing according to claim 3 is characterized in that: In step S1, the park is divided into regions according to the collection power score of each collection device and the point information setting division rules as follows: A division balance threshold is set, and n adjacent high-scoring collection devices and a corresponding number of low-scoring collection devices are randomly assigned to the same division area until the entire park is covered. The site scope of each division area is determined using the positioning information of each collection device.

5. The method for real-time data collection and processing in a smart park based on edge computing according to claim 1 is characterized in that: In step S2, the time from data collection to data transmission to the host computer for different types of data collection devices is calculated. The number of failures is obtained by performing statistical counting on the data collection devices in each divided area after fault determination. The specific steps are as follows: In each divided area, the collection device stores the real-time uploaded data into a temporary database. When making a fault judgment, the amount of space occupied by the stored data in the temporary database is detected. When the amount of space occupied by the stored data stops increasing, the current time point is recorded to obtain the first time point. When the amount of space occupied by the stored data starts to increase, the current time point is recorded to obtain the second time point. The second time point is subtracted from the first time point to obtain the fault time. When the fault time of the collection device exceeds the preset fault threshold, the collection device is judged to be faulty, and a count is performed. The fault counts of all collection devices in each divided area are counted as the number of faults in the corresponding divided area.

6. The method for real-time data collection and processing in a smart park based on edge computing according to claim 5 is characterized in that: In step S2, after obtaining the acquisition delay and the number of failures of the acquisition equipment in each divided area, the real-time coverage rate of each divided area is calculated using a polynomial regression algorithm. The specific steps are as follows: The acquisition delay and the number of failures of the acquisition equipment in each divided area are used as input to construct a polynomial regression model, and its formula is: Among them, L is the calculation result of the polynomial regression model, c is the adjustment parameter, a is the acquisition delay of the acquisition equipment in the divided area, and b is the number of faults in the divided area. and The input weights of the two inputs, namely, the collection delay of the preset collection equipment and the number of faults, are used as the regional real-time coverage rate of each divided area; The average of the regional real-time coverage rates of all divided areas is calculated as the regional coverage threshold, and the divided areas whose real-time coverage rates exceed the regional coverage threshold are classified as high real-time coverage areas, otherwise, they are classified as low real-time coverage areas.

7. The method for real-time data collection and processing in a smart park based on edge computing according to claim 1 is characterized in that: In step S3, a period of time is selected as analysis time, and the humidity of each marked area is collected during the analysis time; The analysis time is divided into N detection times. The humidity of the marked area is collected during each detection time. The maximum and minimum humidity values within the N detection times are selected for subtraction, and the subtraction result is used as the humidity transfer rate of the corresponding marked area.

8. The method for real-time data collection and processing in a smart park based on edge computing according to claim 7 is characterized in that: In step S4, the spatial dust content and humidity transfer rate of each area image are processed using the logarithmic transformation normalization formula: , where x is the amount of dust or the humidity transfer rate of each regional image, and y is the result of the logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image. The logarithmic transformation and normalization of the amount of dust or the humidity transfer rate of each regional image are used as the influencing parameters 1 and 2 of whether to adopt the enhanced edge computing algorithm. The geometric mean method is used to comprehensively analyze the influencing parameters 1 and 2 to determine whether the current area image should be processed by the enhanced edge computing algorithm. The specific steps are as follows: The geometric mean result is calculated by taking the influencing parameter 1 and the influencing parameter 2 as inputs through the geometric mean method: , where k is the influencing parameter 1, z is the influencing parameter 2, and h is the geometric mean result; When the geometric mean result exceeds the preset enhancement threshold, the current area image is processed using the enhanced edge computing algorithm, otherwise, it is processed using the ordinary edge computing algorithm; random selection is made from the area images of all marked areas for judgment until all images are traversed.

9. A real-time data acquisition and processing system for a smart park based on edge computing, based on the real-time data acquisition and processing method for a smart park based on edge computing according to any one of claims 1 to 8, characterized in that: It includes data acquisition module, acquisition force scoring module, coverage analysis module and image processing module; The data collection module is used to obtain the attribute data and point information of each collection device in the park and transmit it to the collection force scoring module; after receiving the divided area, the collection delay and fault number of the collection equipment in each divided area are detected in real time, and the detection results are transmitted to the coverage analysis module; The collection capacity scoring module is used to receive the attribute data and point information of each collection device, score the collection capacity of different collection devices, divide the park into regions, and send each divided region to the data collection module; The coverage analysis module calculates the real-time coverage of each divided area based on the acquisition delay and fault count of the acquisition equipment in each divided area, classifies the different divided areas, selects and marks the areas with low real-time coverage, collects regional images of the marked areas, and detects the dust content and humidity in the marked areas. The regional images of the marked areas and the detection results are transmitted to the image processing module; The image processing module determines whether to use the enhanced edge computing algorithm to process the regional images of different marked areas based on the detection results.

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