Method and system for detecting operation safety of amusement equipment based on image signal identification

Abstract

The invention discloses a method and system for detecting the operation safety of amusement equipment based on image signal identification, and the method comprises the following steps: obtaining the image signal data information of the amusement equipment, and carrying out the enhancement preprocessing of the image signal; performing amusement equipment image segmentation processing on an image signal enhancement preprocessing result; performing amusement equipment feature recognition extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model; based on the amusement equipment operation state model, performing amusement equipment feature recognition and extraction processing results and generating a corresponding amusement equipment feature map, and performing amusement equipment part classification and comparison processing; performing amusement equipment operation state judgment processing on the amusement equipment part classification processing result based on an amusement equipment operation state model; the amusement equipment image enhancement segmentation processing is performed on the amusement equipment image signal, the feature recognition extraction processing and the amusement equipment part classification comparison processing are performed, and thus the operation state of the amusement equipment is accurately judged.

Description

Technical Field

[0001] The present invention relates to the technical field of amusement equipment, and in particular to a method and system for detecting the operation safety of amusement equipment based on image signal recognition. Background Art

[0002] At present, the safe operation status of amusement equipment is mostly determined by collecting voltage and current signals of the amusement equipment, collecting image signals of the amusement equipment, and extracting seat belt features to monitor whether tourists have fastened their seat belts in real time, and judging the safe operation status of the amusement equipment based on the collected voltage, current and image signals of the amusement equipment. This method of judging the safe operation status of amusement equipment by voltage, current and image signals cannot clearly understand the operation status of the amusement equipment and its components in each time period, and does not conduct real-time full-time monitoring of the speed and acceleration of the amusement equipment in different areas, so it cannot accurately judge the safe operation status of the amusement equipment. Summary of the invention

[0003] In order to overcome the shortcomings of the prior art, the present invention provides a method and system for detecting the safe operation of amusement equipment based on image signal recognition, which realizes the accurate judgment of the operating status of the amusement equipment by performing amusement equipment image enhancement and segmentation processing on the amusement equipment image signal, and performing feature recognition and extraction processing and amusement equipment component classification and comparison processing.

[0004] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present application provides a method for detecting the safe operation of an amusement device based on image signal recognition, comprising the following steps:

[0006] S101, obtaining image signal data information of an amusement device, and performing image signal enhancement preprocessing;

[0007] S102, performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image;

[0008] S103, performing amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generating a corresponding amusement equipment feature map;

[0009] S104, based on the amusement equipment operation state model, the amusement equipment feature recognition and extraction processing results are generated and the corresponding amusement equipment feature map is generated, and the amusement equipment parts are classified and compared;

[0010] S105 , performing amusement equipment operation status determination processing on the amusement equipment component classification processing result based on the amusement equipment operation status model.

[0011] Furthermore, the image signal data information of the amusement equipment includes carousel type amusement equipment, rotating aircraft type amusement equipment, rail train type amusement equipment, trampoline swing type amusement equipment, bumper car type amusement equipment, overhead tour bus type amusement equipment, and pirate ship type amusement equipment.

[0012] Furthermore, obtaining the image signal data information of the amusement equipment and performing image signal enhancement preprocessing includes the following steps:

[0013] Perform image filtering processing on the image signal of the amusement equipment;

[0014] Performing image histogram equalization processing on the image signal of the amusement equipment processed by image filtering;

[0015] Divide the amusement equipment image signal processed by image histogram equalization into several regions, and calculate the average brightness of the regions;

[0016] The image signal of the amusement equipment processed by calculating the average brightness of the area is subjected to image normalization processing.

[0017] Further, performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain the amusement equipment image local segmentation processing result includes the following steps:

[0018] Based on the gray value edge detection, the image signal is enhanced and preprocessed to identify the background area and the amusement equipment area;

[0019] Perform image segmentation on the background area and the amusement equipment area to remove the background area;

[0020] Perform local segmentation of the amusement equipment image in the amusement equipment area.

[0021] Furthermore, the construction of the amusement equipment operation status model includes the following steps:

[0022] Obtaining historical data information on normal operation status of amusement equipment and historical data information on abnormal operation status of amusement equipment, and performing image preprocessing of amusement equipment;

[0023] Performing feature recognition and extraction processing on the amusement equipment image preprocessing results;

[0024] Perform classification and comparison of amusement equipment parts based on the amusement equipment feature recognition and extraction processing results;

[0025] Based on the classification and comparison results of the amusement equipment parts, the initial model of the amusement equipment is trained;

[0026] If the model training results are the same as the training sample results, the model training is completed; if the model training results are different from the training sample results, the model training is repeated.

[0027] Further, performing amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generating a corresponding amusement equipment feature map includes the following steps:

[0028] Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image;

[0029] Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result;

[0030] Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components;

[0031] A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

[0032] Further, based on the amusement equipment operation state model, the amusement equipment feature recognition and extraction processing results are processed and the corresponding amusement equipment feature map is generated, and the amusement equipment component classification and comparison processing is performed, which includes the following steps:

[0033] Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment;

[0034] Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map;

[0035] Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

[0036] Further, the amusement equipment operation state determination process based on the amusement equipment component classification comparison process results includes the following steps:

[0037] If the speed map and acceleration map of the active component type of the amusement equipment contain speed thresholds and acceleration thresholds greater than or equal to the corresponding set speed thresholds of the active component, the operating status of the active component type of the amusement equipment is judged to be abnormal; otherwise, the operating status of the active component type of the amusement equipment is judged to be normal.

[0038] If the amplitude spectrum of the fixed component type of the amusement equipment contains an amplitude greater than or equal to the corresponding set amplitude threshold of the fixed component, the operation state of the fixed component type of the amusement equipment is judged to be abnormal; otherwise, the operation state of the fixed component type of the amusement equipment is judged to be normal.

[0039] The second aspect of the present application provides a system for detecting the operation safety of an amusement device based on image signal recognition, comprising:

[0040] A data acquisition unit, used to acquire image signal data information of the amusement equipment;

[0041] A model building unit, used for building an operation status model of the amusement equipment;

[0042] A first data processing unit, used for performing image signal enhancement preprocessing on the image signal data information of the amusement equipment;

[0043] The second data processing unit is used to perform amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image;

[0044] A third data processing unit is used to perform amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generate a corresponding amusement equipment feature map;

[0045] A fourth data processing unit is used to identify and extract the amusement equipment feature based on the amusement equipment operation state model, generate a corresponding amusement equipment feature map, and perform a classification and comparison process on the amusement equipment components;

[0046] The fifth data processing unit is used to perform amusement equipment operation state determination processing on the amusement equipment component classification processing result based on the amusement equipment operation state model.

[0047] Furthermore, the first data processing unit performs image signal enhancement preprocessing on the image signal data information of the amusement device, including:

[0048] Perform image filtering processing on the image signal of the amusement equipment;

[0049] Performing image histogram equalization processing on the image signal of the amusement equipment processed by image filtering;

[0050] Divide the amusement equipment image signal processed by image histogram equalization into several regions, and calculate the average brightness of the regions;

[0051] The image signal of the amusement equipment processed by calculating the average brightness of the area is subjected to image normalization processing.

[0052] Furthermore, the second data processing unit performs amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain the amusement equipment image local segmentation processing result including:

[0053] Based on the gray value edge detection, the image signal is enhanced and preprocessed to identify the background area and the amusement equipment area;

[0054] Perform image segmentation on the background area and the amusement equipment area to remove the background area;

[0055] Perform local segmentation of the amusement equipment image in the amusement equipment area.

[0056] Furthermore, the model building unit is used to build an amusement equipment operation status model, and the amusement equipment operation status model building includes:

[0057] Obtaining historical data information on normal operation status of amusement equipment and historical data information on abnormal operation status of amusement equipment, and performing image preprocessing of amusement equipment;

[0058] Performing feature recognition and extraction processing on the amusement equipment image preprocessing results;

[0059] Perform classification and comparison of amusement equipment parts based on the amusement equipment feature recognition and extraction processing results;

[0060] Based on the classification and comparison results of the amusement equipment parts, the initial model of the amusement equipment is trained;

[0061] If the model training results are the same as the training sample results, the model training is completed; if the model training results are different from the training sample results, the model training is repeated.

[0062] Furthermore, the third data processing unit performs amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generates a corresponding amusement equipment feature map including:

[0063] Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image;

[0064] Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result;

[0065] Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components;

[0066] A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

[0067] Furthermore, the fourth data processing unit extracts and processes the amusement equipment feature recognition results based on the amusement equipment operation state model and generates a corresponding amusement equipment feature map, and performs a classification and comparison process on the amusement equipment components, including:

[0068] Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment;

[0069] Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map;

[0070] Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

[0071] Furthermore, the fifth data processing unit performs a process of determining the operation state of the amusement equipment on the amusement equipment component classification process result based on the amusement equipment operation state model, including:

[0072] If the speed map and acceleration map of the active component type of the amusement equipment contain speed thresholds and acceleration thresholds greater than or equal to the corresponding set speed thresholds of the active component, the operating status of the active component type of the amusement equipment is judged to be abnormal; otherwise, the operating status of the active component type of the amusement equipment is judged to be normal.

[0073] If the amplitude spectrum of the fixed component type of the amusement equipment contains an amplitude greater than or equal to the corresponding set amplitude threshold of the fixed component, the operation state of the fixed component type of the amusement equipment is judged to be abnormal; otherwise, the operation state of the fixed component type of the amusement equipment is judged to be normal.

[0074] The beneficial effects of the present application are as follows: compared with the prior art which collects image signals of amusement equipment to extract seat belt features to identify whether tourists have fastened their seat belts, and combines the voltage signal and current signal of the amusement equipment to judge the operation status of the amusement equipment, the present application achieves the goal of performing image enhancement and segmentation processing on the image signals of amusement equipment to obtain feature image signals of amusement equipment components with clear contours, and performs feature recognition and extraction processing to generate corresponding feature maps of amusement equipment and classification and comparison processing of amusement equipment components, thereby accurately judging the operation status of the amusement equipment.

[0075] It is achieved by performing speed calculation and amplitude calculation on the line segment area of ​​the feature marking processing results of the amusement equipment components and generating the corresponding amusement equipment feature map. Based on the amusement equipment operation status model, the amusement equipment feature identification and extraction processing results and the generated corresponding amusement equipment feature map are classified and compared to perform the amusement equipment component classification processing, and the operation status of the amusement equipment in each line segment area is monitored in real time, thereby greatly improving the accuracy of judging the operation status of the amusement equipment.

[0076] The image filtering process is realized for the image signal of the amusement equipment, and the image quality of the amusement equipment is improved. The image histogram equalization process is performed on the image signal of the amusement equipment, and the image signal of the amusement equipment with uniform gray value distribution is obtained, so that the image signal of the amusement equipment has more data information. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0078] Figure 1 This is a schematic diagram of the steps of a method for detecting the operation safety of an amusement device based on image signal recognition according to the present invention:

[0079] Figure 2 It is a structural schematic diagram of a system for detecting the operation safety of an amusement device based on image signal recognition according to the present invention. DETAILED DESCRIPTION

[0080] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0081] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0082] Embodiment 1:

[0083] A method for detecting the operation safety of an amusement device based on image signal recognition comprises the following steps:

[0084] S101, obtaining image signal data information of an amusement device, and performing image signal enhancement preprocessing;

[0085] The image signal data information of the amusement equipment is obtained based on the time series, and the image signal enhancement preprocessing is performed on the image signal data information of the amusement equipment to obtain the image signal enhancement preprocessing result. The image signal data information of the amusement equipment includes carousel amusement equipment, rotating aircraft amusement equipment, rail train amusement equipment, trampoline swing amusement equipment, bumper car amusement equipment, overhead tour bus amusement equipment, and pirate ship amusement equipment.

[0086] Acquiring the image signal data information of the amusement equipment and performing image signal enhancement preprocessing includes the following steps:

[0087] Perform image filtering processing on the image signal of the amusement equipment;

[0088] Performing image histogram equalization processing on the image signal of the amusement equipment processed by image filtering;

[0089] Divide the amusement equipment image signal processed by image histogram equalization into several regions, and calculate the average brightness of the regions;

[0090] The image signal of the amusement equipment processed by calculating the average brightness of the area is subjected to image normalization processing.

[0091] Perform image filtering on the image signal of the amusement equipment to improve the image quality of the amusement equipment. Perform image histogram equalization on the image signal of the amusement equipment to obtain an amusement equipment image signal with uniform grayscale value distribution, so that the amusement equipment image signal has more data information. By dividing the amusement equipment image signal processed by image histogram equalization into several regions, calculating the average brightness of the regions and normalizing the image, the image is adjusted to a uniform image size, which is beneficial to the subsequent amusement equipment image signal processing.

[0092] S102, performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image;

[0093] By performing image signal enhancement preprocessing on the image signal data information of the amusement equipment, an image signal enhancement preprocessing result is obtained, and by performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result, the image background area is removed to obtain the amusement equipment image local segmentation processing result.

[0094] Performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain the amusement equipment image local segmentation processing result includes the following steps:

[0095] Based on the gray value edge detection, the image signal is enhanced and preprocessed to identify the background area and the amusement equipment area;

[0096] Perform image segmentation on the background area and the amusement equipment area to remove the background area;

[0097] Perform local segmentation of the amusement equipment image in the amusement equipment area.

[0098] Based on the gray value edge detection, the image signal is enhanced and preprocessed, and the background area and the amusement equipment area are identified and processed. The background area and the amusement equipment area are identified, and image segmentation processing is performed on the background area and the amusement equipment area to obtain the background area and the amusement equipment area. The background area is removed to obtain the amusement equipment image signal of only the amusement equipment area, and the amusement equipment area is locally segmented to obtain the local segmentation processing result of the amusement equipment image.

[0099] For example, the image signal of the rail train type amusement equipment is subjected to the amusement equipment image segmentation processing. Based on the gray value edge detection, the rail train type background area and the rail train type amusement equipment area are identified on the image signal of the rail train type amusement equipment, and the rail train type background area and the rail train type amusement equipment area are identified. The image segmentation processing is performed on the rail train type background area and the rail train type amusement equipment area to obtain the rail train type background area and the rail train type amusement equipment area, and the rail train type background area is removed to obtain the rail train type amusement equipment image signal with only the rail train type amusement equipment area, and the amusement equipment image local segmentation processing is performed on the rail train type amusement equipment area to obtain the rail train type amusement equipment image local segmentation processing result.

[0100] S103, performing amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generating a corresponding amusement equipment feature map;

[0101] By performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result, the background area is removed to obtain the amusement equipment image signal of only the amusement equipment area, and then the amusement equipment area is subjected to amusement equipment image local segmentation processing to obtain the amusement equipment image local segmentation processing result. The historical data information of the normal operation status of the amusement equipment and the historical data information of the abnormal operation status of the amusement equipment are obtained, and an amusement equipment operation status model is constructed. Based on the amusement equipment operation status model, the amusement equipment feature recognition and extraction processing is performed on the amusement equipment image local segmentation processing result to obtain the amusement equipment feature recognition and extraction processing result, and the corresponding amusement equipment feature spectrum is generated, and the corresponding amusement equipment feature spectrum generated includes a velocity spectrum, an acceleration spectrum and an amplitude spectrum.

[0102] The construction of the amusement equipment operation status model includes the following steps:

[0103] Obtaining historical data information on normal operation status of amusement equipment and historical data information on abnormal operation status of amusement equipment, and performing image preprocessing of amusement equipment;

[0104] Performing feature recognition and extraction processing on the amusement equipment image preprocessing results;

[0105] Perform classification and comparison of amusement equipment parts based on the amusement equipment feature recognition and extraction processing results;

[0106] Based on the classification and comparison results of the amusement equipment parts, the initial model of the amusement equipment is trained;

[0107] If the model training results are the same as the training sample results, the model training is completed; if the model training results are different from the training sample results, the model training is repeated.

[0108] Based on the amusement equipment running state model, the amusement equipment feature recognition and extraction processing is performed on the amusement equipment image local segmentation processing result, and the corresponding amusement equipment feature map is generated, which includes the following steps:

[0109] Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image;

[0110] Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result;

[0111] Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components;

[0112] A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

[0113] For example, the local segmentation processing result of the image of the rail train type amusement equipment is processed by the feature recognition and extraction of the amusement equipment to obtain the feature recognition and extraction processing result of the rail train type amusement equipment. The features of the rail train type amusement equipment are processed by the feature marking of the amusement equipment components. The features of the rail train type amusement equipment include the features of the train type track, the features of the train type seat, the features of the train type speed and the features of the train type safety protection fixed components. The features of the train type track include the features of the track slope, the features of the track rotation, the features of the track amplitude and the features of the track route. The results of the feature marking processing of the components of the train type amusement equipment are processed by the line segment area speed calculation processing to obtain the results of the line segment area speed calculation processing of the train type amusement equipment, and the speed spectrum and the acceleration spectrum of the train type amusement equipment are generated according to the results of the line segment area speed calculation processing of the train type amusement equipment. The results of the feature marking processing are processed by the line segment area amplitude calculation processing, and the amplitude spectrum is generated based on the amplitude calculation processing results.

[0114] S104, based on the amusement equipment operation state model, the amusement equipment feature recognition and extraction processing results are generated and the corresponding amusement equipment feature map is generated, and the amusement equipment parts are classified and compared;

[0115] By performing feature recognition and extraction processing on the local segmentation processing result of the amusement equipment image based on the amusement equipment operation state model, and generating a corresponding amusement equipment feature map, a velocity map and an acceleration map, the amusement equipment feature recognition and extraction processing result is obtained, and by performing classification and comparison processing on the amusement equipment component classification and comparison processing on the amusement equipment feature recognition and extraction processing result based on the amusement equipment operation state model and generating a corresponding amusement equipment feature map, the amusement equipment component classification and comparison processing result is obtained.

[0116] Based on the amusement equipment operation state model, the amusement equipment feature recognition and extraction processing results are generated and the corresponding amusement equipment feature map is generated, and the amusement equipment component classification and comparison processing is performed, which includes the following steps:

[0117] Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment;

[0118] Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map;

[0119] Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

[0120] For example, by performing classification and comparison processing on the speed calculation processing results of the line segment area of ​​train-type amusement equipment, the classification and comparison processing results of the train-type amusement equipment components are obtained. The classification and comparison processing results of the train-type amusement equipment components include the types of active components of the amusement equipment and the types of fixed components of the amusement equipment. The types of active components of the amusement equipment include train-type seats and train-type safety protection fixed components. The types of fixed components of the amusement equipment include train-type tracks. By constructing the speed and acceleration constraints of the types of active components of the amusement equipment and constructing the vibration amplitude constraints of the types of fixed components of the amusement equipment, and comparing them with the speed spectrum and the acceleration spectrum, and comparing them with the amplitude spectrum, the speed spectrum and the acceleration spectrum are compared. The acceleration spectrum and the amplitude spectrum are compared.

[0121] S105, performing amusement equipment operation state determination processing on the amusement equipment component classification processing result based on the amusement equipment operation state model;

[0122] The amusement equipment component classification processing is performed on the amusement equipment feature recognition and extraction processing results based on the amusement equipment operation status model to obtain the amusement equipment component classification processing results, and the velocity spectrum, acceleration spectrum, and amplitude spectrum are compared and processed. The amusement equipment operation status is determined by performing amusement equipment component classification comparison processing results based on the amusement equipment operation status model to obtain the amusement equipment operation status determination processing results.

[0123] The following steps are included in the process of determining the operation status of the amusement equipment based on the amusement equipment operation status model:

[0124] If the speed map and acceleration map of the active component type of the amusement equipment contain speed thresholds and acceleration thresholds greater than or equal to the corresponding set speed thresholds of the active component, the operating status of the active component type of the amusement equipment is judged to be abnormal; otherwise, the operating status of the active component type of the amusement equipment is judged to be normal.

[0125] If the amplitude spectrum of the fixed component type of the amusement equipment contains an amplitude greater than or equal to the corresponding set amplitude threshold of the fixed component, the operation state of the fixed component type of the amusement equipment is judged to be abnormal; otherwise, the operation state of the fixed component type of the amusement equipment is judged to be normal.

[0126] For example, if the velocity spectrum and acceleration spectrum of a train seat or a train safety protection fixed component contain a velocity threshold and acceleration threshold greater than or equal to the corresponding set velocity threshold of the movable component, the running state of the train seat or the train safety protection fixed component is judged to be abnormal. If the amplitude spectrum of a train track contains an amplitude threshold greater than or equal to the corresponding set amplitude threshold of the fixed component, the running state of the train track is judged to be abnormal.

[0127] The above is a method for detecting the safe operation of an amusement device based on image signal recognition provided in an embodiment of the present application. The following is a system for detecting the safe operation of an amusement device based on image signal recognition provided in an embodiment of the present application.

[0128] A system for detecting the operation safety of an amusement device based on image signal recognition, comprising:

[0129] A data acquisition unit, used to acquire image signal data information of the amusement equipment;

[0130] A model building unit, used for building an operation status model of the amusement equipment;

[0131] A first data processing unit, used for performing image signal enhancement preprocessing on the image signal data information of the amusement equipment;

[0132] The second data processing unit is used to perform amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image;

[0133] A third data processing unit is used to perform amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generate a corresponding amusement equipment feature map;

[0134] A fourth data processing unit is used to identify and extract the amusement equipment feature based on the amusement equipment operation state model, generate a corresponding amusement equipment feature map, and perform a classification and comparison process on the amusement equipment components;

[0135] The fifth data processing unit is used to perform amusement equipment operation state determination processing on the amusement equipment component classification processing result based on the amusement equipment operation state model.

[0136] The data acquisition unit is used to acquire image signal data information of amusement equipment, which includes carousel type amusement equipment, rotating aircraft type amusement equipment, rail train type amusement equipment, trampoline swing type amusement equipment, bumper car type amusement equipment, overhead tour bus type amusement equipment, and pirate ship type amusement equipment.

[0137] The first data processing unit is used to perform image signal enhancement preprocessing on the image signal data information of the amusement device, and includes:

[0138] Perform image filtering processing on the image signal of the amusement equipment;

[0139] Performing image histogram equalization processing on the image signal of the amusement equipment processed by image filtering;

[0140] Divide the amusement equipment image signal processed by image histogram equalization into several regions, and calculate the average brightness of the regions;

[0141] The image signal of the amusement equipment processed by calculating the average brightness of the area is subjected to image normalization processing.

[0142] The second data processing unit is used to perform amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain the amusement equipment image local segmentation processing result, including:

[0143] Based on the gray value edge detection, the image signal is enhanced and preprocessed to identify the background area and the amusement equipment area;

[0144] Perform image segmentation on the background area and the amusement equipment area to remove the background area;

[0145] Perform local segmentation of the amusement equipment image in the amusement equipment area.

[0146] The model building unit is used to build the amusement equipment operation status model. The amusement equipment operation status model building includes:

[0147] Obtaining historical data information on normal operation status of amusement equipment and historical data information on abnormal operation status of amusement equipment, and performing image preprocessing of amusement equipment;

[0148] Performing feature recognition and extraction processing on the amusement equipment image preprocessing results;

[0149] Perform classification and comparison of amusement equipment parts based on the amusement equipment feature recognition and extraction processing results;

[0150] Based on the classification and comparison results of the amusement equipment parts, the initial model of the amusement equipment is trained;

[0151] If the model training results are the same as the training sample results, the model training is completed; if the model training results are different from the training sample results, the model training is repeated.

[0152] The third data processing unit is used to perform amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generate a corresponding amusement equipment feature map including:

[0153] Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image;

[0154] Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result;

[0155] Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components;

[0156] A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

[0157] The fourth data processing unit is used to extract and process the amusement equipment feature recognition results based on the amusement equipment operation state model and generate a corresponding amusement equipment feature map, and perform amusement equipment component classification and comparison processing, including:

[0158] Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment;

[0159] Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map;

[0160] Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

[0161] The fifth data processing unit is used to perform amusement equipment operation state determination processing on the amusement equipment component classification processing result based on the amusement equipment operation state model, including:

[0162] If the speed map and acceleration map of the active component type of the amusement equipment contain speed thresholds and acceleration thresholds greater than or equal to the corresponding set speed thresholds of the active component, the operating status of the active component type of the amusement equipment is judged to be abnormal; otherwise, the operating status of the active component type of the amusement equipment is judged to be normal.

[0163] If the amplitude spectrum of the fixed component type of the amusement equipment contains an amplitude greater than or equal to the corresponding set amplitude threshold of the fixed component, the operation state of the fixed component type of the amusement equipment is judged to be abnormal; otherwise, the operation state of the fixed component type of the amusement equipment is judged to be normal.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0165] The terms "first", "second" and "third" etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0169] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting the safe operation of an amusement device based on image signal recognition, characterized in that: The following steps are involved: S101, obtaining image signal data information of an amusement device, and performing image signal enhancement preprocessing; S102, performing amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image; S103, performing amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generating a corresponding amusement equipment feature map; S104, based on the amusement equipment operation state model, the amusement equipment feature recognition and extraction processing results are generated and the corresponding amusement equipment feature map is generated, and the amusement equipment parts are classified and compared; S105 , performing amusement equipment operation status determination processing on the amusement equipment component classification processing result based on the amusement equipment operation status model.

2. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The method of obtaining the image signal data information of the amusement equipment and performing image signal enhancement preprocessing comprises the following steps: Perform image filtering processing on the image signal of the amusement equipment; Performing image histogram equalization processing on the image signal of the amusement equipment processed by image filtering; Divide the amusement equipment image signal processed by image histogram equalization into several regions, and calculate the average brightness of the regions; The image signal of the amusement equipment processed by calculating the average brightness of the area is subjected to image normalization processing.

3. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The amusement equipment image segmentation processing is performed on the image signal enhancement preprocessing result to remove the background area and obtain the amusement equipment image local segmentation processing result, which includes the following steps: Based on the gray value edge detection, the image signal is enhanced and preprocessed to identify the background area and the amusement equipment area; Perform image segmentation on the background area and the amusement equipment area to remove the background area; Perform local segmentation of the amusement equipment image in the amusement equipment area.

4. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The construction of the amusement equipment operation status model includes the following steps: Obtaining historical data information on normal operation status of amusement equipment and historical data information on abnormal operation status of amusement equipment, and performing image preprocessing of amusement equipment; Performing feature recognition and extraction processing on the amusement equipment image preprocessing results; Perform classification and comparison of amusement equipment parts based on the amusement equipment feature recognition and extraction processing results; Based on the classification and comparison results of the amusement equipment parts, the initial model of the amusement equipment is trained; If the model training results are the same as the training sample results, the model training is completed; if the model training results are different from the training sample results, the model training is repeated.

5. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The method of performing amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model and generating a corresponding amusement equipment feature map comprises the following steps: Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image; Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result; Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components; A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

6. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The method of extracting and processing the feature recognition of the amusement equipment based on the amusement equipment operation state model and generating the corresponding amusement equipment feature map to classify and compare the amusement equipment components includes the following steps: Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment; Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map; Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

7. The method for detecting the operation safety of amusement equipment based on image signal recognition according to claim 1 is characterized in that: The process of determining the operation status of an amusement device based on the amusement device operation status model based on the amusement device component classification and comparison processing results comprises the following steps: If the speed map and acceleration map of the active component type of the amusement equipment contain speed thresholds and acceleration thresholds greater than or equal to the corresponding set speed thresholds of the active component, the operating status of the active component type of the amusement equipment is judged to be abnormal; otherwise, the operating status of the active component type of the amusement equipment is judged to be normal. If the amplitude spectrum of the fixed component type of the amusement equipment contains an amplitude greater than or equal to the corresponding set amplitude threshold of the fixed component, the operation state of the fixed component type of the amusement equipment is judged to be abnormal; otherwise, the operation state of the fixed component type of the amusement equipment is judged to be normal.

8. A system for detecting the operation safety of amusement equipment based on image signal recognition, characterized in that: include: A data acquisition unit, used to acquire image signal data information of the amusement equipment; A model building unit, used for building an operation status model of the amusement equipment; A first data processing unit, used for performing image signal enhancement preprocessing on the image signal data information of the amusement equipment; The second data processing unit is used to perform amusement equipment image segmentation processing on the image signal enhancement preprocessing result to remove the background area and obtain a local segmentation processing result of the amusement equipment image; A third data processing unit is used to perform amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generate a corresponding amusement equipment feature map; A fourth data processing unit is used to identify and extract the amusement equipment feature based on the amusement equipment operation state model, generate a corresponding amusement equipment feature map, and perform a classification and comparison process on the amusement equipment components; The fifth data processing unit is used to perform amusement equipment operation state determination processing on the amusement equipment component classification processing result based on the amusement equipment operation state model.

9. The system for detecting the operation safety of amusement equipment based on image signal recognition according to claim 8 is characterized in that: The third data processing unit performs amusement equipment feature recognition and extraction processing on the amusement equipment image local segmentation processing result based on the amusement equipment operation state model, and generates a corresponding amusement equipment feature map including: Performing amusement equipment feature recognition processing on the local segmentation processing result of the amusement equipment image; Performing amusement equipment component feature marking processing on the amusement equipment feature recognition processing result; Performing line segment area speed calculation and amplitude calculation on the result of the feature marking of the amusement equipment components; A velocity spectrum and an acceleration spectrum are generated based on the speed calculation results of the line segment area, and an amplitude spectrum is generated based on the amplitude calculation results.

10. The system for detecting the operation safety of amusement equipment based on image signal recognition according to claim 8, characterized in that: The fourth data processing unit extracts and processes the amusement equipment feature recognition results based on the amusement equipment operation state model and generates a corresponding amusement equipment feature map, and performs a classification and comparison process on the amusement equipment components, including: Based on the result of the feature recognition processing of the amusement equipment, the amusement equipment parts are classified and processed to obtain the types of the movable parts of the amusement equipment and the types of the fixed parts of the amusement equipment; Construct the speed and acceleration constraints of the types of active parts of the amusement equipment, and compare them with the speed map and acceleration map; Construct vibration amplitude constraints for fixed component types of amusement equipment and compare them with the amplitude spectrum.

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