Training verification method for construction range intrusion risk judgment model

Abstract

The invention relates to the technical field of image recognition model training, in particular to a training verification method for a construction range intrusion risk judgment model. Comprising the following steps: S1, acquiring images corresponding to a plurality of regions of interest, and dividing the images into a plurality of image sets according to different regions of interest; s2, performing artificial contour recognition and model contour recognition on each image in each image set to obtain an artificial contour recognition image and a model contour recognition image, and obtaining a plurality of training sets; s3, obtaining a total evaluation factor and a construction machinery evaluation factor; s4, setting a first evaluation set value and a second evaluation set value, comparing the total evaluation factor and the construction machinery evaluation factor with the first evaluation set value and the second evaluation set value, and judging whether the identification model for model contour identification in the step S2 is subjected to iterative training or not according to different conditions; and the region-of-interest identification precision in construction range intrusion risk judgment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition model training, and in particular to a training and verification method for a construction scope intrusion risk determination model. Background Art

[0002] The rapid development of the construction industry has brought high risks and frequent accidents. Factors such as the dense flow of personnel on the construction site and complex cross-operation links have led to frequent safety accidents in construction projects. Among them, close-range dangerous area intrusion accidents, such as collisions between workers and equipment or equipment, are the main types of accidents. In particular, when workers and mobile machinery are in the same construction space, human-machine collisions caused by the intersection of work trajectories are an important cause of such accidents. Workers' unintentional blindness to nearby machinery or machine drivers' negligence in observation often cause construction entities with different predetermined trajectories to overlap their work trajectories, triggering dangerous intrusions and leading to safety accidents. Accurately supervising the interaction status between workers and mobile construction machinery and timely warning of the risk of intrusion into dangerous areas of construction machinery are crucial to reducing on-site safety accidents. However, traditional manual inspection methods are too one-sided and inefficient, and existing sensor monitoring methods require high costs and have poor on-site application effects.

[0003] In the article "Intelligent Early Warning Method for Construction Machinery Intrusion into Dangerous Areas Based on Deep Learning and Depth Estimation" published by Wu Han and Han Yu, a method consisting of three parts: an image dataset of on-site workers and construction machinery, a target detection network, and a depth estimation network is disclosed. In the above method, a target detection network is used to identify areas of interest such as construction workers and construction machinery photographed by a camera, and the pixel coordinates of the area of ​​interest are obtained according to the identified contours. The pixel coordinates are converted into world coordinates through a monocular depth estimation network, and the distance between the construction workers and the construction machinery is obtained by calculation. Judgment is made based on the distance between the construction workers and the construction machinery, thereby performing intelligent early warning of danger; the above method also discloses training and verifying the target detection network through the small / small-seg model in Yolov8 and the average pixel accuracy method, but the accuracy of the target detection network trained and verified using the above method needs to be improved.

[0004] Therefore, there is an urgent need to provide a training and verification method for the construction scope intrusion risk determination model, so as to improve the accuracy of identifying the area of ​​interest in the construction scope intrusion risk determination compared with the existing technology. Summary of the invention

[0005] The present invention solves the technical problems existing in the prior art and provides a training and verification method for a construction scope intrusion risk determination model.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A training and verification method for a construction scope intrusion risk determination model comprises the following steps: S1, acquiring images corresponding to a plurality of regions of interest, and dividing the images into a plurality of image sets according to different regions of interest, where the regions of interest include construction workers and a plurality of construction machines; S2. For each image in each image set, artificial contour recognition and model contour recognition are performed respectively to obtain artificial contour recognition images and model contour recognition images, and multiple training sets are set. All artificial contour recognition images and model contour recognition images processed by one image set are placed in one training set. Each training set includes multiple training pairs, and each training pair includes an artificial contour recognition image and a model contour recognition image corresponding to one image. S3, obtaining a total evaluation factor and a construction machinery evaluation factor according to the multiple training sets obtained in step S2; S4. Set a first evaluation setting value and a second evaluation setting value, compare the total evaluation factor and the construction machinery evaluation factor with the first evaluation setting value and the second evaluation setting value, form different situations, and determine whether the recognition model for model contour recognition in step S2 should be iteratively trained according to the different situations.

[0007] Furthermore, S3 specifically includes the following steps: S31, analyzing each training set to obtain an evaluation factor of each training set; S32. According to the evaluation factor of each training set, the total evaluation factor is obtained, which is specifically calculated by the following formula: ; ; In the above formula, E represents the total evaluation factor, represents the evaluation factor corresponding to the i-th training set, express The corresponding precision weight value, represents the i-th set threshold; S33. Settings , , , are the evaluation factors of the training sets corresponding to four different construction machines. , , , The average value of the construction machinery evaluation factor .

[0008] Furthermore, S31 specifically includes the following steps: S311, obtaining the evaluation factor of each training pair in the ith training set, wherein the method for obtaining the evaluation factor of the jth training pair in the ith training set specifically comprises the following steps: S3111, for the j-th training pair in the i-th training set, obtain the pixel coordinates of all the pixels in the contour in the artificial contour recognition image to form a first pixel coordinate set; and simultaneously obtain the pixels of all the pixels in the contour in the model contour recognition image to form a second pixel coordinate set; S3112, performing error value screening on the first pixel coordinate set to obtain a first screened pixel coordinate set that has undergone error screening, the first screened pixel coordinate set including a plurality of first screened pixel coordinates; S3113, performing error value screening on the second pixel coordinate set to obtain a second screened pixel coordinate set that has undergone error screening, the second screened pixel coordinate set including a plurality of second screened pixel coordinates; S3114, obtaining an evaluation factor of the jth training pair in the ith training set according to the first screening pixel coordinate set and the second screening pixel coordinate set, where j ranges from 1 to Q, and Q represents the total number of training pairs in the ith training set; S312, taking the average of the evaluation factors of all training pairs in the i-th training set obtained in step S311, to obtain the evaluation factor corresponding to the i-th training set.

[0009] Furthermore, in step S3114, the specific method for obtaining the evaluation factor of the j-th training pair in the i-th training set is: for each first screening pixel coordinate, perform Euclidean distance calculation with all second screening pixel coordinates to obtain the second screening pixel coordinate with the smallest Euclidean distance value with the first screening pixel coordinate, and form a sample pair with the corresponding first screening pixel coordinate and the second screening pixel coordinate, each sample pair corresponds to a sample pair distance value, and the sample pair distance value is the Euclidean distance value between the first screening pixel coordinate and the second screening pixel coordinate in the sample pair; Set a first distance judgment value and a second distance judgment value, the first distance judgment value is 0, and the second distance judgment value is greater than the first distance judgment value; select the number of sample pairs with distance values ​​equal to the first distance judgment value, recorded as ; Screen the number of sample distance values ​​greater than the first distance judgment value and less than or equal to the second distance judgment value, recorded as ; Screen the number of sample distance values ​​greater than the second distance judgment value, recorded as ; The evaluation factor of the jth training pair in the i-th training set is calculated by the following formula: ; In the above formula, represents the evaluation factor of the jth training pair in the i-th training set, represents the first weight value, represents the second weight value, Represents the third weight value.

[0010] Furthermore, , , They are calculated by the following formulas: ; ; ; In the above formula, represents the distance value of the lth sample pair that is greater than the first distance judgment value and less than or equal to the second distance judgment value, Represents the average value of all sample distance values ​​that are greater than the first distance judgment value and less than or equal to the second distance judgment value.

[0011] Furthermore, the second distance judgment value is calculated according to the following formula: ; In the above formula, represents the second distance judgment value, A represents the first correction value, B represents the second correction value, and A and B respectively take different constants between 2% and 5%.

[0012] Furthermore, in step S3112, the specific method for obtaining the first filtered pixel coordinate set is: calculating the Euclidean distance between each first pixel coordinate in the first pixel coordinate set and all other first pixel coordinates except itself, to obtain multiple first Euclidean distances, all first Euclidean distances form a first Euclidean distance set, and setting a distance threshold for each training pair, the distance threshold being the longest distance of the region of interest corresponding to the jth training pair on the artificial contour recognition image; comparing each first Euclidean distance in the first Euclidean distance set with the distance threshold, screening out first Euclidean distances greater than the distance threshold, and filtering out the first pixel coordinates corresponding to all the screened first Euclidean distances to obtain the first filtered pixel coordinate set.

[0013] Furthermore, in step S3113, the specific method for obtaining the second filtered pixel coordinate set is: calculating the Euclidean distance between each second pixel coordinate in the second pixel coordinate set and all other second pixel coordinates except itself, to obtain multiple second Euclidean distances, all second Euclidean distances form a second Euclidean distance set, comparing each second Euclidean distance in the second Euclidean distance set with the distance threshold, filtering out second Euclidean distances greater than the distance threshold, filtering out the second pixel coordinates corresponding to all the filtered second Euclidean distances, and obtaining the second filtered pixel coordinate set.

[0014] Furthermore, in step S4, the specific method for judging whether the recognition model for model contour recognition in step S2 is to be iteratively trained according to different situations is as follows: (1) When ,and When , the recognition model does not perform iterative training; (2) When ,and When , the recognition model is iteratively trained F times; (3) When ,and When F Second-rate; (4) When ,and When F Second-rate; In the above formula, represents the overall evaluation factor, represents the mechanical evaluation factor, represents the first evaluation setting value, represents the second evaluation setting value, , , All represent integers.

[0015] Furthermore, F, n, and m satisfy the following formula: ; .

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains images of construction workers and construction machinery at the construction site, performs manual contour recognition and model contour recognition, obtains multiple training sets, obtains corresponding evaluation factors for different training sets, thereby obtaining an overall evaluation factor and a construction machinery evaluation factor, and according to the overall evaluation factor and the construction machinery evaluation factor, sets the number of iterative training times of the recognition model according to different situations, so that in different situations, the recognition model performs a corresponding number of iterative trainings, thereby improving the recognition accuracy of the recognition model, thereby improving the accuracy of the construction scope intrusion risk judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0019] like Figure 1 As shown, the present invention provides a training and verification method for a construction scope intrusion risk determination model, comprising the following steps: S1. Acquire images collected on the spot, wherein the images include at least one region of interest. According to different regions of interest, the regions of interest include construction workers, excavators, trucks, drilling machines and pile drivers. The images are divided into multiple image sets according to different regions of interest. In this embodiment, the images are divided into a first image set, a second image set, a third image set, a fourth image set and a fifth image set. The first image set includes multiple images of construction workers, the second image set includes multiple images of excavators, the third image set includes multiple images of trucks, the fourth image set includes multiple images of drilling machines, and the fifth image set includes multiple images of pile drivers.

[0020] S2. Perform artificial contour recognition and model contour recognition on each image in each image set to obtain an artificial contour recognition image and a model contour recognition image. In both the artificial contour recognition image and the model contour recognition image, the corresponding region of interest is framed with a contour. Multiple training sets are set, and one training set contains all artificial contour recognition images and model contour recognition images after processing an image set. Each training set includes multiple training pairs, and each training pair includes an artificial contour recognition image and a model contour recognition image corresponding to an image.

[0021] S3, obtaining the total evaluation factor and the construction machinery evaluation factor according to the multiple training sets obtained in step S2, specifically comprising the following steps: S31. Analyze each training set to obtain the evaluation factor corresponding to each training set; the evaluation factor corresponding to the i-th training set is the i-th evaluation factor, denoted as ,i takes 1-5, The method for obtaining includes the following steps: S311, obtain the evaluation factor of each training pair in the ith training set, and the evaluation factor of the jth training pair in the ith training set is recorded as , j ranges from 1 to Q, Q represents the total number of training pairs in the i-th training set, The method to obtain is: S3111. For the j-th training pair in the ith training set, the pixel coordinates of all the pixels within the contour in the artificial contour recognition image are obtained to form a first pixel coordinate set, and the pixel coordinates of all the pixels within the contour in the model contour recognition image are obtained to form a second pixel coordinate set.

[0022] The first pixel coordinate set includes multiple first pixel coordinates, each of which is the pixel coordinate of a pixel point in the artificial contour image of the jth training pair in the i-th training set; the second pixel coordinate set includes multiple second pixel coordinates, each of which is the pixel coordinate of a pixel point in the model contour image of the j-th training pair in the i-th training set.

[0023] S3112: Perform error value screening on the first pixel coordinate set to obtain a first screened pixel coordinate set that has been screened by the error value. , ;in, represents the first first filtered pixel coordinate, represents the tth first screened pixel coordinate, t represents the total number of first screened pixel coordinates, , g represents the total number of first pixel coordinates in the first pixel coordinate set.

[0024] The specific method of performing error value screening on the first pixel coordinate set to obtain the first screened pixel coordinate set after error value screening is as follows: for each first pixel coordinate in the first pixel coordinate set, the Euclidean distance is calculated with all other first pixel coordinates except itself to obtain multiple first Euclidean distances, and all first Euclidean distances form a first Euclidean distance set, and a distance threshold is set for each training pair, and the distance threshold is the longest distance of the region of interest corresponding to the jth training pair in the artificial contour recognition image; each first Euclidean distance in the first Euclidean distance set is compared with the distance threshold, and the first Euclidean distance greater than the distance threshold is screened out, and the first pixel coordinates corresponding to all the screened first Euclidean distances are screened out to obtain the first screened pixel coordinate set.

[0025] S3113: Perform error value screening on the second pixel coordinate set to obtain a second screened pixel coordinate set that has been screened by the error value. , ;in, represents the first second filtered pixel coordinates, represents the Tth second screening pixel coordinate, T represents the total number of second screening pixel coordinates, , G represents the total number of second pixel coordinates in the second pixel coordinate set.

[0026] The specific method of performing error value screening on the second pixel coordinate set to obtain the second screened pixel coordinate set after error value screening is as follows: for each second pixel coordinate in the second pixel coordinate set, the Euclidean distance is calculated with all other second pixel coordinates except itself to obtain multiple second Euclidean distances, all second Euclidean distances form a second Euclidean distance set, each second Euclidean distance in the second Euclidean distance set is compared with a distance threshold, second Euclidean distances greater than the distance threshold are screened out, and the second pixel coordinates corresponding to all the screened second Euclidean distances are screened out to obtain the second screened pixel coordinate set.

[0027] S3114, for each first filtered pixel coordinate, perform Euclidean distance calculation with all second filtered pixel coordinates, obtain the second filtered pixel coordinate with the smallest Euclidean distance value of each first filtered pixel coordinate, obtain multiple sample pairs, each sample pair includes the first filtered pixel coordinate and the second filtered pixel coordinate with the smallest Euclidean distance value, each sample pair has a Euclidean distance value, and the Euclidean distance value of each sample pair is set as the sample pair distance value; set the first distance judgment value and the second distance judgment value , the second distance judgment value is greater than the first distance judgment value, all sample pair distance values ​​are compared with the first distance judgment value and the second distance judgment value, and the number of sample pair distance values ​​equal to the first distance judgment value is recorded as The number of sample distance values ​​greater than the first distance judgment value and less than or equal to the second distance judgment value is recorded as , the number of sample pairs with distance values ​​greater than the second distance judgment value is recorded as .

[0028] The first distance judgment value and the second distance judgment value are calculated according to the following formula: ; ; In the above formula, A represents the first correction value, B represents the second correction value, and A and B take different constants between 2% and 5% respectively.

[0029] according to , , , we get the evaluation factor of the jth training pair in the i-th training set, which is specifically expressed as: ; In the above formula, represents the evaluation factor of the jth training pair in the i-th training set, represents the first weight value, represents the second weight value, Represents the third weight value.

[0030] , , They are calculated by the following formulas: ; ; ; In the above formula, represents the distance value of the lth sample pair that is greater than the first distance judgment value and less than or equal to the second distance judgment value, Represents the average value of all sample distance values ​​that are greater than the first distance judgment value and less than or equal to the second distance judgment value.

[0031] S312, according to the evaluation factor of each training pair in the i-th training set, obtain the evaluation factor of the i-th training set, which is specifically calculated by the following formula: .

[0032] S32. According to the evaluation factor of each training set, the total evaluation factor is obtained, which is specifically calculated by the following formula: ; ; In the above formula, E represents the total evaluation factor, express The corresponding precision weight value, represents the i-th set threshold, It is obtained by taking the average of the distance thresholds of all training pairs in the i-th training set.

[0033] S33. Settings , , , , are the evaluation factors of the training sets corresponding to construction workers, excavators, trucks, drilling machines, and pile drivers, respectively. The evaluation factor of construction machinery is obtained by the following formula: ; In the above formula, Represents the construction machinery evaluation factor.

[0034] S4. According to the total evaluation factor and the construction machinery evaluation factor obtained in step S3, it is determined whether the recognition model for model contour recognition in step S2 is to be iteratively trained. The specific method is as follows: A first evaluation setting value and a second evaluation setting value are set, and the first evaluation setting value and the second evaluation setting value are set to and ,and .

[0035] (1) When ,and , the recognition model does not perform iterative training.

[0036] (2) When ,and When , the recognition model is iteratively trained F times.

[0037] (3) When ,and When F Second-rate.

[0038] (4) When ,and When F Second-rate.

[0039] F, n, and m satisfy the following formula: ; ; , n, and m are all integers.

[0040] The present invention obtains images of construction workers and construction machinery at the construction site, performs manual contour recognition and model contour recognition, obtains multiple training sets, obtains corresponding evaluation factors for different training sets, thereby obtaining an overall evaluation factor and a construction machinery evaluation factor, and according to the overall evaluation factor and the construction machinery evaluation factor, sets the number of iterative training times of the recognition model according to different situations, so that in different situations, the recognition model performs a corresponding number of iterative trainings, thereby improving the recognition accuracy of the recognition model, thereby improving the accuracy of the construction scope intrusion risk judgment.

[0041] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A training and verification method for a construction scope intrusion risk determination model, characterized in that: The following steps are involved: S1, acquiring images corresponding to a plurality of regions of interest, and dividing the images into a plurality of image sets according to different regions of interest, where the regions of interest include construction workers and a plurality of construction machines; S2. For each image in each image set, artificial contour recognition and model contour recognition are performed respectively to obtain artificial contour recognition images and model contour recognition images, and multiple training sets are set. All artificial contour recognition images and model contour recognition images processed by one image set are placed in one training set. Each training set includes multiple training pairs, and each training pair includes an artificial contour recognition image and a model contour recognition image corresponding to one image. S3, obtaining a total evaluation factor and a construction machinery evaluation factor according to the multiple training sets obtained in step S2; S4. Set a first evaluation setting value and a second evaluation setting value, compare the total evaluation factor and the construction machinery evaluation factor with the first evaluation setting value and the second evaluation setting value, form different situations, and determine whether the recognition model for model contour recognition in step S2 should be iteratively trained according to the different situations.

2. The training and verification method of the construction scope intrusion risk determination model according to claim 1 is characterized in that: S3 specifically includes the following steps: S31, analyzing each training set to obtain an evaluation factor of each training set; S32. According to the evaluation factor of each training set, the total evaluation factor is obtained, which is specifically calculated by the following formula: ； ； In the above formula, E represents the total evaluation factor, represents the evaluation factor corresponding to the i-th training set, express The corresponding precision weight value, represents the i-th set threshold; S33. Settings , , , are the evaluation factors of the training sets corresponding to four different construction machines. , , , The average value of the construction machinery evaluation factor .

3. The training and verification method of the construction scope intrusion risk determination model according to claim 2 is characterized in that: S31 specifically includes the following steps: S311, obtaining the evaluation factor of each training pair in the ith training set, wherein the method for obtaining the evaluation factor of the jth training pair in the ith training set specifically comprises the following steps: S3111, for the j-th training pair in the i-th training set, obtain the pixel coordinates of all the pixels in the contour in the artificial contour recognition image to form a first pixel coordinate set; and simultaneously obtain the pixels of all the pixels in the contour in the model contour recognition image to form a second pixel coordinate set; S3112, performing error value screening on the first pixel coordinate set to obtain a first screened pixel coordinate set that has undergone error screening, the first screened pixel coordinate set including a plurality of first screened pixel coordinates; S3113, performing error value screening on the second pixel coordinate set to obtain a second screened pixel coordinate set that has undergone error screening, the second screened pixel coordinate set including a plurality of second screened pixel coordinates; S3114, obtaining an evaluation factor of the jth training pair in the i-th training set according to the first screened pixel coordinate set and the second screened pixel coordinate set, where j ranges from 1 to Q, and Q represents the total number of training pairs in the i-th training set; S312, taking the average of the evaluation factors of all training pairs in the i-th training set obtained in step S311, to obtain the evaluation factor corresponding to the i-th training set.

4. The training and verification method of the construction scope intrusion risk determination model according to claim 3 is characterized in that: In step S3114, the specific method for obtaining the evaluation factor of the j-th training pair in the i-th training set is as follows: for each first screening pixel coordinate, the Euclidean distance calculation is performed with all the second screening pixel coordinates to obtain the second screening pixel coordinate with the smallest Euclidean distance value with the first screening pixel coordinate, and the corresponding first screening pixel coordinates and the second screening pixel coordinates are combined into a sample pair, each sample pair corresponds to a sample pair distance value, and the sample pair distance value is the Euclidean distance value between the first screening pixel coordinate and the second screening pixel coordinate in the sample pair; Set a first distance judgment value and a second distance judgment value, the first distance judgment value is 0, and the second distance judgment value is greater than the first distance judgment value; select the number of sample pairs with distance values ​​equal to the first distance judgment value, recorded as ; Screen the number of sample distance values ​​greater than the first distance judgment value and less than or equal to the second distance judgment value, recorded as ; Screen the number of sample distance values ​​greater than the second distance judgment value, recorded as ; The evaluation factor of the jth training pair in the i-th training set is calculated by the following formula: ； In the above formula, represents the evaluation factor of the jth training pair in the i-th training set, represents the first weight value, represents the second weight value, Represents the third weight value.

5. The training and verification method of the construction scope intrusion risk determination model according to claim 4 is characterized in that: , , They are calculated by the following formulas: ； ； ； In the above formula, represents the distance value of the lth sample pair that is greater than the first distance judgment value and less than or equal to the second distance judgment value, Represents the average value of all sample distance values ​​that are greater than the first distance judgment value and less than or equal to the second distance judgment value.

6. The training and verification method of the construction scope intrusion risk determination model according to claim 4 is characterized in that: The second distance judgment value is calculated according to the following formula: ； In the above formula, represents the second distance judgment value, A represents the first correction value, B represents the second correction value, and A and B respectively take different constants between 2% and 5%.

7. The training and verification method of the construction scope intrusion risk determination model according to claim 3 is characterized in that: In step S3112, the specific method for obtaining the first filtered pixel coordinate set is: for each first pixel coordinate in the first pixel coordinate set, the Euclidean distance is calculated with all other first pixel coordinates except itself, to obtain multiple first Euclidean distances, all first Euclidean distances form a first Euclidean distance set, a distance threshold is set for each training pair, and the distance threshold is the longest distance of the region of interest corresponding to the jth training pair on the artificial contour recognition image; each first Euclidean distance in the first Euclidean distance set is compared with the distance threshold, and the first Euclidean distance greater than the distance threshold is screened out, and the first pixel coordinates corresponding to all the screened first Euclidean distances are screened out to obtain the first filtered pixel coordinate set.

8. The training and verification method of the construction scope intrusion risk determination model according to claim 7 is characterized in that: In step S3113, the specific method for obtaining the second filtered pixel coordinate set is: calculating the Euclidean distance between each second pixel coordinate in the second pixel coordinate set and all other second pixel coordinates except itself, obtaining multiple second Euclidean distances, all second Euclidean distances form a second Euclidean distance set, comparing each second Euclidean distance in the second Euclidean distance set with a distance threshold, filtering out second Euclidean distances greater than the distance threshold, filtering out the second pixel coordinates corresponding to all the filtered second Euclidean distances, and obtaining the second filtered pixel coordinate set.

9. The training and verification method of a construction scope intrusion risk determination model according to claim 1 is characterized in that: In step S4, the specific method for judging whether the recognition model for model contour recognition in step S2 should be iteratively trained according to different situations is: (1) When ,and When , the recognition model does not perform iterative training; (2) When ,and When , the recognition model is iteratively trained F times; (3) When ,and When F Second-rate; (4) When ,and When F Second-rate; In the above formula, represents the overall evaluation factor, represents the mechanical evaluation factor, represents the first evaluation setting value, represents the second evaluation setting value, , , All represent integers.

10. The training and verification method of the construction scope intrusion risk determination model according to claim 9 is characterized in that: F, n, and m satisfy the following formula: ； 。

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