Automatic marking method and system for injury state labels

Abstract

The invention belongs to the technical field of forensic identification. The invention provides an automatic marking method and system for injury state labels. The method comprises the following steps: acquiring first video stream data of the wounded person subjected to injury inspection work by a forensic, and segmenting the first video stream data into a plurality of sections of images according to an injury inspection area; performing injury condition feature extraction on each section of image to obtain a corresponding first injury condition label and a corresponding injury condition position; receiving a text injury test report generated by a forensic, performing semantic understanding on the text injury test report by using the large language model, and extracting a plurality of second injury state tags; performing matching analysis on the second injury state labels and the first injury state labels, and if a matching result is obtained, determining the corresponding second injury state labels as target injury state labels; and in the target image, labeling each target injury condition label to an injury condition position corresponding to the matched first injury condition label. According to the method, automatic and accurate marking of the injury state labels is realized, and the efficiency of marking the injury identification result of the forensic is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forensic identification, and in particular to a method and system for automatically labeling injury labels. Background Art

[0002] Forensic injury assessment refers to the activity of forensic doctors assessing the injuries of injured individuals according to certain standards and procedures. This activity is very important for determining the nature, extent and consequences of injuries, and is often used in criminal cases, civil disputes, industrial accidents and other occasions. When issuing an injury assessment report, in order to improve the intuitiveness of the report, in addition to describing it in text, forensic doctors or other staff are required to mark the injury assessment results one by one in the image of the injured person. Obviously, the manual annotation method is relatively inefficient and cannot meet actual needs. Summary of the invention

[0003] In this regard, the present invention provides a method, system, electronic device, computer storage medium and computer program product for automatically labeling injury labels to solve the above-mentioned technical problems.

[0004] The present invention discloses a method for automatically marking injury labels, the method comprising the following steps: Acquire first video stream data of a forensic doctor performing injury examination on an injured person, and divide the first video stream data into multiple image segments according to the injury examination area; Extract injury features from each image segment to obtain the corresponding first injury label and the corresponding injury location; receiving a text injury report generated by a forensic doctor, using a large language model to perform semantic understanding on the text injury report, and extracting a plurality of second injury labels; Performing a matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determining the corresponding second injury condition label as a target injury condition label; In a target image, each target injury label is marked at the injury position corresponding to the matching first injury label; wherein the target image is any frame of high-definition image in the first video stream data.

[0005] In some embodiments, the target image is determined by: Acquire second video stream data containing the entire body area of ​​the injured person, and extract the external features and behavioral features of the injured person according to the second video stream data; Predicting the injury area distribution information of the injured person according to the external features and the behavioral features, and determining the category attribute of the target image according to the injury area distribution information, wherein the category attribute includes a full-body image and a non-full-body image; If the determined category attribute of the target image is a full-body image, a high-definition image frame containing a full-body image of the injured person is intercepted from the second video stream data, and a plurality of high-definition image frames corresponding to the second injury label are intercepted from the first video stream data; If the determined category attribute of the target image is a non-full body image, a number of high-definition image frames corresponding to the second injury label are captured from the first video stream data.

[0006] In some embodiments, determining the category attribute of the target image according to the injury area distribution information includes: According to each injury point in the injury area distribution information, two injury points with the farthest distance are determined, and a connected area is drawn on the body image of the injured person based on the two injury points; The ratio of the area of ​​the connected region to the area of ​​the injured person's body image is calculated. If the ratio is greater than a threshold, the category attribute of the target image is determined to be a full-body image; otherwise, the category attribute of the target image is determined to be a non-full-body image.

[0007] In some embodiments, dividing the first video stream data into multiple video segments according to the injury assessment area includes: Identifying the forensic doctor's injury examination action and the injured person's body area in the first video stream data, and determining the injury examination area according to the injury examination action and the injured person's body area; When the injury detection area changes beyond a preset distance, it is determined that there is a switch in the injury detection area, and a moment of a preset time before the switching moment is determined as a split node; The first video stream data is divided into a plurality of image segments according to each of the segmentation nodes.

[0008] In some embodiments, a large language model is used to perform semantic understanding on the text injury assessment report to extract a number of second injury labels, including: Obtaining an injury report template from a database, and determining an injury record area according to the injury report template; An injury record area in the text injury assessment report is identified, and a large language model is used to perform semantic understanding on the text content in the injury record area to extract a plurality of the second injury labels.

[0009] In some embodiments, the second injury condition label is matched with the first injury condition label and if no matching result is obtained, then: Obtaining the injury position corresponding to the second injury label, querying whether a transmission medical image corresponding to the injury position is stored in a database, and if so, determining a third injury label corresponding to the injury position according to the transmission medical image; If the second injury condition label matches the third injury condition label, determining the second injury condition label as the target injury condition label; If the database does not store the transmission medical image corresponding to the injury position or the second injury label does not match the third injury label, a manual confirmation prompt message is output.

[0010] The present invention also discloses an automatic injury labeling system, which includes an acquisition module, an injury extraction module, a report analysis module, a comparison analysis module, and an injury labeling module; The acquisition module is used to acquire first video stream data of the forensic doctor performing injury examination on the injured person, and divide the first video stream data into multiple image segments according to the injury examination area; The injury extraction module is used to extract injury features from each image segment to obtain a corresponding first injury label and a corresponding injury position; The report analysis module is used to receive the text injury report generated by the forensic doctor, use the large language model to perform semantic understanding on the text injury report, and extract a plurality of second injury labels; The comparison analysis module is used to perform matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determine the corresponding second injury condition label as a target injury condition label; The injury labeling module is used to label each target injury label at the injury position corresponding to the matching first injury label in the target image; wherein the target image is any frame of high-definition image in the first video stream data.

[0011] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the above methods.

[0012] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0013] The present invention also discloses a computer program product, which implements any of the above methods when the computer program product is run on a terminal.

[0014] The present invention processes both the forensic injury examination video and the forensic injury examination text report, performs matching analysis on the respectively obtained injury labels, thereby confirming the confidence of the automatically generated injury labels through mutual verification of the injury labels, and then realizes automatic labeling of the injury labels. This can greatly improve the efficiency of labeling of forensic injury examination results on the basis of ensuring the accuracy of injury labeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for automatically labeling injury labels disclosed in an embodiment of the present invention.

[0017] Figure 2 It is a structural schematic diagram of an automatic injury labeling system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0019] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0020] like Figure 1 As shown, an embodiment of the present invention discloses a method for automatically labeling injury labels, and the method comprises the following steps: Acquire first video stream data of a forensic doctor performing injury examination on an injured person, and divide the first video stream data into multiple image segments according to the injury examination area; Extract injury features from each image segment to obtain the corresponding first injury label and the corresponding injury location; receiving a text injury report generated by a forensic doctor, using a large language model to perform semantic understanding on the text injury report, and extracting a plurality of second injury labels; Performing a matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determining the corresponding second injury condition label as a target injury condition label; In a target image, each target injury label is marked at the injury position corresponding to the matching first injury label; wherein the target image is any frame of high-definition image in the first video stream data.

[0021] When the forensic doctor is conducting an injury inspection on the injured, he uses a high-definition camera to record the video data of the forensic injury inspection process. According to the different injury inspection areas of the forensic doctor (each injury inspection area corresponds to an injury), the entire video data can be divided into multiple small segments, and each small segment corresponds to the injury inspection process of one area. The image recognition technology can be used to extract the injury characteristics from each small segment of the image, and the corresponding first injury label and the injury position corresponding to the first injury label can be determined based on this. Then, after completing the injury inspection operation, the forensic doctor will manually generate a text injury inspection report. The text injury inspection report is semantically understood using a large language model, and several second injury labels corresponding to the injury inspection results determined by the forensic doctor can be extracted. Finally, each second injury label is matched and analyzed with each first injury label. If there is a matching result, it means that the second injury label obtained by the semantic analysis is associated with a first injury label extracted from the image, that is, it means that the confidence of the second injury label is high enough. At this time, it is marked at the corresponding position in the target image, which is the injury position corresponding to the matched first injury label. The injury labels include a calf fracture, a 3cm forehead wound, and a back burn area of ​​approximately 3cm².

[0022] Therefore, the present invention processes both the forensic injury identification video and the forensic injury identification text report, performs matching analysis on the respectively obtained injury labels, thereby confirming the confidence of the automatically generated injury labels through mutual verification of the injury labels, and then realizes automatic labeling of the injury labels. This can greatly improve the efficiency of labeling of forensic injury identification results while ensuring the accuracy of injury labeling.

[0023] Among them, the large language model can be any one of the BERT model, T5 (Text-to-Text Transfer Transformer) model, PaLM (Pathways Language Model) model, and LLaMA (Large Language Model Meta AI) model, and the present invention does not make any limitation. In addition, the large language model can generally obtain a proprietary model suitable for specific problem recognition, classification, prediction, etc. by fine-tuning the training data set of a specific scenario. The construction of its training data set and the fine-tuning training method are mature existing technologies, and the present invention will not repeat them here.

[0024] In some embodiments, the target image is determined by: Acquire second video stream data containing the entire body area of ​​the injured person, and extract the external features and behavioral features of the injured person according to the second video stream data; Predicting the injury area distribution information of the injured person according to the external features and the behavioral features, and determining the category attribute of the target image according to the injury area distribution information, wherein the category attribute includes a full-body image and a non-full-body image; If the determined category attribute of the target image is a full-body image, a high-definition image frame containing a full-body image of the injured person is intercepted from the second video stream data, and a plurality of high-definition image frames corresponding to the second injury label are intercepted from the first video stream data; If the determined category attribute of the target image is a non-full body image, a number of high-definition image frames corresponding to the second injury label are captured from the first video stream data.

[0025] In this embodiment, the target image is a high-definition image in the captured video stream data, which contains an image of the injured person and needs to be labeled with an injury label. The labeled target image will be used as part of the injury assessment report. Therefore, the target image should correspond to the injury distribution of the injured person.

[0026] Specifically, before the forensic doctor conducts the injury examination, the second video stream data containing the entire body area of ​​the injured person is first obtained, from which the external characteristics and behavioral characteristics of the injured person can be extracted. The external characteristics refer to wounds, bruises, blood, bandages, fixed splints (fractures), etc. that can directly represent the injured person's injuries, and behavioral characteristics include walking posture, hand movements (such as the left hand supporting the fractured right hand), and other people's support actions for the injured person that can indirectly represent the injured person's injuries. By analyzing the external characteristics and behavioral characteristics of the injured person, it is possible to roughly know where the injured person has injuries and the distribution of the injuries.

[0027] The external features and behavioral features can be processed by a preset injury prediction model, and the injury prediction model can predict the possible injuries and their location distribution on the injured person, thereby outputting the regional distribution information of the injuries. The injury prediction model is preferably constructed using the Transformer with a self-attention mechanism, and is fully pre-trained using the corresponding data set. The data set contains multiple groups of data, each of which contains a video data of an injured person, the external features and behavioral features extracted from the video data, and the injury labels marked at the corresponding positions on the injured person's body in the video data.

[0028] According to the distribution information of the injured person's injuries, the category attributes of the target image can be determined, that is, full-body image and non-full-body image. The meaning of the full-body image category is that the target image should contain at least one full-body image of the injured person, and the meaning of the non-full-body image category is that the target image does not need to contain the full-body image of the injured person, and all the images contained are partial images of the injured person's injury areas. For the full-body image category, it is necessary to intercept a high-definition image frame containing the full-body image of the injured person in the second video stream data, because the second video stream data is more likely to have image frames containing only the full-body image of the injured person, and the presence of the forensic doctor in the first video stream data will make it difficult to obtain image frames containing only the full-body image of the injured person; for the non-full-body image category, it is only necessary to intercept multiple high-definition image frames in the first video stream data, and the high-definition image frames correspond to the second injury labels determined above. Similarly, for the full-body image category, it is also necessary to intercept multiple high-definition image frames in the first video stream data, so that full-body images and partial images can be added to the injury report to improve the injury display effect of the injury report.

[0029] It should be noted that the object of forensic injury examination may also be a non-living object, in which case only its external characteristics can be obtained, that is, the regional distribution information of the injury of the non-living object can be directly determined based on the external characteristics alone.

[0030] In some embodiments, determining the category attribute of the target image according to the injury area distribution information includes: According to each injury point in the injury area distribution information, two injury points with the farthest distance are determined, and a connected area is drawn on the body image of the injured person based on the two injury points; The ratio of the area of ​​the connected region to the area of ​​the injured person's body image is calculated. If the ratio is greater than a threshold, the category attribute of the target image is determined to be a full-body image; otherwise, the category attribute of the target image is determined to be a non-full-body image.

[0031] In this embodiment, the injury area distribution information includes each injury point and its distribution point in the injured person's body image, from which the two injury points with the farthest distance are determined, for example, according to the direction from bottom to top or from top to bottom. A connected area is drawn on the injured person's body image based on the two injury points with the farthest distance, and the connected area is the injury distribution area. Then, the ratio of the area of ​​the injury distribution area to the area of ​​the injured person's body image is calculated. The larger the ratio, the more injuries the injured person has and the more dispersedly distributed at multiple points on the body. Conversely, the smaller the ratio, the less injuries the injured person has and the more concentratedly distributed at certain points on the body. If the ratio is greater than the threshold, it is determined that all or the main injury labels need to be marked in the full-body image, that is, an overview of the injuries needs to be provided. At this time, the category attribute of the target image is determined to be a full-body image; otherwise, it is determined that there is no need to provide an overview of the injuries, and only an injury map of a specific area is provided. At this time, the category attribute of the target image is determined to be a non-full-body image.

[0032] In some embodiments, dividing the first video stream data into multiple video segments according to the injury assessment area includes: Identifying the forensic doctor's injury examination action and the injured person's body area in the first video stream data, and determining the injury examination area according to the injury examination action and the injured person's body area; When the injury detection area changes beyond a preset distance, it is determined that there is a switch in the injury detection area, and a moment of a preset time before the switching moment is determined as a split node; The first video stream data is divided into a plurality of image segments according to each of the segmentation nodes.

[0033] In this embodiment, when the forensic doctor examines the injury, he can use image recognition technology to extract the forensic doctor's injury examination action (mainly hand action) and the image of the injured person's body area in the image frame, dynamically track the forensic doctor's injury examination action and the image of the injured person's body area, and analyze the relative position relationship between the area corresponding to the injury examination action (located on the image of the injured person's body area) and the injured person's body area, thereby determining whether the injury examination area has changed beyond the preset distance, thereby determining whether the forensic doctor has switched to the next point of other injuries. The moment of the preset time before the above-identified switching moment is determined as a segmentation node, and the preset time is, for example, 2s. According to these segmentation nodes, the first video stream data can be segmented into multiple image segments.

[0034] The preset distance can also be determined according to the type of body region where the injury inspection area is located. The human body is roughly divided into the head region, the internal organs region, and the limbs region from top to bottom. Since there are many organs in the head, more detailed distinctions are required during injury inspection, such as eye injuries, ear injuries, back of the head injuries, neck injuries, etc., and these organs are distributed at a close distance, so the preset distance of the head region is set to the minimum; while there are fewer types of organs in the limbs region, so the preset distance of the limbs region is set to the maximum; in addition, the number of organs in the internal organs region is between the head and the limbs, so the preset distance of the internal organs region is set to a medium value.

[0035] It should be noted that injury assessment actions refer to actions related to injury assessment, such as picking, tweezing, measuring, etc., applied to the injured person's body, and do not involve non-direct injury assessment actions such as checking records after the injury, looking for professional equipment, etc. This can reduce the probability of misjudgment of switching injury assessment areas.

[0036] In some embodiments, a large language model is used to perform semantic understanding on the text injury assessment report to extract a number of second injury labels, including: Obtaining an injury report template from a database, and determining an injury record area according to the injury report template; An injury record area in the text injury assessment report is identified, and a large language model is used to perform semantic understanding on the text content in the injury record area to extract a plurality of the second injury labels.

[0037] In this embodiment, the database stores an injury report template, which includes filling specifications for the injury report, for example, in the form of annotations added to the corresponding position of the injury report template. By semantically understanding these annotations, the injury record area in the injury report template can be determined. Therefore, the text content corresponding to the injury record area in the text injury report produced by the forensic doctor is extracted, and the semantic understanding is performed by the large language model to extract a number of second injury labels.

[0038] There may be multiple versions of injury examination report templates in the database. The corresponding injury examination report template can be selected according to the type of case involving the injured person, and the latest injury examination report template should be selected.

[0039] In some embodiments, the second injury condition label is matched with the first injury condition label and if no matching result is obtained, then: Obtaining the injury position corresponding to the second injury label, querying whether a transmission medical image corresponding to the injury position is stored in a database, and if so, determining a third injury label corresponding to the injury position according to the transmission medical image; If the second injury condition label matches the third injury condition label, determining the second injury condition label as the target injury condition label; If the database does not store the transmission medical image corresponding to the injury position or the second injury label does not match the third injury label, a manual confirmation prompt message is output.

[0040] In this embodiment, if the second injury label hits a first injury label, it means that the second injury label is credible, and if the second injury label does not hit any first injury label, it means that the second injury label extracted by the large language model may be wrong. In this regard, the present invention further queries whether there is a transmissive medical image corresponding to the injury position stored in the database. If there is and the third injury label corresponding to the transmissive medical image matches the second injury label, it means that the second injury label is credible, and it is still determined as the target injury label.

[0041] It should be noted that for internal injuries such as fractures, forensic doctors generally cannot determine them through external observation, but need to confirm them through X-ray, CT and other transmissive medical images, which are stored in the database. Obviously, for these internal injuries, the first injury label determined based on the external features of the injury in the first video stream data is inevitably wrong, that is, the reason why the second injury label does not hit the result may be the error of the first injury label.

[0042] like Figure 2 As shown, the embodiment of the present invention also discloses an automatic injury labeling system, the system includes an acquisition module, an injury extraction module, a report analysis module, a comparison analysis module, and an injury labeling module; The acquisition module is used to acquire first video stream data of the forensic doctor performing injury examination on the injured person, and divide the first video stream data into multiple image segments according to the injury examination area; The injury extraction module is used to extract injury features from each image segment to obtain a corresponding first injury label and a corresponding injury position; The report analysis module is used to receive the text injury report generated by the forensic doctor, use the large language model to perform semantic understanding on the text injury report, and extract a plurality of second injury labels; The comparison analysis module is used to perform matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determine the corresponding second injury condition label as a target injury condition label; The injury labeling module is used to label each target injury label at the injury position corresponding to the matching first injury label in the target image; wherein the target image is any frame of high-definition image in the first video stream data.

[0043] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0044] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0045] The embodiment of the present invention further discloses a computer program product. When the computer program product is run on a terminal, the method described in the above embodiment is implemented.

[0046] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in the device according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0047] References herein to "one embodiment," "embodiment," or "one or more embodiments" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. In addition, please note that examples of the term "in one embodiment" do not necessarily all refer to the same embodiment.

[0048] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0049] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0050] In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or limiting the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative rather than restrictive with respect to the scope of the present invention, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for automatically labeling injury labels, characterized in that: The method comprises the following steps: Acquire first video stream data of a forensic doctor performing injury examination on an injured person, and divide the first video stream data into multiple image segments according to the injury examination area; Extract injury features from each image segment to obtain the corresponding first injury label and the corresponding injury location; receiving a text injury report generated by a forensic doctor, using a large language model to perform semantic understanding on the text injury report, and extracting a plurality of second injury labels; Performing a matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determining the corresponding second injury condition label as a target injury condition label; In a target image, each target injury label is marked at the injury position corresponding to the matching first injury label; wherein the target image is any frame of high-definition image in the first video stream data.

2. The method for automatically labeling injury labels according to claim 1, characterized in that: The target image is determined by: Acquire second video stream data containing the entire body area of ​​the injured person, and extract the external features and behavioral features of the injured person according to the second video stream data; Predicting the injury area distribution information of the injured person according to the external features and the behavioral features, and determining the category attribute of the target image according to the injury area distribution information, wherein the category attribute includes a full-body image and a non-full-body image; If the determined category attribute of the target image is a full-body image, a high-definition image frame containing a full-body image of the injured person is intercepted from the second video stream data, and a plurality of high-definition image frames corresponding to the second injury label are intercepted from the first video stream data; If the determined category attribute of the target image is a non-full body image, a number of high-definition image frames corresponding to the second injury label are captured from the first video stream data.

3. The method for automatically labeling injury labels according to claim 2, characterized in that: The determining the category attribute of the target image according to the injury area distribution information includes: According to each injury point in the injury area distribution information, two injury points with the farthest distance are determined, and a connected area is drawn on the body image of the injured person based on the two injury points; The ratio of the area of ​​the connected region to the area of ​​the injured person's body image is calculated. If the ratio is greater than a threshold, the category attribute of the target image is determined to be a full-body image; otherwise, the category attribute of the target image is determined to be a non-full-body image.

4. The method for automatically labeling injury labels according to claim 3 is characterized by: The step of dividing the first video stream data into a plurality of image segments according to the injury detection area includes: Identifying the forensic doctor's injury examination action and the injured person's body area in the first video stream data, and determining the injury examination area according to the injury examination action and the injured person's body area; When the injury detection area changes beyond a preset distance, it is determined that there is a switch in the injury detection area, and a moment of a preset time before the switching moment is determined as a split node; The first video stream data is divided into a plurality of image segments according to each of the segmentation nodes.

5. The method for automatically labeling injury labels according to claim 1, characterized in that: A large language model is used to perform semantic understanding on the text injury assessment report, and several second injury labels are extracted, including: Obtaining an injury report template from a database, and determining an injury record area according to the injury report template; An injury record area in the text injury assessment report is identified, and a large language model is used to perform semantic understanding on the text content in the injury record area to extract a plurality of the second injury labels.

6. The method for automatically labeling injury labels according to claim 1, characterized in that: The second injury condition label is matched and analyzed with the first injury condition label, and if no matching result is obtained, then: Obtaining the injury position corresponding to the second injury label, querying whether a transmission medical image corresponding to the injury position is stored in a database, and if so, determining a third injury label corresponding to the injury position according to the transmission medical image; If the second injury condition label matches the third injury condition label, determining the second injury condition label as the target injury condition label; If the database does not store the transmission medical image corresponding to the injury position or the second injury label does not match the third injury label, a manual confirmation prompt message is output.

7. An automatic injury labeling system, the system comprising an acquisition module, an injury extraction module, a report analysis module, a comparison analysis module, and an injury labeling module; The acquisition module is used to acquire first video stream data of the forensic doctor performing injury examination on the injured person, and divide the first video stream data into multiple image segments according to the injury examination area; The injury extraction module is used to extract injury features from each image segment to obtain a corresponding first injury label and a corresponding injury position; The report analysis module is used to receive the text injury report generated by the forensic doctor, use the large language model to perform semantic understanding on the text injury report, and extract a plurality of second injury labels; The comparison analysis module is used to perform matching analysis on the second injury condition label and the first injury condition label, and if a matching result is obtained, determine the corresponding second injury condition label as a target injury condition label; The injury labeling module is used to label each target injury label at the injury position corresponding to the matched first injury label in the target image; wherein, The target image is any frame of high-definition image in the first video stream data.

8. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that: When the computer program product runs on a terminal, the method according to any one of claims 1 to 6 is implemented.

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