Safety warning method and related device

Abstract

Embodiments of the present application provide a safety warning method and related device, applied to a cloud platform, the method comprising: receiving image information of a target area sent by a collection device, the image information comprising a target image and K label information; obtaining logical information between the K label information; determining warning discrimination information according to the logical information between the K label information; performing warning discrimination according to the target image and the warning discrimination information to obtain a discrimination result; and performing safety warning according to the discrimination result, which can perform safety warning through the cloud platform, thereby improving the adaptability of safety warning in different scenarios.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a security early warning method and related device. Background Technology

[0002] In traditional cloud-edge AI applications, the cloud platform is generally only responsible for collecting and forwarding information from edge devices; packaged algorithms (referring to algorithm products with complete business judgments, such as a warning algorithm for whether a safety helmet is being worn) are deployed on the edge (the data collection device side). In other words, the calculation results on the edge side are often the final calculation results, making it difficult to customize recognition according to different scenarios.

[0003] For example, in scenario a, the workwear is defined as a red short-sleeved shirt; in scenario b, the workwear is defined as a yellow long-sleeved shirt. If the algorithm is deployed at the edge, it can only identify either scenario a or b. Therefore, deploying the early warning algorithm at the edge reduces deployment flexibility and makes it unsuitable for multiple application scenarios. Summary of the Invention

[0004] This application provides a security early warning method and related device, which can perform security early warning through a cloud platform, thereby improving the adaptability of security early warning in different scenarios.

[0005] A first aspect of this application provides a security early warning method applied to a cloud platform, the method comprising:

[0006] Receive image information of the target area sent by the acquisition device, the image information including the target image and K tag information;

[0007] Obtain the logical information between the K tags;

[0008] Based on the logical information between the K tags, the warning and discrimination information is determined;

[0009] Based on the target image and the warning discrimination information, a warning discrimination is performed to obtain a discrimination result;

[0010] A security warning will be issued based on the judgment results.

[0011] A first aspect of this application provides a security early warning method applied to a data acquisition device, the data acquisition device including an electronic device, the method comprising:

[0012] Acquire target images of the target area;

[0013] Determine K tag information corresponding to the target image;

[0014] The cloud platform sends image information of the target area, including the target image and K tag information, so that the cloud platform can determine warning discrimination information based on the logical information between the K tag information, perform warning discrimination based on the target image and the warning discrimination information, obtain discrimination result, and issue a security warning based on the discrimination result.

[0015] A third aspect of this application provides a server including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0016] A fourth aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the second aspect of this application.

[0017] A fifth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first or second aspect of this application.

[0018] A sixth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first or second aspect of this application. The computer program product may be a software installation package.

[0019] Implementing the embodiments of this application has at least the following beneficial effects:

[0020] By receiving image information of the target area sent by the acquisition device, the image information includes a target image and K tag information. The logical information between the K tag information is obtained. Based on the logical information between the K tag information, warning discrimination information is determined. Warning discrimination is performed based on the target image and the warning discrimination information to obtain the discrimination result. A safety warning is issued based on the discrimination result. Therefore, the warning discrimination logic can be determined based on the logical information between the tag information in the image information, thereby adapting to the warning discrimination in this scenario and improving the flexibility of warning discrimination. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This application provides a schematic diagram of a security early warning system as an embodiment;

[0023] Figure 2 An interactive diagram illustrating a security early warning method is provided as an example in this application;

[0024] Figure 3 This application provides another interactive schematic diagram of a security early warning method.

[0025] Figure 4 This application provides a flowchart illustrating a security early warning method.

[0026] Figure 5 This application provides a schematic diagram of the structure of a server according to an embodiment of the present application.

[0027] Figure 6 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0028] Figure 7 This application provides a schematic diagram of the structure of a safety early warning device.

[0029] Figure 8 This application provides a schematic diagram of the structure of a safety warning device. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0032] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0033] To better understand the security warning method provided in this application embodiment, a brief introduction to the security warning system that applies the security warning method is given below.

[0034] Please see Figure 1 , Figure 1 This diagram illustrates a security early warning system as provided in an embodiment of this application. Figure 1 As shown, the security early warning system includes a server and acquisition equipment. The acquisition equipment includes electronic devices, such as cameras for information collection. The cloud platform is applied to the server; specifically, it can be understood as the cloud platform running on the server. The acquisition equipment acquires target images of a target area, which can be an area requiring security detection and early warning, such as a factory, construction site, office, or office park. Specifically, it can be a specific area within these areas, such as the access control area at a factory entrance or the office area of ​​an office. The acquisition equipment determines K tag information corresponding to the target image. This K tag information is used for subsequent early warning judgment. The acquisition equipment sends the image information, including the target image and the K tag information, to the server. The cloud platform on the server determines early warning judgment information based on the logical information between the K tag information. Based on this early warning judgment information and the target image, it performs early warning judgment on the target area to obtain a judgment result. Based on this judgment result, a security early warning is issued; for example, if the judgment result is negative, an early warning is issued. Therefore, the warning judgment logic can be determined based on the logical information between the label information in the image information, thereby adapting to the warning judgment in this scenario and improving the flexibility of warning judgment.

[0035] Please see Figure 2 , Figure 2An interactive diagram illustrating a security alert method is provided as an example in this application. For instance... Figure 2 As shown, the specific safety early warning methods include:

[0036] 201. The acquisition device acquires target images of the target area.

[0037] The data acquisition device can be an electronic device, which includes a camera.

[0038] The target area can be an area requiring security monitoring and early warning, such as a factory, construction site, office, or office park. Specifically, it can be a specific area within these areas, such as the access control area at a factory entrance or the office area of ​​an office. The target image can include images of users, vehicles, and common objects. Common objects can include signs, streetlights, and equipment.

[0039] 202. The acquisition device determines K tag information corresponding to the target image.

[0040] The data acquisition device can determine K tags corresponding to the target image from a database. Alternatively, it can determine K tags based on the target area. This target area can be pre-set with corresponding tags for early warning identification. Tags such as "human body," "face," "head," "torso," "vehicle type," and "type of common objects" can be used. Different areas can have different tags because the content requiring early warning varies. For example, if the target area is in an office space and the goal is to determine employee attire (whether they are wearing uniforms), the tags could be: "human body," "red short-sleeved shirt."

[0041] 203. The acquisition device sends image information of the target area to the cloud platform, the image information including the target image and the K tag information.

[0042] The acquisition device can send image information to the cloud platform via wired or wireless means.

[0043] The cloud platform receives image information of the target area sent by the acquisition device. After receiving the image information, the cloud platform can perform subsequent security warning operations.

[0044] 204. The cloud platform obtains the logical information between the K tags.

[0045] The cloud platform can obtain logical information between tag information based on the scene information corresponding to the target area. Logical information can include AND, NOT, AND, and overlap. For example, if the target area is an office space, and employee attire needs to be assessed to determine if they are wearing work uniforms, the logical information for tags indicating a human body and a red short-sleeved shirt could be: the tag information corresponding to a human body, and the tag information corresponding to a red short-sleeved shirt. As another example, when determining whether a vehicle is a new energy vehicle, the logical information can include AND and AND operations. Specifically, if the tags can be "car," "license plate number," and "license plate color," the logical information could be: the tag information corresponding to the car, the tag information corresponding to the license plate number, and the tag information corresponding to the license plate color. In other words, if the vehicle is a car, the license plate number is a new energy vehicle number, and the license plate color is green, then the vehicle is determined to be a new energy vehicle.

[0046] 205. The cloud platform determines the early warning judgment information based on the logical information between the K tags.

[0047] The system can logically combine K tags based on logical information to obtain warning and discrimination information. For example, if the logical information includes "AND" and the tags indicate "human body" and "red short-sleeved shirt," then the warning and discrimination information could be: "human body and red short-sleeved shirt." As another example, when determining whether a vehicle is a new energy vehicle, the warning and discrimination information could be: "If the vehicle is a car, the license plate number is a new energy vehicle number, and the license plate color is green, then the vehicle is determined to be a new energy vehicle." Furthermore, regarding the degree of overlap between identifiers, such as determining the degree of overlap between trademarks, the warning and discrimination information could be the degree of overlap between trademark 1 and trademark 2, etc.

[0048] 206. The cloud platform performs early warning judgment based on the target image and the early warning judgment information, and obtains the judgment result.

[0049] The cloud platform can extract features from the target image to obtain feature data, and then perform early warning discrimination based on the feature data and early warning discrimination information to obtain the discrimination result. Alternatively, it can determine the corresponding early warning discrimination template based on the early warning discrimination information, and then determine the discrimination result based on the early warning discrimination template. Of course, discrimination can also be performed through methods such as direct comparison to obtain the discrimination result.

[0050] 207. The cloud platform issues a security warning based on the judgment results.

[0051] The method for issuing safety warnings based on the judgment results can be as follows: if the judgment result matches the warning judgment information, the safety warning is empty; if the judgment result does not match the warning judgment information, the safety warning is issued. The warning information can be pre-set information used for issuing warnings. For example, it could be that the employee is not wearing work clothes.

[0052] In this example, image information of the target area sent by the acquisition device is received. The image information includes the target image and K tag information. Logical information between the K tag information is obtained. Based on the logical information between the K tag information, warning discrimination information is determined. Warning discrimination is performed based on the target image and the warning discrimination information to obtain the discrimination result. A safety warning is issued based on the discrimination result. Therefore, the warning discrimination logic can be determined based on the logical information between the tag information in the image information, thereby adapting to the warning discrimination in this scenario and improving the flexibility of warning discrimination.

[0053] In one possible implementation, a possible method for performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes:

[0054] A1. Perform feature extraction on the target image to obtain feature data;

[0055] A2. Obtain sub-feature data corresponding to the K label information from the feature data;

[0056] A3. Based on the sub-feature data corresponding to the K tag information and the early warning discrimination information, perform early warning discrimination to obtain the discrimination result.

[0057] The method for feature extraction of the target image can employ general feature extraction algorithms, and the feature data can be grayscale values, RGB values, etc. Different label information has its corresponding sub-feature data, which can be determined based on the label indicated by the label information. Of course, among the K label information, there may be some that do not have corresponding sub-feature data. For example, if the label information indicates a person, but the target image does not contain a person, then there is no corresponding sub-feature data, and in this case, the sub-feature data is recorded as empty.

[0058] The warning boundary value can be determined based on the warning discrimination information, and discrimination can be performed based on the sub-feature data to obtain the discrimination result. If the sub-feature information is empty, it can be directly determined that the discrimination result does not conform to the warning discrimination information.

[0059] In this example, by extracting sub-feature data corresponding to the label information from the target image, compared to using all feature data for early warning discrimination, only the sub-feature data corresponding to the label information needs to be discriminated, thereby reducing the amount of data to be discriminated, reducing system overhead, and improving discrimination efficiency.

[0060] In one possible implementation, a possible method for performing early warning discrimination based on sub-feature data corresponding to the K label information and the early warning discrimination information to obtain a discrimination result includes:

[0061] B1. Determine the warning boundary value based on the aforementioned warning discrimination information;

[0062] B2. Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value;

[0063] B3. Determine the discrimination result based on the offset information.

[0064] The method for determining warning boundary values ​​based on warning discrimination information can be as follows: The warning boundary values ​​are determined based on the label information within the warning discrimination information. Each label has a corresponding warning boundary value. Specifically, the warning boundary values ​​can be determined based on the warning fluctuation range indicated by the label information. This warning fluctuation range can be understood as: within this range, information that does not require a warning. For example, if the label information is "red clothing (color code 1)," and the warning fluctuation range is: light red clothing (color code 0), red clothing, and dark red clothing (color code 2), then the warning boundary values ​​could be: light red clothing (color code 0) and dark red clothing (color code 2). Clothing of other red colors would then be outside the warning boundary values.

[0065] The offset information between the sub-feature data and the warning boundary value can be understood as the average of the differences between the sub-feature data and the warning boundary value. For example, if the warning boundary value has two boundary values, the differences between the sub-feature data and the two warning boundary values ​​are obtained respectively, and the average of these differences is determined as the offset information.

[0066] The offset information is compared with the preset offset information. Since there are multiple offset information, if any offset information is greater than the preset offset information, the judgment result is that it does not match the warning judgment information. If all offset information is less than the preset offset information, the judgment result is that it matches the warning judgment information.

[0067] In this example, the warning boundary value is determined by the warning discrimination information, and the discrimination result is determined by the offset information between the sub-feature data and the warning boundary value. The warning information can be represented by the warning boundary value, so parallel discrimination can be performed, which can improve the efficiency of discrimination.

[0068] In one possible implementation, a possible method for performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes:

[0069] C1. Determine the early warning judgment template based on the aforementioned early warning judgment information;

[0070] C2. Determine the warning discrimination region in the target image based on the warning discrimination template;

[0071] C3. Extract the image from the target image within the warning discrimination region to obtain the image of the region to be discriminated;

[0072] C4. Perform early warning discrimination based on the image of the region to be discriminated and the early warning discrimination template to obtain the discrimination result.

[0073] Different warning judgment information corresponds to different warning judgment templates, which are pre-set templates. These templates can roughly indicate the warning judgment area. Since the acquisition device can be set in a fixed position, the acquired area in the image remains relatively fixed if there is no displacement, and therefore the warning area within it will also be relatively fixed. Thus, the warning judgment area can be indicated according to the warning judgment template.

[0074] Based on the location information of the warning discrimination area in the image, the image of the warning discrimination area can be extracted from the target image to obtain the image of the area to be discriminated.

[0075] Based on the information extraction method indicated by the early warning discrimination template, key information can be extracted from the image of the region to be discriminated against, and discrimination can be performed based on the key information. This information extraction method can involve indicating the region description information, determining the image region to be discriminated against based on the region description information, and then extracting the key information. The key information is information associated with the label information. For example, if the label information is "red short-sleeved shirt," then the key information is color and clothing type. If the key information is different from the label information, the discrimination result is that it does not match the early warning discrimination information; if the key information is the same as the label information, the discrimination result is that it matches the early warning discrimination information.

[0076] In this example, the warning discrimination template determined by the warning discrimination information can identify the warning discrimination area. This allows processing of the information carried in the image of the area to be discriminated within the warning discrimination area, thereby improving the efficiency of warning discrimination.

[0077] In one possible implementation, a possible method for determining the warning discrimination region in the target image based on the warning discrimination template includes:

[0078] D1. Determine the area description information based on the aforementioned early warning judgment template;

[0079] D2. Based on the region description information, determine N reference early warning discrimination regions in the target image, wherein the reference early warning discrimination regions are the initial early warning discrimination regions in the target image;

[0080] D3. Obtain the type similarity between the target image and a preset standard image;

[0081] D4. Based on the aforementioned type similarity, determine the adjustment information for the early warning discrimination area;

[0082] D5. Based on the adjustment information of the warning discrimination area and the N reference warning discrimination areas, determine the warning discrimination area in the target image.

[0083] Where N is a positive integer less than or equal to K. The region description information can include the region's location, size, and shape. This region description information is used to identify a pre-defined warning detection region, which can be an initial warning detection region, i.e., a default warning detection region associated with the acquisition device. Since the position of the person or object being captured may deviate from the pre-defined region during image acquisition, correction of the warning detection is necessary.

[0084] During calibration, the type similarity between the target image and the preset standard image can be obtained. This type similarity can encompass similarities in aspects such as shooting type, user location, and user orientation.

[0085] The greater the type similarity, the smaller the adjustment of the information indication; conversely, the smaller the type similarity, the larger the adjustment of the information indication. Adjusting the information indication can be understood as adjusting the reference warning discrimination region to a position that matches the warning discrimination region in the target image, thus obtaining the warning discrimination region in the target image. Of course, adjusting the information indication can also include adjusting the size of the reference warning discrimination region.

[0086] In this example, since the position of the person or object being captured may deviate from the pre-set area during image acquisition, the reference warning discrimination area is adjusted by the type similarity between the target image and the preset standard image to obtain the warning discrimination area in the target image, thereby improving the accuracy of determining the warning discrimination area in the target image.

[0087] In one possible implementation, a possible method for obtaining the type similarity between the target image and a preset standard image, based on the target image including the target user, includes:

[0088] E1. Obtain the first shooting direction of the target image;

[0089] E2. Determine the first similarity between the first shooting direction and the second shooting direction of the standard image;

[0090] E3. Obtain the location information of the feature points of the target user in the target image;

[0091] E4. Determine the first location information of the target user based on the location information of the feature points of the target user;

[0092] E5. Determine the second similarity between the first location information of the target user and the second location information of the user in the standard image;

[0093] E6. Determine the third similarity between the orientation information of the target user and the orientation information of the user in the standard image;

[0094] E7. Determine the type similarity based on the first similarity, the second similarity, and the third similarity.

[0095] The method for obtaining the first shooting direction of the target image can be as follows: acquire the angle information of the camera in the acquisition device, and determine the first shooting direction based on the camera angle information. Specifically, for example, the camera angle information can be directly determined as the first shooting direction. Alternatively, the camera angle information can be offset by a preset value to obtain the shooting direction. This preset offset is set through empirical values ​​or historical data. The standard image can be an image acquired by the acquisition device, which is then used as the comparison image to obtain the standard image.

[0096] The first similarity between the first shooting direction and the second shooting direction can be understood as follows: the larger the angle between the first shooting direction and the second shooting direction, the lower the similarity; the smaller the angle between the first shooting direction and the second shooting direction, the higher the similarity.

[0097] The feature points of a target user can be understood as the feature points corresponding to the information associated with the target user and the tag information. For example, if the tag information is "red short-sleeved shirt," then the feature points could be the hem or collar of the shirt. The center point of multiple feature points can be determined, and the location information of this center point can be used to determine the location information of the target user. The center point can be determined using a general center point acquisition algorithm.

[0098] The second similarity between the first location information and the second location information can be understood as follows: the greater the distance between the location indicated by the first location information and the location indicated by the second location information, the smaller the similarity; the smaller the distance between the location indicated by the first location information and the location indicated by the second location information, the greater the similarity.

[0099] The target user's facial orientation can be determined as the target user's orientation, thus allowing us to obtain a third similarity score between the target user's orientation information and the orientation information of users in a standard image. The smaller the angle between the orientations, the greater the third similarity score; conversely, the larger the angle between the orientation information, the smaller the third similarity score.

[0100] The type similarity can be determined by the average of the first similarity, the second similarity, and the third similarity, or it can be determined by the minimum of the first similarity, the second similarity, and the third similarity.

[0101] In this example, the type similarity is determined by the first similarity between the determined first shooting direction and the second shooting direction of the standard image, the second similarity between the determined first location information of the target user and the second location information of the user in the standard image, and the third similarity between the determined orientation information of the target user and the orientation information of the user in the standard image. This can improve the accuracy of type similarity determination.

[0102] Please see Figure 3 , Figure 3 This application provides a flowchart illustrating the intent of a security early warning method. For example... Figure 3 As shown, the security early warning method is applied to the cloud platform, and the method includes:

[0103] 301. The acquisition device acquires target images of the target area.

[0104] The data acquisition device can be an electronic device, which includes a camera.

[0105] The target area can be an area requiring security monitoring and early warning, such as a factory, construction site, office, or office park. Specifically, it can be a specific area within these areas, such as the access control area at a factory entrance or the office area of ​​an office. The target image can include images of users, vehicles, and common objects. Common objects can include signs, streetlights, and equipment.

[0106] 302. The acquisition device determines K tag information corresponding to the target image.

[0107] The data acquisition device can determine K tags corresponding to the target image from a database. Alternatively, it can determine K tags based on the target area, which also includes corresponding image information. The target area can be pre-set with corresponding tags for early warning identification. Tags such as "human body," "face," "head," "torso," "vehicle type," and "type of common objects" can be used. Different areas can have different tags due to varying warning requirements. For example, if the target area is an office space and the goal is to determine employee attire (e.g., whether they are wearing uniforms), the tags could be: "human body," "red short-sleeved shirt." For instance, if the tag is "face," the corresponding image could be a sub-image of the target image containing a face. The acquisition device extracts the indicated information to obtain the image corresponding to the tag.

[0108] 303. The acquisition device sends image information of the target area to the cloud platform, the image information including the target image and the K tag information.

[0109] The acquisition device can send image information to the cloud platform via wired or wireless means.

[0110] The cloud platform receives image information of the target area sent by the acquisition device. After receiving the image information, the cloud platform can perform subsequent security warning operations.

[0111] 304. The cloud platform obtains the logical information between the K tags.

[0112] The cloud platform can obtain logical information between tag information based on the scene information corresponding to the target area. Logical information can include: AND, NOT, AND, overlap, etc.

[0113] 305. The cloud platform performs early warning judgment on the K tag information based on the logical information between the K tag information, and obtains the early warning judgment result.

[0114] The system can logically combine K tags based on logical information to obtain warning information. For example, if the logical information includes "AND" and the tags indicate "human body" and "red short-sleeved shirt", then the warning information could be: "human body and red short-sleeved shirt".

[0115] The cloud platform can extract features from the image corresponding to the tag information to obtain feature data. Based on this feature data and early warning discrimination information, it can then perform early warning discrimination to obtain the discrimination result. Alternatively, it can determine the corresponding early warning discrimination template based on the early warning discrimination information and then determine the discrimination result based on the template. Of course, it can also perform discrimination through direct comparison or other methods to obtain the discrimination result.

[0116] 306. The cloud platform issues a security warning based on the judgment results.

[0117] The method for issuing safety warnings based on the judgment results can be as follows: if the judgment result matches the warning judgment information, the safety warning is empty; if the judgment result does not match the warning judgment information, the safety warning is issued. The warning information can be pre-set information used for issuing warnings. For example, it could be that the employee is not wearing work clothes.

[0118] In this example, the label information already includes the sub-image corresponding to the label, so the cloud platform does not need to extract the image corresponding to the label to make early warning judgments, which reduces the amount of computation on the cloud platform. Since the amount of data processed by the cloud platform is very large, reducing the amount of computation on the cloud platform can greatly improve the computational efficiency.

[0119] Please see Figure 4 , Figure 4 This application provides a flowchart illustrating the intent of a security early warning method. For example... Figure 4 As shown, the security early warning method is applied to the cloud platform, and the method includes:

[0120] 401. Receive image information of the target area sent by the acquisition device, wherein the image information includes the target image and K tag information;

[0121] 402. Obtain the logical information between the K tags;

[0122] 403. Determine the warning judgment information based on the logical information between the K tags;

[0123] 404. Based on the aforementioned warning judgment information, determine the warning judgment template;

[0124] 405. Determine the area description information based on the aforementioned early warning judgment template;

[0125] 406. Based on the region description information, determine N reference early warning discrimination regions, wherein the reference early warning discrimination regions are the initial early warning discrimination regions in the target image;

[0126] 407. Obtain the type similarity between the target image and a preset standard image;

[0127] 408. Based on the aforementioned type similarity, determine the adjustment information for the early warning discrimination area;

[0128] 409. Based on the adjustment information of the warning discrimination area and the N reference warning discrimination areas, determine the warning discrimination area in the target image;

[0129] 410. Extract the image from the target image within the warning discrimination region to obtain the image of the region to be discriminated;

[0130] 411. Perform early warning discrimination based on the image of the region to be discriminated and the early warning discrimination template to obtain the discrimination result;

[0131] 412. Issue a security warning based on the judgment results.

[0132] In this example, since the position of the person or object being captured may deviate from the pre-set area during image acquisition, the reference warning discrimination area is adjusted by the type similarity between the target image and the preset standard image to obtain the warning discrimination area in the target image, thereby improving the accuracy of determining the warning discrimination area in the target image.

[0133] For examples consistent with the above embodiments, please refer to... Figure 5 , Figure 5 A schematic diagram of a server structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0134] Receive image information of the target area sent by the acquisition device, the image information including the target image and K tag information;

[0135] Obtain the logical information between the K tags;

[0136] Based on the logical information between the K tags, the warning and discrimination information is determined;

[0137] Based on the target image and the warning discrimination information, a warning discrimination is performed to obtain a discrimination result;

[0138] A security warning will be issued based on the judgment results.

[0139] For examples consistent with the above embodiments, please refer to... Figure 6 , Figure 6 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, a camera, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0140] Acquire target images of the target area;

[0141] Determine K tag information corresponding to the target image;

[0142] The system sends image information of the target area to the cloud platform. The image information includes the target image and the K tag information. The target image is used to instruct the cloud platform to issue a security warning.

[0143] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0144] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0145] For those consistent with the above, please refer to Figure 7 , Figure 7 This application provides a schematic diagram of the structure of a safety warning device. Figure 7 As shown, the device is applied to a cloud platform, and the device includes:

[0146] The receiving unit 701 is used to receive image information of the target area sent by the acquisition device, wherein the image information includes the target image and K tag information;

[0147] Acquisition unit 702 is used to acquire logical information between the K tag information;

[0148] The determining unit 703 is used to determine the warning judgment information based on the logical information between the K tag information;

[0149] The discrimination unit 704 is used to perform early warning discrimination based on the target image and the early warning discrimination information, and obtain a discrimination result;

[0150] The early warning unit 705 is used to issue a safety warning based on the judgment result.

[0151] In one possible implementation, the discrimination unit 704 is used for:

[0152] Feature extraction is performed on the target image to obtain feature data;

[0153] Obtain sub-feature data corresponding to the K label information from the feature data;

[0154] Early warning judgment is performed based on the sub-feature data corresponding to the K tags and the early warning judgment information to obtain the judgment result.

[0155] In one possible implementation, in the step of performing early warning discrimination based on the sub-feature data corresponding to the K tag information and the early warning discrimination information to obtain a discrimination result, the discrimination unit 704 is used to:

[0156] Based on the aforementioned warning discrimination information, determine the warning boundary value;

[0157] Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value;

[0158] The discrimination result is determined based on the offset information.

[0159] In one possible implementation, the discrimination unit 704 is used for:

[0160] Based on the aforementioned warning discrimination information, a warning discrimination template is determined;

[0161] The warning discrimination region in the target image is determined according to the warning discrimination template;

[0162] Extract the image from the warning discrimination region from the target image to obtain the image of the region to be discriminated;

[0163] The warning is determined based on the image of the region to be judged and the warning judgment template to obtain the judgment result.

[0164] In one possible implementation, in determining the warning discrimination region in the target image based on the warning discrimination template, the discrimination unit 704 is configured to:

[0165] Based on the aforementioned early warning judgment template, N area description information are determined;

[0166] Based on the N region descriptions, N reference warning and discrimination regions are determined;

[0167] Obtain the type similarity between the target image and a preset standard image;

[0168] Based on the type similarity, the adjustment information for the early warning discrimination area is determined;

[0169] Based on the adjustment information of the warning discrimination region and the N reference warning discrimination regions, the warning discrimination region in the target image is determined.

[0170] In one possible implementation, the target image includes a target user, and the discrimination unit 704 is used to: Regarding obtaining the type similarity between the target image and a preset standard image,

[0171] Obtain the first shooting direction of the target image;

[0172] Determine a first similarity between the first shooting direction and the second shooting direction of the standard image;

[0173] Obtain the location information of feature points of the target user in the target image;

[0174] Based on the location information of the feature points of the target user, the first location information of the target user is determined;

[0175] Determine a second similarity between the first location information of the target user and the second location information of the user in the standard image;

[0176] Determine the third similarity between the orientation information of the target user and the orientation information of the user in the standard image;

[0177] The type similarity is determined based on the first similarity, the second similarity, and the third similarity.

[0178] For those consistent with the above, please refer to Figure 8 , Figure 8 This application provides a schematic diagram of the structure of a safety warning device. For example... Figure 8 As shown, the device is applied to a data acquisition device, which includes electronic components. The device includes:

[0179] Acquisition unit 801 is used to acquire target images of the target area;

[0180] Determining unit 802 is used to determine K tag information corresponding to the target image;

[0181] The sending unit 803 is used to send image information of the target area to the cloud platform. The image information includes the target image and the K tag information. The target image is used to instruct the cloud platform to issue a security warning.

[0182] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the security warning methods described in the above method embodiments.

[0183] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the security warning methods described in the above method embodiments.

[0184] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0185] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0189] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0190] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0191] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A safety early warning method, characterized in that, Applied to a cloud platform, the method includes: Receive image information of the target area sent by the acquisition device, the image information including the target image and K tag information; Obtain the logical information between the K tags; Based on the logical information between the K tags, the warning and discrimination information is determined; Based on the target image and the warning discrimination information, a warning discrimination is performed to obtain a discrimination result; A security warning will be issued based on the judgment results. The step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Feature extraction is performed on the target image to obtain feature data; Obtain sub-feature data corresponding to the K label information from the feature data; Based on the sub-feature data corresponding to the K tag information and the early warning discrimination information, an early warning discrimination is performed to obtain a discrimination result; The step of performing early warning discrimination based on the sub-feature data corresponding to the K tag information and the early warning discrimination information to obtain the discrimination result includes: Based on the aforementioned warning discrimination information, determine the warning boundary value; Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value; The discrimination result is determined based on the offset information.

2. A safety early warning method, characterized in that, Applied to a cloud platform, the method includes: Receive image information of the target area sent by the acquisition device, the image information including the target image and K tag information; Obtain the logical information between the K tags; Based on the logical information between the K tags, the warning and discrimination information is determined; Based on the target image and the warning discrimination information, a warning discrimination is performed to obtain a discrimination result; A security warning will be issued based on the judgment results. The step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Based on the aforementioned warning discrimination information, a warning discrimination template is determined; The warning discrimination region in the target image is determined according to the warning discrimination template; Extract the image from the warning discrimination region from the target image to obtain the image of the region to be discriminated; Based on the image of the region to be identified and the warning identification template, a warning identification is performed to obtain the identification result; The step of determining the warning discrimination region in the target image according to the warning discrimination template includes: Based on the aforementioned early warning discrimination template, determine the area description information; Based on the region description information, N reference early warning discrimination regions are determined in the target image, and the reference early warning discrimination regions are the initial early warning discrimination regions in the target image; Obtain the type similarity between the target image and a preset standard image; Based on the type similarity, the adjustment information for the early warning discrimination area is determined; Based on the adjustment information of the warning discrimination region and the N reference warning discrimination regions, the warning discrimination region in the target image is determined.

3. The method according to claim 2, characterized in that, The target image includes the target user, and obtaining the type similarity between the target image and a preset standard image includes: Obtain the first shooting direction of the target image; Determine a first similarity between the first shooting direction and the second shooting direction of the standard image; Obtain the location information of feature points of the target user in the target image; Based on the location information of the feature points of the target user, the first location information of the target user is determined; Determine a second similarity between the first location information of the target user and the second location information of the user in the standard image; Determine the third similarity between the orientation information of the target user and the orientation information of the user in the standard image; The type similarity is determined based on the first similarity, the second similarity, and the third similarity.

4. A safety early warning method, characterized in that, Applied to a data acquisition device, the data acquisition device including an electronic device, the method includes: Acquire target images of the target area; Determine K tag information corresponding to the target image; The cloud platform sends image information of the target area, including the target image and K tag information, so that the cloud platform determines warning discrimination information based on the logical information between the K tag information, performs warning discrimination based on the target image and the warning discrimination information, obtains discrimination results, and performs security warnings based on the discrimination results. The step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Based on the aforementioned warning discrimination information, a warning discrimination template is determined; The warning discrimination region in the target image is determined according to the warning discrimination template; Extract the image from the warning discrimination region from the target image to obtain the image of the region to be discriminated; Based on the image of the region to be identified and the warning identification template, a warning identification is performed to obtain the identification result; The step of determining the warning discrimination region in the target image according to the warning discrimination template includes: Based on the aforementioned early warning discrimination template, determine the area description information; Based on the region description information, N reference early warning discrimination regions are determined in the target image, and the reference early warning discrimination regions are the initial early warning discrimination regions in the target image; Obtain the type similarity between the target image and a preset standard image; Based on the type similarity, the adjustment information for the early warning discrimination area is determined; Based on the adjustment information of the warning discrimination region and the N reference warning discrimination regions, the warning discrimination region in the target image is determined; Alternatively, the step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Feature extraction is performed on the target image to obtain feature data; Obtain sub-feature data corresponding to the K label information from the feature data; Based on the sub-feature data corresponding to the K tag information and the early warning discrimination information, an early warning discrimination is performed to obtain a discrimination result; The step of performing early warning discrimination based on the sub-feature data corresponding to the K tag information and the early warning discrimination information to obtain the discrimination result includes: Based on the aforementioned warning discrimination information, determine the warning boundary value; Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value; The discrimination result is determined based on the offset information.

5. A safety early warning device, characterized in that, The device, applied to a cloud platform, includes: A receiving unit is used to receive image information of a target area sent by a data acquisition device, wherein the image information includes a target image and K tag information; The acquisition unit is used to acquire the logical information between the K tag information; The determining unit is used to determine the warning judgment information based on the logical information between the K tag information; The discrimination unit is used to perform early warning discrimination based on the target image and the early warning discrimination information, and obtain a discrimination result; The early warning unit is used to issue a safety warning based on the judgment result; The discrimination unit is specifically used for: Based on the aforementioned warning discrimination information, a warning discrimination template is determined; The warning discrimination region in the target image is determined according to the warning discrimination template; Extract the image from the warning discrimination region from the target image to obtain the image of the region to be discriminated; Based on the image of the region to be identified and the warning identification template, a warning identification is performed to obtain the identification result; In determining the warning discrimination region in the target image based on the warning discrimination template, the discrimination unit is specifically used for: Based on the aforementioned early warning discrimination template, determine the area description information; Based on the region description information, N reference early warning discrimination regions are determined in the target image, and the reference early warning discrimination regions are the initial early warning discrimination regions in the target image; Obtain the type similarity between the target image and a preset standard image; Based on the type similarity, the adjustment information for the early warning discrimination area is determined; Based on the adjustment information of the warning discrimination region and the N reference warning discrimination regions, the warning discrimination region in the target image is determined; Alternatively, the step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Feature extraction is performed on the target image to obtain feature data; Obtain sub-feature data corresponding to the K label information from the feature data; Based on the sub-feature data corresponding to the K tags and the warning discrimination information, a warning discrimination is performed to obtain a discrimination result; The step of performing early warning discrimination based on the sub-feature data corresponding to the K tag information and the early warning discrimination information to obtain the discrimination result includes: Based on the aforementioned warning discrimination information, determine the warning boundary value; Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value; The discrimination result is determined based on the offset information.

6. A safety early warning device, characterized in that, Applied to a data acquisition device, the data acquisition device including an electronic device, the device comprising: The acquisition unit is used to acquire target images of the target area; A determining unit is used to determine K tag information corresponding to the target image; The sending unit is used to send image information of the target area to the cloud platform. The image information includes the target image and the K tag information, so that the cloud platform can determine the warning discrimination information based on the logical information between the K tag information, perform warning discrimination based on the target image and the warning discrimination information, obtain the discrimination result, and perform a security warning based on the discrimination result. The step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Based on the aforementioned warning discrimination information, a warning discrimination template is determined; The warning discrimination region in the target image is determined according to the warning discrimination template; Extract the image from the warning discrimination region from the target image to obtain the image of the region to be discriminated; Based on the image of the region to be identified and the warning identification template, a warning identification is performed to obtain the identification result; The step of determining the warning discrimination region in the target image according to the warning discrimination template includes: Based on the aforementioned early warning discrimination template, determine the area description information; Based on the region description information, N reference early warning discrimination regions are determined in the target image, and the reference early warning discrimination regions are the initial early warning discrimination regions in the target image; Obtain the type similarity between the target image and a preset standard image; Based on the type similarity, the adjustment information for the early warning discrimination area is determined; Based on the adjustment information of the warning discrimination region and the N reference warning discrimination regions, the warning discrimination region in the target image is determined; Alternatively, the step of performing early warning discrimination based on the target image and the early warning discrimination information to obtain a discrimination result includes: Feature extraction is performed on the target image to obtain feature data; Obtain sub-feature data corresponding to the K label information from the feature data; Based on the sub-feature data corresponding to the K tag information and the early warning discrimination information, an early warning discrimination is performed to obtain a discrimination result; The step of performing early warning discrimination based on the sub-feature data corresponding to the K tag information and the early warning discrimination information to obtain the discrimination result includes: Based on the aforementioned warning discrimination information, determine the warning boundary value; Obtain the offset information between the sub-feature data corresponding to the K label information and the warning boundary value; The discrimination result is determined based on the offset information.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-3.

Images