Millimeter wave fusion uniqueness detection method and device for coal mine

Abstract

The invention provides a coal mine millimeter wave fusion uniqueness detection method and device, and the method comprises the steps: obtaining the three-dimensional point cloud data of a target object when the target object reaches a coal mine wellhead; when the three-dimensional point cloud data of the target object is obtained, obtaining of identity verification data of the target object is triggered, and the identity verification data comprises biological characteristic information and intelligent card information; determining a contraband detection result according to the three-dimensional point cloud data; determining an identity verification result according to the biological characteristic information and the intelligent card information; and determining a coal mine well entering detection result according to the contraband detection result and the identity verification result. By implementing the method disclosed by the invention, the safety and reliability of coal mine well entering detection can be improved, comprehensive automatic detection is realized, and the degree of dependence on manual detection is effectively reduced.

Description

Technical Field

[0001] This disclosure relates to the field of coal mine entry inspection technology, specifically to a millimeter-wave fusion uniqueness detection method and device for coal mines. Background Technology

[0002] In coal mine safety production, entry inspection is a crucial link. Traditional methods of detecting contraband mainly rely on manual inspection, but this approach has many shortcomings, such as low detection efficiency, high risk of missed detections, and false positives. With technological advancements, automated detection technologies, such as millimeter-wave technology, have been gradually introduced into coal mine entry inspection. However, coal mine entry inspection is divided into two types: uniqueness detection (i.e., card-based detection and biometric detection) and contraband detection (i.e., whether dangerous items such as ignition sources are being brought into the mine).

[0003] In related technologies, the two detection methods are relatively independent, with no integrated detection approach. This leads to a cumbersome detection process and may affect the accuracy and comprehensiveness of the detection results due to information silos. Especially in the complex and ever-changing environment of coal mines, a single detection method may be insufficient to address various potential safety hazards. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the purpose of this disclosure is to propose a millimeter-wave fusion uniqueness detection method, device, computer equipment and storage medium for coal mines, which can improve the safety and reliability of coal mine entry detection, realize fully automated detection and effectively reduce the dependence on manual detection.

[0006] To achieve the above objectives, the first aspect of this disclosure provides a millimeter-wave fusion uniqueness detection method for coal mines, comprising:

[0007] When the target object arrives at the coal mine entrance, acquire the three-dimensional point cloud data of the target object;

[0008] When the three-dimensional point cloud data of the target object is acquired, the acquisition of the identity verification data of the target object is triggered, wherein the identity verification data includes: biometric information and smart card information;

[0009] The detection results of prohibited items are determined based on the aforementioned three-dimensional point cloud data;

[0010] The identity verification result is determined based on the biometric information and the smart card information;

[0011] Based on the results of the prohibited items detection and the results of the identity verification, the coal mine entry inspection results are determined.

[0012] To achieve the above objectives, the second aspect of this disclosure provides a millimeter-wave fusion uniqueness detection device for coal mines, comprising:

[0013] The first acquisition module is used to acquire the three-dimensional point cloud data of the target object when the target object arrives at the coal mine entrance;

[0014] The second acquisition module is used to trigger the acquisition of the identity verification data of the target object when the three-dimensional point cloud data of the target object is acquired, wherein the identity verification data includes: biometric information and smart card information;

[0015] The first determining module is used to determine the detection result of contraband based on the three-dimensional point cloud data;

[0016] The second determining module is used to determine the identity verification result based on the biometric information and the smart card information;

[0017] The third determining module is used to determine the coal mine entry inspection result based on the contraband detection result and the identity verification result.

[0018] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the millimeter-wave fusion uniqueness detection method for coal mines as proposed in the first aspect of this disclosure.

[0019] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the millimeter-wave fusion uniqueness detection method for coal mines as proposed in the first aspect of this disclosure.

[0020] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs the millimeter-wave fusion uniqueness detection method for coal mines as proposed in the first aspect of this disclosure.

[0021] The millimeter-wave fusion uniqueness detection method, device, computer equipment, and storage medium disclosed herein for coal mines acquire three-dimensional point cloud data of the target object when it arrives at the coal mine entrance; upon acquiring the three-dimensional point cloud data, the acquisition of the target object's identity verification data is triggered, including biometric information and smart card information; based on the three-dimensional point cloud data, the contraband detection result is determined; based on the biometric information and smart card information, the identity verification result is determined; and based on the contraband detection result and the identity verification result, the coal mine entry detection result is determined. This improves the safety and reliability of coal mine entry detection, achieves fully automated detection, and effectively reduces reliance on manual detection.

[0022] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This is a schematic flowchart of a millimeter-wave fusion uniqueness detection method for coal mines proposed in an embodiment of this disclosure;

[0025] Figure 2 This is based on the schematic diagram of the mine personnel entry and exit detection process presented in this disclosure;

[0026] Figure 3 This is a schematic diagram of the structure of a millimeter-wave fusion uniqueness detection device for coal mines according to an embodiment of this disclosure;

[0027] Figure 4 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0028] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0029] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0030] Figure 1 This is a schematic flowchart of a millimeter-wave fusion uniqueness detection method for coal mines proposed in one embodiment of this disclosure.

[0031] It should be noted that the execution subject of the millimeter-wave fusion uniqueness detection method for coal mines in this embodiment is a millimeter-wave fusion uniqueness detection device for coal mines. This device can be implemented by software and / or hardware. The device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a PDA, etc.

[0032] like Figure 1 As shown, the unique detection method for this coal mine using millimeter-wave fusion includes:

[0033] S101: When the target object arrives at the coal mine entrance, acquire the three-dimensional point cloud data of the target object.

[0034] The target group can refer to the workers in the coal mine.

[0035] Among them, three-dimensional point cloud data can refer to point cloud data obtained by scanning the entire body of a target object using millimeter-wave radar.

[0036] In other words, in this embodiment of the present disclosure, a millimeter-wave radar can be configured at the coal mine entrance. When personnel approach the area where the coal mine entrance is located, the millimeter-wave radar is automatically triggered to scan the entire body of the target object to obtain three-dimensional point cloud data, thereby providing reliable data support for subsequent detection of contraband.

[0037] S102: When acquiring the 3D point cloud data of the target object, trigger the acquisition of the target object's authentication data, which includes: biometric information and smart card information.

[0038] Among them, identity verification data can refer to the data obtained for verifying the identity of the target object.

[0039] Biometric information refers to biometric information that a target can use for identity verification. For example, it could refer to a fingerprint.

[0040] Among them, smart card information can refer to the relevant information contained in the smart card (work card) carried by the target object to indicate its own identity.

[0041] In this embodiment of the disclosure, when the authentication data of the target object is obtained, reliable data support can be provided for the subsequent determination of the authentication result.

[0042] S103: Determine the detection results of prohibited items based on 3D point cloud data.

[0043] Understandably, in order to ensure the safety of coal mine operations, it is necessary to prohibit personnel from bringing certain items into the coal mine, such as sources of ignition.

[0044] Among them, the results of contraband detection can be used to indicate whether a target is carrying contraband.

[0045] Optionally, in some embodiments, when determining the contraband detection result based on 3D point cloud data, the 3D point cloud data can be preprocessed, and the preprocessed 3D point cloud data can be input into a pre-trained contraband recognition model to determine the target object and the confidence level corresponding to the target object. The confidence level indicates the degree of confidence that the target object belongs to a contraband. Based on the confidence level, the contraband detection result is determined. This allows for a quantitative assessment of whether a target object is a contraband, thereby effectively improving the clarity of the obtained contraband detection result.

[0046] Among them, the contraband identification model can be, for example, a lightweight YOLOv7-MobileNet model.

[0047] The target object can refer to the object identified by the 3D point cloud data.

[0048] Optionally, in some embodiments, when determining the contraband detection result based on the confidence level, it may be as follows: determine the contraband determination result of the target object based on the confidence level; if the contraband determination result indicates that there is a target object that is a contraband in the target object, then the contraband detection result is unqualified; if the contraband determination result indicates that there is no target object that is a contraband in the target object, then the contraband detection result is qualified.

[0049] Optionally, in some embodiments, when determining the contraband status of a target object based on confidence level, the process may be as follows: if the confidence level is less than or equal to a first threshold, the target object is determined not to be a contraband; if the confidence level is greater than or equal to a second threshold, the target object is determined to be a contraband; if the confidence level is greater than the first threshold but less than the second threshold, the target object undergoes a second verification to determine a new confidence level. Thus, by combining the first and second thresholds, accurate determination of whether a target item is a contraband can be achieved, and a second verification is performed when the confidence level is greater than the first threshold but less than the second threshold, ensuring the robustness of the determination process.

[0050] The specific values ​​of the first and second thresholds can be flexibly adjusted according to the application scenario. For example, the first threshold could be 0.6, and the second threshold could be 0.9.

[0051] S104: Determine the identity verification result based on biometric information and smart card information.

[0052] Optionally, in some embodiments, when determining the authentication result based on biometric information and smart card information, the process may involve: determining a first acquisition time of the biometric information and a second acquisition time of the smart card information; determining the time difference between the first and second acquisition times; if the time difference is less than or equal to a third threshold, then determining the authentication result based on the biometric information and smart card information; if the time difference is greater than the third threshold, then acquiring new biometric information and new smart card information. Thus, by calculating the difference between the first and second acquisition times and comparing this difference with the third threshold, it can be ensured that the biometric information and smart card information belong to the same target object, thereby further improving the security of the coal mine system.

[0053] The third threshold can be flexibly configured according to the application scenario. For example, it can be set to 5ms.

[0054] Optionally, in some embodiments, the biometric information is the iris image of the target object. When determining the authentication result based on the biometric information and smart card information, the process may involve: determining the target hash value corresponding to the iris image; determining the reference hash value of the target object's iris based on the smart card information; determining the similarity between the target hash value and the reference hash value; if the similarity is greater than or equal to a fourth threshold, determining the authentication result as passed; if the similarity is less than the fourth threshold, determining the authentication result as failed. Thus, biometric information and smart card information can be fully combined to achieve authentication of the target object.

[0055] S105: Determine the coal mine entry inspection results based on the results of prohibited items detection and identity verification.

[0056] In this embodiment, when a target object arrives at the coal mine entrance, its 3D point cloud data is acquired. Upon acquiring this data, the acquisition of the target object's identity verification data is triggered. This data includes biometric information and smart card information. Based on the 3D point cloud data, the contraband detection result is determined. Based on the biometric information and smart card information, the identity verification result is determined. Based on the contraband detection result and the identity verification result, the coal mine entry inspection result is determined. This improves the safety and reliability of coal mine entry inspection, achieves fully automated inspection, and effectively reduces reliance on manual inspection.

[0057] Based on the above embodiments, this disclosure addresses the current problems of automated detection and identification in coal mines by proposing a method and system for millimeter-wave fusion uniqueness detection, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the mine personnel entry and exit detection process proposed in this disclosure. To achieve the above objectives, the present invention includes the following steps:

[0058] Step 1: When personnel approach the wellhead, the millimeter-wave radar scans their entire body and generates a contraband detection result. When personnel approach within 1.5 meters of the wellhead, the millimeter-wave radar array automatically activates, emitting a 77GHz frequency-modulated continuous wave (FMCW). The reflected signal is received via a MIMO antenna, generating high-density three-dimensional point cloud data (resolution up to 2cm). 3 Point cloud data, after preprocessing (denoising and clustering), is input into a lightweight YOLOv7-MobileNet model to detect metal tools (e.g., wrenches), non-metallic contraband (e.g., lighters), and liquid containers in real time. Detection results are labeled with bounding boxes, outputting the contraband category (10 preset categories + 1 unknown category) and confidence level (0-1). If the confidence level is ≥0.9, it is marked as a "high-risk item"; 0.6-0.9 triggers a secondary verification. Simultaneously, the millimeter-wave module sends a hardware trigger signal to the iris / RFID module via GPIO pins to initiate the authentication process.

[0059] Step 2: The iris camera and RFID or UWB reader (depending on the cards used by the mine) simultaneously collect biometric and smart card information: Upon arrival of the millimeter-wave trigger signal, the iris acquisition module activates 850nm infrared illumination and adjusts brightness using an adaptive exposure control (AEC) algorithm to capture a 1024×1024 pixel iris image under low-light conditions. After filtering with an anti-dust optical filter, the image is input into the preprocessing pipeline: First, ROI positioning (based on a U-Net segmentation network) is performed to extract the iris annular region; then, the Retinex enhancement algorithm is applied to separate the illumination and reflection components, improving texture contrast. Simultaneously, the UHF RFID or UWB reader scans the encrypted smart card worn by the personnel using the corresponding frequency band, decrypting the employee ID, access level, and pre-stored iris hash value stored on the card. The two types of data (iris image + RFID information) are aligned using hardware timestamps to ensure a collection time error ≤5ms and are bound to the same transaction ID.

[0060] Step 3: Edge computing unit execution:

[0061] a. Contraband Detection Model Inference → Output Contraband Category and Confidence Score: Convert millimeter-wave point clouds into a 2D top-down view, input it into a pre-trained ResNet-18 classification network, and perform secondary verification on the initial screening results to reduce the false alarm rate. If the two detection results conflict, initiate confidence fusion based on DS evidence theory and output the final judgment;

[0062] b. Iris Feature Extraction + Smart Card Decryption → Generation of Unique Hash Code: After preprocessing, the iris image is input into an improved DenseNet-iris network (with added CBAM attention module) to generate a 256-dimensional feature vector. This vector is then compared with the pre-stored iris hash value in the smart card using cosine similarity calculation, with a threshold set at 0.78 (FRR = 0.1%, FAR = 0.001%).

[0063] c. Two-factor authentication: Iris and smart card information matching + permission verification: The employee number after the smart card is decrypted is bound to the iris feature, and the local permission database (SQLite encrypted storage) is queried to verify whether the person has the current wellhead access permission and the timeliness of the operation (e.g., night shift personnel will be denied daytime access).

[0064] Step 4: Dynamic Decision Making

[0065] If the prohibited item test is positive or identity verification fails, an audible and visual alarm will be triggered and the gate will be closed.

[0066] If the two-factor authentication is successful and there are no prohibited items, log the information and release the vehicle.

[0067] Figure 3 This is a schematic diagram of the structure of a millimeter-wave fusion uniqueness detection device for coal mines according to an embodiment of this disclosure.

[0068] like Figure 3 As shown, the millimeter-wave fusion uniqueness detection device 30 for coal mines includes:

[0069] The first acquisition module 301 is used to acquire the three-dimensional point cloud data of the target object when the target object arrives at the coal mine entrance;

[0070] The second acquisition module 302 is used to trigger the acquisition of the identity verification data of the target object when acquiring the three-dimensional point cloud data of the target object. The identity verification data includes: biometric information and smart card information.

[0071] The first determining module 303 is used to determine the detection result of contraband based on the three-dimensional point cloud data;

[0072] The second determining module 304 is used to determine the identity verification result based on the biometric information and the smart card information;

[0073] The third determining module 305 is used to determine the coal mine entry inspection results based on the results of the prohibited items detection and the identity verification results.

[0074] It should be noted that the aforementioned explanation of the millimeter-wave fusion uniqueness detection method for coal mines also applies to the millimeter-wave fusion uniqueness detection device for coal mines in this embodiment, and will not be repeated here.

[0075] In this embodiment, when a target object arrives at the coal mine entrance, its 3D point cloud data is acquired. Upon acquiring this data, the acquisition of the target object's identity verification data is triggered. This data includes biometric information and smart card information. Based on the 3D point cloud data, the contraband detection result is determined. Based on the biometric information and smart card information, the identity verification result is determined. Based on the contraband detection result and the identity verification result, the coal mine entry inspection result is determined. This improves the safety and reliability of coal mine entry inspection, achieves fully automated inspection, and effectively reduces reliance on manual inspection.

[0076] Figure 4 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 4 The computer device 12 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0077] like Figure 4 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0078] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0079] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0080] Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 Not shown; usually referred to as a "hard drive".

[0081] although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a Compact Disc Read-Only Memory (CD-ROM), a Digital Video Disc Read-Only Memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0082] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0083] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0084] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the millimeter-wave fusion uniqueness detection method for coal mines mentioned in the foregoing embodiments.

[0085] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the millimeter-wave fusion uniqueness detection method for coal mines as proposed in the foregoing embodiments of this disclosure.

[0086] To implement the above embodiments, this disclosure also proposes a computer program product that, when executed by an instruction processor, performs the millimeter-wave fusion uniqueness detection method for coal mines as proposed in the foregoing embodiments of this disclosure.

[0087] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0088] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0089] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0090] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0091] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0092] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0094] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0097] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A millimeter-wave fusion uniqueness detection method for coal mines, characterized in that, include: When the target object arrives at the coal mine entrance, acquire the three-dimensional point cloud data of the target object; When the three-dimensional point cloud data of the target object is acquired, the acquisition of the identity verification data of the target object is triggered, wherein the identity verification data includes: biometric information and smart card information; The detection results of prohibited items are determined based on the aforementioned three-dimensional point cloud data; The identity verification result is determined based on the biometric information and the smart card information; Based on the results of the prohibited items detection and the results of the identity verification, the coal mine entry inspection results are determined.

2. The method as described in claim 1, characterized in that, The step of determining the detection result of contraband based on the three-dimensional point cloud data includes: The three-dimensional point cloud data is preprocessed and then input into a pre-trained contraband identification model to determine the target object and the confidence level corresponding to the target object. The confidence level is used to indicate the degree of confidence that the target object belongs to a contraband. The detection result of the prohibited item is determined based on the confidence level.

3. The method as described in claim 2, characterized in that, Determining the detection result of the prohibited item based on the confidence level includes: Based on the confidence level, the contraband determination result of the target object is determined; If the contraband determination result indicates that there is a contraband object among the target objects, then the contraband detection result is unqualified; If the contraband determination result indicates that there is no contraband target object among the target objects, then the contraband detection result is qualified.

4. The method as described in claim 3, characterized in that, The step of determining the contraband determination result of the target object based on the confidence level includes: If the confidence level is less than or equal to the first threshold, then it is determined that the target object is not a contraband. If the confidence level is greater than or equal to the second threshold, then the target object is determined to be a contraband. If the confidence level is greater than the first threshold and less than the second threshold, the target object is re-verified to determine a new confidence level.

5. The method as described in claim 1, characterized in that, The step of determining the identity verification result based on the biometric information and the smart card information includes: Determine the first acquisition time of the biometric information and the second acquisition time of the smart card information; Determine the time difference between the first acquisition time and the second acquisition time; If the time difference is less than or equal to the third threshold, the identity verification result is determined based on the biometric information and the smart card information. If the time difference is greater than the third threshold, new biometric information and new smart card information are obtained.

6. The method as described in claim 5, characterized in that, The biometric information is the iris image of the target object; The step of determining the authentication result based on the biometric information and the smart card information includes: Determine the target hash value corresponding to the iris image; Based on the smart card information, determine the reference hash value of the target object's iris; Determine the similarity between the target hash value and the reference hash value; If the similarity is greater than or equal to the fourth threshold, the authentication result is determined to be successful. If the similarity is less than the fourth threshold, the authentication result is determined to be unsuccessful.

7. A millimeter-wave fusion uniqueness detection device for coal mines, characterized in that, include: The first acquisition module is used to acquire the three-dimensional point cloud data of the target object when the target object arrives at the coal mine entrance; The second acquisition module is used to trigger the acquisition of the identity verification data of the target object when the three-dimensional point cloud data of the target object is acquired, wherein the identity verification data includes: biometric information and smart card information; The first determining module is used to determine the detection result of contraband based on the three-dimensional point cloud data; The second determining module is used to determine the identity verification result based on the biometric information and the smart card information; The third determining module is used to determine the coal mine entry inspection result based on the contraband detection result and the identity verification result.

8. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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