High-calcium limestone identification method, device and equipment, storage medium and program product

Abstract

The invention discloses a high-calcium limestone recognition method and device, equipment, a storage medium and a program product, and relates to the technical field of limestone recognition, the disclosed high-calcium limestone recognition method comprises the steps that to-be-recognized data is acquired, and the to-be-recognized data comprises a to-be-recognized limestone image and environment temperature and humidity information; based on the environment temperature and humidity information, performing temperature and humidity compensation processing on the to-be-identified limestone image to obtain a to-be-identified limestone compensation image; and carrying out identification processing on the to-be-identified limestone compensation image to obtain an identification result. According to the method, the influence of environmental factors on image recognition is fully considered, and the technical effect of improving the recognition efficiency and accuracy of the high-calcium limestone is achieved through a temperature and humidity compensation mechanism and an image processing technology.

Description

Technical Field

[0001] This application relates to the field of limestone identification technology, and in particular to methods, apparatus, equipment, storage media and program products for identifying high-calcium limestone. Background Technology

[0002] During limestone ore extraction, it is necessary to identify and screen high-calcium limestone. Traditional testing methods mainly rely on manual visual inspection or laboratory chemical analysis. Manual visual inspection is highly subjective due to differences in the visual judgment standards of different inspectors, making it difficult to guarantee the consistency and accuracy of the results. While single-sample laboratory chemical analysis can obtain relatively accurate compositional data, the testing time is long, which cannot provide timely information for production decisions in continuous production scenarios such as mining, cement plant raw material processing, and metallurgical furnace charge preparation, thus affecting efficiency.

[0003] Furthermore, in terms of using acquired images for identification, limestone ore images are easily affected by factors such as acquisition angle, lighting conditions, and surface coverings, resulting in significant identification errors when identifying acquired limestone images. Therefore, a method is needed to improve the accuracy and stability of image recognition. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, equipment, storage medium, and computer product for identifying high-calcium limestone, aiming to solve the technical problems of large deviation in identification results and low efficiency in the identification of high-calcium limestone in related technologies.

[0005] To achieve the above objectives, this application proposes a method for identifying high-calcium limestone, comprising: Acquire the data to be identified, which includes images of limestone to be identified and environmental temperature and humidity information; Based on environmental temperature and humidity information, temperature and humidity compensation processing is performed on the image of limestone to be identified to obtain a compensated image of limestone to be identified. The limestone compensation image to be identified is processed to obtain the recognition result; The temperature and humidity compensation processing includes compensating for the color features of the limestone image to be identified based on environmental humidity information, and compensating for the texture features of the limestone image to be identified based on environmental temperature information.

[0006] In one embodiment, the limestone to be identified is transported via a conveyor belt, and the step of acquiring the data to be identified includes: During the transport of limestone to be identified, the conveyor belt speed and the image of the limestone to be identified are acquired. The kernel size of the image filter is adjusted based on the transmission speed; the kernel size is positively correlated with the transmission speed. Based on image filters, the limestone image to be identified is filtered; Obtain the filtered image of the limestone to be identified.

[0007] In one embodiment, the step of compensating for the color features of the limestone image to be identified based on environmental humidity information includes: If the ambient humidity exceeds the first humidity threshold, the CLAHE algorithm is used to perform histogram equalization on the limestone image to be identified, and the processed limestone image to be identified is obtained.

[0008] In one embodiment, the step of compensating for the texture features of the limestone image to be identified based on ambient temperature information includes: Determine the gray-level co-occurrence matrix of the limestone image to be identified; If the ambient temperature exceeds the first temperature threshold, the energy value of the gray-level co-occurrence matrix is ​​compensated; the degree of energy compensation is positively correlated with the ambient temperature. If the ambient temperature is lower than the second temperature threshold, the entropy value of the gray-level co-occurrence matrix is ​​compensated; the degree of entropy compensation is positively correlated with the ambient temperature.

[0009] In one embodiment, the step of performing recognition processing on the limestone compensation image to be identified and obtaining the recognition result includes: Feature extraction is performed on the limestone image to be identified after temperature and humidity compensation processing to obtain the feature information of the limestone image to be identified. The feature information and environmental temperature and humidity information are input into the high-calcium limestone identification model to obtain the identification results; the identification results include limestone calcium content information. The feature information includes color features and texture features. The color features include a first color feature and a second color feature. The first color feature includes the hue information H and brightness information V of the limestone image to be identified in the HSV color space. The second color feature includes the red channel intensity value R and the green channel intensity value G of the limestone image to be identified in the RGB color space. Texture features include the energy and entropy values ​​of the gray-level co-occurrence matrix of the limestone image to be identified.

[0010] In one embodiment, after the step of extracting features from the limestone image to be identified after temperature and humidity compensation processing to obtain the feature information of the limestone image to be identified, the method further includes the following step: If the ambient humidity exceeds the second humidity threshold, the brightness information V will be processed by gain according to the preset gain ratio. If the ambient temperature exceeds the third temperature threshold, the intensity value R of the red channel will be adjusted based on the preset attenuation coefficient.

[0011] Furthermore, to achieve the above objectives, this application also provides a high-calcium limestone identification device, characterized in that the device comprises: The acquisition module is used to acquire the data to be identified, which includes images of limestone to be identified and environmental temperature and humidity information. The compensation module is used to perform temperature and humidity compensation processing on the limestone image to be identified based on the ambient temperature and humidity information to obtain a compensated image of the limestone to be identified. The temperature and humidity compensation processing includes compensating for the color features of the limestone image to be identified based on the ambient humidity information and compensating for the texture features of the limestone image to be identified based on the ambient temperature information. The recognition module is used to process the limestone compensation image to be recognized and obtain the recognition result.

[0012] In addition, to achieve the above objectives, this application also provides a high-calcium limestone identification device, the device including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the above-described high-calcium limestone identification method.

[0013] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it implements the steps of the above-described high-calcium limestone identification method.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described high-calcium limestone identification method.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application provides a method for identifying high-calcium limestone that combines image processing and environmental perception, effectively improving recognition accuracy and real-time performance in complex production environments. By incorporating environmental temperature and humidity information, targeted temperature and humidity compensation processing is applied to limestone images, reducing the impact of environmental changes on image features. Simultaneously, image filter parameters are dynamically adjusted based on transmission speed to enhance image clarity and stability. Multidimensional color and texture features are comprehensively extracted and input into the recognition model along with environmental information, enabling a more comprehensive and accurate determination of limestone calcium content. This achieves intelligent identification and precise classification of high-calcium limestone, meeting the demands for rapid and stable identification in continuous production scenarios. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the high-calcium limestone identification method of this application.

[0019] Figure 2 This is a schematic diagram of the high-calcium limestone identification device of this application.

[0020] Figure 3 This is a schematic diagram of the high-calcium limestone identification system of this application.

[0021] Figure 4 This is a schematic diagram of the high-calcium limestone identification equipment of this application.

[0022] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] Based on the problems mentioned in the background art, this application provides a method for identifying high-calcium limestone, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the high-calcium limestone identification method of this application.

[0026] In this embodiment of the application, the design idea of ​​the text intent reconstruction method is as follows: the acquired limestone image to be identified is compensated based on the ambient temperature and humidity, and then the processed image is feature extracted, and high-calcium limestone is identified based on the feature information.

[0027] Specifically, in this embodiment, the method for identifying high-calcium limestone includes steps S10 to S30: Step S10: Obtain the data to be identified, which includes the image of the limestone to be identified and the environmental temperature and humidity information.

[0028] It should be noted that the image of the limestone to be identified is an image of the limestone to be identified that has been collected. However, this embodiment can be used to identify limestone during the transmission process, such as the limestone pre-sorting scenario in a cement plant. In this scenario, the limestone to be identified is being transported on a conveyor belt. The image of the limestone to be identified can be acquired in real time at a frame rate of 30fps using a high-definition industrial camera, and the real-time environmental temperature and humidity information can be read by a temperature and humidity sensor for subsequent processing.

[0029] Understandably, the above methods not only ensure the high quality and timeliness of the data, but also enhance the system's adaptability to complex environments, improve production efficiency, and reduce operating costs, laying a solid foundation for subsequent image processing and recognition.

[0030] In one feasible implementation, the limestone to be identified is transported by a conveyor belt, and step S10 further includes steps A10 to A40: Step A10: During the conveying process of the limestone to be identified, acquire the conveyor belt speed and the image of the limestone to be identified.

[0031] Step A20: Based on the transmission speed, adjust the kernel size of the image filter. The kernel size is positively correlated with the transmission speed.

[0032] Step A30: Based on the image filter, the limestone image to be identified is filtered.

[0033] Step A40: Obtain the filtered image of the limestone to be identified.

[0034] In this embodiment, a filtering method for the limestone image to be identified is provided to eliminate noise caused by the movement and transportation of limestone and avoid excessive image blurring.

[0035] Specifically, the conveyor belt speed is first obtained, and the filter parameters are dynamically adjusted based on this speed. For example, a Gaussian filter is used. When the conveyor belt speed is high, the filter kernel size is increased to remove noise generated during rapid acquisition; when the speed is slow, the filter kernel size is decreased to avoid excessive image blurring and to preserve key features such as mineral texture details to the greatest extent possible. Filtering the limestone image to be identified using Gaussian filtering provides a high-quality image foundation for subsequent analysis.

[0036] Step S20: Based on the ambient temperature and humidity information, perform temperature and humidity compensation processing on the limestone image to be identified to obtain the compensated limestone image to be identified. The temperature and humidity compensation processing includes compensating for the color features of the limestone image to be identified based on environmental humidity information, and compensating for the texture features of the limestone image to be identified based on environmental temperature information.

[0037] It should be noted that the temperature and humidity compensation process includes temperature compensation and humidity compensation, which are used to enhance image quality and correct features based on ambient temperature and humidity information. In this embodiment, the temperature and humidity compensation stage mainly compensates for the color features of the image based on humidity data and compensates for the texture features of the image based on temperature data.

[0038] For example, when the humidity is high, the image contrast is automatically enhanced, thereby effectively reducing the detection error caused by environmental factors such as light, temperature, humidity and dust, making the detection results more reliable; At high temperatures, the limestone surface may thermally expand and generate microcracks, while at low temperatures, the dark current noise of the sensor may be significantly enhanced, resulting in an abnormally high background grayscale value in the image. Therefore, temperature gradient compensation can effectively restore the true texture characteristics of the ore.

[0039] In one possible implementation, step S20 includes step B10: Step B10: If the ambient humidity exceeds the first humidity threshold, the limestone image to be identified is processed by histogram equalization using the CLAHE algorithm to obtain the processed limestone image to be identified.

[0040] It should be noted that CLAHE (Contrast Limited Adaptive Histogram Equalization) is an image enhancement algorithm. In this embodiment, the decision to enable the CLAHE algorithm to process the limestone image to be identified for humidity compensation is based on the current ambient humidity.

[0041] For example, when the relative humidity of the environment exceeds 60% (the first humidity threshold), the CLAHE algorithm is enabled to perform humidity compensation on the limestone image to be identified. It is understood that when the humidity is high, water vapor is prone to condensation on the surface of the ore, which may reduce the image contrast. The CLAHE algorithm adaptively enhances the image texture contrast by performing histogram equalization processing on the local area of ​​the image, so as to improve the accuracy of subsequent feature extraction.

[0042] In another feasible real-time approach, step S20 further includes the following steps: Step C10: Determine the gray-level co-occurrence matrix of the limestone image to be identified; Step C20: If the ambient temperature exceeds the first temperature threshold, then the energy value of the gray-level co-occurrence matrix is ​​compensated; the degree of energy compensation is positively correlated with the ambient temperature. Step C30: If the ambient temperature is lower than the second temperature threshold, then the entropy value of the gray-level co-occurrence matrix is ​​compensated; the degree of entropy compensation is positively correlated with the ambient temperature.

[0043] It should be noted that the Gray Level Co-occurrence Matrix (GLCM) is used to describe the spatial relationship between pixel gray values ​​in an image. It can reflect the texture features in the limestone image to be identified. Correcting / compensating the texture features according to the current ambient temperature can improve the accuracy of the subsequent recognition model.

[0044] For example, when the temperature exceeds 30℃ (first temperature threshold) or falls below 10℃ (second temperature threshold), the energy and entropy values ​​of the gray-level co-occurrence matrix are compensated according to the temperature gradient. Specifically, when the temperature exceeds 30℃ (first temperature threshold), the energy value is increased by 0.015 for every 1℃ increase, and when the temperature falls below 10℃, the entropy value is decreased by 0.02 for every 1℃ decrease. This means that when the temperature exceeds the first preset threshold, the degree of energy compensation is positively correlated with the ambient temperature, and when the temperature falls below the second preset threshold, the degree of entropy compensation is positively correlated with the ambient temperature. This method effectively restores the true texture features of the ore, ensuring that the accuracy of high-calcium limestone identification remains high even under extreme temperatures.

[0045] Step S30: Perform recognition processing on the limestone compensation image to be recognized to obtain the recognition result.

[0046] It should be noted that recognizing the limestone in the compensated image, compared to directly recognizing the limestone image, can improve the accuracy of the recognition results. The recognition result is the limestone calcium content information, which can include the probability of it being high-calcium limestone. If the probability of the limestone having a high calcium content is very high, it means that the limestone to be identified is high-calcium limestone.

[0047] In one feasible implementation, step S30 includes steps S31 to S32: Step S31: Extract features from the limestone image to be identified after temperature and humidity compensation processing to obtain feature information of the limestone image to be identified.

[0048] The feature information includes color features and texture features.

[0049] Understandably, images that have undergone temperature and humidity compensation have, to some extent, eliminated interference caused by environmental changes and possess a stable feature base.

[0050] In one feasible implementation, the color feature information includes a first color feature and a second color feature. The first color feature includes the hue information H and the lightness information V of the limestone image to be identified in the HSV color space. The second color feature includes the red channel intensity value R and the green channel intensity value G of the limestone image to be identified in the RGB color space.

[0051] Texture feature information includes the energy value and entropy value of the gray-level co-occurrence matrix of the limestone image to be identified.

[0052] Extensive experiments have revealed that high-calcium limestone possesses unique color characteristics compared to ordinary limestone. Specifically, these characteristics are manifested in the hue information (H) and lightness information (V) in the HSV color space, and the red channel intensity value (R) and green channel intensity value (G) in the RGB color space. Furthermore, it also exhibits unique texture features, which can be distinguished by the energy and entropy values ​​of the gray-level co-occurrence matrix of the image.

[0053] Specifically, under normal temperature and humidity conditions, the color characteristics of high-calcium limestone images are as follows: In the HSV color space, the hue information (H value) of high-calcium limestone images is concentrated between 0 and 30, and the lightness information (V) is greater than 200. Its R / G ratio in the RGB color model also exhibits certain characteristics. The texture characteristics are as follows: The energy value calculated using the gray-level co-occurrence matrix of high-calcium limestone images is typically greater than 0.6, and the entropy is less than 2.0. Based on these color and texture characteristics, high-calcium limestone can be identified.

[0054] Furthermore, considering the influence of changes in ambient temperature and humidity, after step S31, steps D10 to D20 are also included: Step D10: If the ambient humidity exceeds the second humidity threshold, then the brightness information V is processed by gain according to the preset gain ratio. Step D20: If the ambient temperature exceeds the third temperature threshold, adjust the intensity value R of the red channel based on the preset attenuation coefficient.

[0055] Specifically, considering the influence of ambient temperature and humidity, the extracted color features need to be appropriately modified. For example, if the ambient humidity exceeds the second humidity threshold (70%), the brightness information V is processed with a gain ratio of 15%. When the ambient temperature exceeds the third temperature threshold (30℃), the intensity value R of the red channel is adjusted according to the preset attenuation coefficient (0.9).

[0056] Step S32: Input the feature information and environmental temperature and humidity information into the high-calcium limestone identification model to obtain the identification results; the identification results include limestone calcium content information.

[0057] It should be noted that the high-calcium limestone identification model is trained through transfer learning based on a lightweight deep learning model, which can be the MobileNetV3 model. Specifically, a limestone image can be selected, and temperature and humidity compensation and feature extraction and correction can be performed based on the ambient temperature and humidity corresponding to the ring image. The limestone image is then cropped to obtain a 256×256 pixel ROI (Region of Interest) image as a training sample, which includes the image's feature information and the ambient temperature and humidity corresponding to the image. The model output is the limestone calcium content information, which may include the probability of high-calcium limestone.

[0058] Specifically, in image recognition, features can be extracted from the temperature-compensated grayscale image to be recognized, and the extracted feature data can be input into the high-calcium limestone recognition model to obtain the recognition result.

[0059] Understandably, training with a lightweight deep learning model can enable edge computing, thereby improving the real-time performance and efficiency of high-calcium limestone identification.

[0060] Furthermore, to improve detection efficiency, a preliminary screening can be performed before inputting the high-calcium limestone recognition model for detection. Specifically, after extensive experimental verification, under normal temperature and humidity conditions, in the HSV color space, the hue information H value of high-calcium limestone images is concentrated between 0-30, the brightness information V is greater than 200, the gray-level co-occurrence matrix energy value of high-calcium limestone images is greater than 0.6, and the entropy value is less than 2.0. Therefore, a brightness threshold and an energy threshold can be set, and high-calcium limestone images with a brightness information V value greater than the brightness threshold of 200 and an energy value greater than 0.6 can be used as input values ​​for the high-calcium limestone recognition model. However, this judgment condition may change due to the influence of ambient temperature and humidity. For example, at low temperatures, the brightness information V and energy value of high-calcium limestone images will decrease. For example, if the ambient temperature is below 10℃, the brightness threshold is set to 180, and the energy threshold is lowered by 0.05 to 0.55.

[0061] Therefore, before step S32, steps E10~E20 are also included: Step E10: Based on the environmental temperature and humidity information corresponding to the limestone image to be identified, determine the energy threshold and brightness threshold.

[0062] Step E20: Determine whether the brightness information V and energy value of the limestone image to be identified exceed the brightness threshold and energy threshold, respectively, and obtain preliminary identification results.

[0063] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the high-calcium limestone identification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0064] This application also provides a high-calcium limestone identification device, please refer to... Figure 2 The high-calcium limestone identification device includes: The acquisition module 10 is used to acquire the data to be identified, which includes the image of the limestone to be identified and the environmental temperature and humidity information. The compensation module 20 is used to perform temperature and humidity compensation processing on the limestone image to be identified based on the ambient temperature and humidity information to obtain the limestone compensated image to be identified; wherein, the temperature and humidity compensation processing includes compensating the color features of the limestone image to be identified based on the ambient humidity information and compensating the texture features of the limestone image to be identified based on the ambient temperature information. The recognition module 30 is used to perform recognition processing on the limestone compensation image to be recognized and obtain the recognition result.

[0065] The high-calcium limestone identification device provided in this application, employing the high-calcium limestone identification method described in the above embodiments, can solve the technical problems of large identification result deviation and low efficiency in related technologies for high-calcium limestone identification. Compared with related technologies, the beneficial effects of the high-calcium limestone identification device provided in this application are the same as those of the high-calcium limestone identification method provided in the above embodiments, and other technical features in the high-calcium limestone identification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0066] This application also provides a high-calcium limestone identification system, referring to... Figure 3 , Figure 3This is a schematic diagram of a high-calcium limestone identification system. The system includes a data acquisition module, a high-calcium limestone identification device, a sorting device, and a dedicated silo. The data acquisition module includes a temperature and humidity sensor and an industrial camera. The high-calcium limestone identification device can be an edge computing terminal, and the sorting device can be an actuation spray valve. Specifically, the industrial camera is responsible for real-time image acquisition of the ore on the conveyor belt. Leveraging high resolution, global shutter technology, high frame rate, HDR functionality, and high dynamic range, it can clearly capture the texture, color, and luster of the ore, providing basic image data for subsequent analysis and judgment. Simultaneously, the industrial camera is equipped with a ring-shaped LED white light source and a polarizing filter. The former provides stable and uniform illumination for the industrial camera, accurately reproducing the color of the ore and avoiding misjudgments of ore color characteristics due to lighting color deviations, ensuring the accuracy and reliability of the color information in the images acquired by the industrial camera. The latter, installed in front of the industrial camera, primarily eliminates reflections from the ore surface. Reflections from the ore surface can interfere with image acquisition, causing bright spots or reflective areas in the image, affecting the identification of ore features. Polarizing filters effectively reduce reflections and improve image clarity and quality by filtering light from specific directions. Temperature and humidity sensors monitor the temperature and humidity of the environment surrounding the ore in real time. Changes in temperature and humidity affect the surface condition of the ore and image acquisition. For example, high humidity may cause condensation on the ore surface, altering its reflective properties and image contrast. Temperature changes affect color distortion in the acquired image and may also affect the physical properties of the ore. The temperature and humidity sensors transmit the collected data to an edge computing terminal for subsequent image data processing and environmental compensation. The edge computing terminal receives image data from industrial cameras and environmental data from the temperature and humidity sensors. Utilizing its computing power, it runs algorithms such as image preprocessing, feature extraction, and classification models to analyze and process the data. Based on the processing results, it makes decisions to determine whether the ore is high-calcium limestone and controls subsequent sorting actions. Pneumatic spray valves receive control signals from the edge computing terminal. When the edge computing terminal determines that the ore is high-calcium limestone, it sends a signal to the pneumatic spray valve. The pneumatic spray valve then performs the corresponding action based on the signal, separating the high-calcium limestone from the conveyor belt and guiding it into a dedicated silo. The dedicated silo is used to store the sorted limestone in a categorized manner. This ensures that the high-calcium limestone can be accurately identified and used in subsequent production processes, avoiding mixing with other low-calcium ores with higher impurity content, and guaranteeing product quality.

[0067] For example, the specific identification process can be as follows: an industrial camera captures images of the ore according to the belt conveyor speed intervals, while a temperature and humidity sensor simultaneously reads ambient temperature and humidity data. After receiving the data, the computing terminal first performs adaptive denoising processing on the image and then performs compensation operations based on the humidity data. Next, the color and texture features of the image are extracted and input into a deep learning model, which outputs the calcium content level. If the model determines that it is high-calcium limestone and the probability of high-calcium limestone exceeds 90%, and two consecutive sampling results are the same, then a pneumatic spray valve is triggered to sort it into a dedicated silo, completing the online detection and sorting workflow for high-calcium limestone.

[0068] This application provides a high-calcium limestone identification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the high-calcium limestone identification method described above.

[0069] The following is for reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing a high-calcium limestone identification device according to embodiments of this application. The high-calcium limestone identification device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. The illustrated high-calcium limestone identification device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0070] like Figure 4As shown, the high-calcium limestone identification device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for device operation. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the high-calcium limestone identification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show high-calcium limestone identification devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0071] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0072] The high-calcium limestone identification device provided in this application, employing the high-calcium limestone identification method described in the above embodiments, can solve the technical problems of large identification result deviation and low efficiency in related technologies for high-calcium limestone identification. Compared with related technologies, the beneficial effects of the high-calcium limestone identification device provided in this application are the same as those of the high-calcium limestone identification method provided in the above embodiments, and other technical features in this high-calcium limestone identification device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0073] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0074] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0075] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the high-calcium limestone identification method described in the above embodiments.

[0076] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0077] The aforementioned computer-readable storage medium may be included in the high-calcium limestone identification device; or it may exist independently and not assembled into the high-calcium limestone identification device.

[0078] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the high-calcium limestone identification device, the high-calcium limestone identification device: acquires the target user's password requirement information; selects a standard high-calcium limestone identification scheme consistent with the password requirement information from a preset cryptography library; the standard high-calcium limestone identification scheme includes standard cryptographic algorithms, standard cryptographic protocols, and standard cryptographic schemes; and outputs the standard high-calcium limestone identification scheme.

[0079] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0080] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0081] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0082] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described high-calcium limestone identification method. This solves the technical problems of large identification result deviations and low efficiency in related technologies for high-calcium limestone identification. Compared with related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the high-calcium limestone identification method provided in the above embodiments, and will not be elaborated upon here.

[0083] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the high-calcium limestone identification method described above.

[0084] The computer program product provided in this application can solve the technical problems of large identification result deviation and low efficiency in the identification of high-calcium limestone in related technologies. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as those of the high-calcium limestone identification method provided in the above embodiments, and will not be repeated here.

[0085] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for identifying high-calcium limestone, characterized in that, The method includes: Acquire the data to be identified, which includes an image of limestone to be identified and environmental temperature and humidity information; Based on the environmental temperature and humidity information, the image of the limestone to be identified is subjected to temperature and humidity compensation processing to obtain a compensated image of the limestone to be identified. The image of the limestone to be identified is processed to obtain the identification result; The temperature and humidity compensation process includes compensating for the color features of the limestone image to be identified based on environmental humidity information, and compensating for the texture features of the limestone image to be identified based on environmental temperature information.

2. The method as described in claim 1, characterized in that, The limestone to be identified is transported via a conveyor belt, and the step of acquiring the data to be identified includes: During the conveying process of the limestone to be identified, the conveying speed of the conveyor belt and the image of the limestone to be identified are acquired; Based on the transmission speed, the filter kernel size of the image filter is adjusted; the filter kernel size is positively correlated with the transmission speed. The image of limestone to be identified is filtered based on the image filter. Obtain the filtered image of the limestone to be identified.

3. The method as described in claim 1, characterized in that, The step of compensating for the color features of the limestone image to be identified based on environmental humidity information includes: If the ambient humidity exceeds the first humidity threshold, the limestone image to be identified is subjected to histogram equalization processing using the CLAHE algorithm to obtain the processed limestone image to be identified.

4. The method as described in claim 3, characterized in that, The step of compensating the texture features of the limestone image to be identified based on ambient temperature information includes: Determine the gray-level co-occurrence matrix of the limestone image to be identified; If the ambient temperature exceeds the first temperature threshold, the energy value of the gray-level co-occurrence matrix is ​​compensated; the degree of energy compensation is positively correlated with the ambient temperature. If the ambient temperature is lower than the second temperature threshold, then the entropy value of the gray-level co-occurrence matrix is ​​compensated; the degree of entropy compensation is positively correlated with the ambient temperature.

5. The method as described in claim 1, characterized in that, The step of performing recognition processing on the limestone compensation image to be identified and obtaining the recognition result includes: Feature extraction is performed on the limestone image to be identified after temperature and humidity compensation processing to obtain the feature information of the limestone image to be identified. The feature information and the environmental temperature and humidity information are input into the high-calcium limestone identification model to obtain the identification result; the identification result includes limestone calcium content information. The feature information includes color features and texture features. The color features include a first color feature and a second color feature. The first color feature includes the hue information H and brightness information V of the limestone image to be identified in the HSV color space. The second color feature includes the red channel intensity value R and the green channel intensity value G of the limestone image to be identified in the RGB color space. The texture features include the energy value and entropy value of the gray-level co-occurrence matrix of the limestone image to be identified.

6. The method as described in claim 5, characterized in that, After the step of extracting features from the temperature and humidity compensated image of the limestone to be identified to obtain the feature information of the image, the method further includes the following step: If the ambient humidity exceeds the second humidity threshold, the brightness information V is processed by gain according to the preset gain ratio. If the ambient temperature exceeds the third temperature threshold, the intensity value R of the red channel is adjusted based on the preset attenuation coefficient.

7. A high-calcium limestone identification device, characterized in that, The device comprises: The acquisition module is used to acquire the data to be identified, which includes an image of limestone to be identified and environmental temperature and humidity information; The compensation module is used to perform temperature and humidity compensation processing on the limestone image to be identified based on the ambient temperature and humidity information to obtain a compensated limestone image to be identified; wherein, the temperature and humidity compensation processing includes compensating the color features of the limestone image to be identified based on the ambient humidity information and compensating the texture features of the limestone image to be identified based on the ambient temperature information. The recognition module is used to perform recognition processing on the limestone compensation image to be recognized and obtain the recognition result.

8. A high-calcium limestone identification device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the high-calcium limestone identification method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the high-calcium limestone identification method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the high-calcium limestone identification method as described in any one of claims 1 to 6.

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