Special gas safety control method and system for production of silicon-carbon negative electrode material

Abstract

The invention provides a special gas safety control method and system for production of a silicon-carbon negative electrode material, and relates to the technical field of battery production, the method comprises the following steps: obtaining audio data during operation of a target transmission pipeline, the target transmission pipeline being a pipeline for transmitting special gas for production of the silicon-carbon negative electrode material; audio analysis is conducted on the audio data according to a preset first network model, an audio analysis result is obtained, and the first network model is used for detecting the probability of gas leakage of a pipeline corresponding to the audio data; and when the audio analysis result indicates that the probability of gas leakage of the target transmission pipeline is greater than or equal to a set probability threshold, outputting a gas leakage alarm. According to the invention, the mode of audio detection by using the network model is used to replace the mode of air pressure detection used in the related technology, so that a more accurate air leakage detection result can be obtained.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery production, and in particular to a special gas safety control method and system for the production of silicon-carbon negative electrode materials. Background Art

[0002] In the production process of silicon-carbon negative electrode materials, the use of special gases (such as silane gas, acetylene gas, etc.) is playing an important role, leading to revolutionary breakthroughs in the negative electrode materials industry.

[0003] The above-mentioned special gases are highly hazardous. Special attention should be paid to safety when they are transported from the gas source to the equipment use end and then discharged into the atmosphere. In related technologies, the gas pressure in the special gas transmission pipeline is often detected to identify whether the special gas is leaking.

[0004] It was found in the application that, on the one hand, the accuracy of the air pressure sensor is limited and it is unable to timely identify the air pressure changes caused by tiny cracks. On the other hand, in actual production, the flow rate in the special gas transmission pipeline will be dynamically adjusted to meet the production needs of silicon-carbon negative electrode materials at different stages. That is, the air pressure in the special gas transmission pipeline itself is constantly changing, which also increases the difficulty of air pressure detection.

[0005] That is to say, the accuracy of special gas leakage detection results obtained based on relevant technologies is low. Summary of the invention

[0006] The purpose of the present disclosure is to provide a special gas safety control method and system for the production of silicon-carbon negative electrode materials, which is used to solve the technical problem of low accuracy of special gas leakage detection results obtained by related technologies.

[0007] In a first aspect, an embodiment of the present application provides a special gas safety control method for the production of silicon-carbon negative electrode materials, the method comprising: Acquire audio data when a target transmission pipeline is in operation, wherein the target transmission pipeline is a pipeline used to transmit special gases for the production of silicon-carbon negative electrode materials; Performing audio analysis on the audio data according to a preset first network model to obtain an audio analysis result, wherein the first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data; When the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold, a gas leakage alarm is output.

[0008] In a second aspect, an embodiment of the present application provides a special gas safety control system for the production of silicon-carbon negative electrode materials, the system comprising: An acquisition module, used to acquire audio data when a target transmission pipeline is in operation, wherein the target transmission pipeline is a pipeline used to transmit special gases for the production of silicon-carbon negative electrode materials; an analysis module, configured to perform audio analysis on the audio data according to a preset first network model to obtain an audio analysis result, wherein the first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data; The gas leakage alarm module is used to output a gas leakage alarm when the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold.

[0009] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0010] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0011] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0012] In the present application, the audio data of the transmission pipeline during operation is analyzed through a pre-set first network model to obtain an audio analysis result indicating the probability of gas leakage in the transmission pipeline, and when the probability indicated by the audio analysis result is greater than or equal to the set probability threshold, a gas leakage alarm is output, thereby replacing the air pressure detection method used in the related art with the audio detection method using the network model to obtain more accurate detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of a special gas safety control method for silicon-carbon negative electrode material production provided by an embodiment of the present disclosure; Figure 2 It is a structural schematic diagram of a special gas safety control system for silicon-carbon negative electrode material production provided by an embodiment of the present disclosure; Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] See also Figure 1 , Figure 1 It is a flow chart of a special gas safety control method for the production of silicon-carbon negative electrode materials provided in an embodiment of the present application.

[0016] like Figure 1 As shown, the special gas safety control method for the production of silicon-carbon negative electrode materials includes: Step 101: Acquire audio data when the target transmission pipeline is running.

[0017] Wherein, the target transmission pipeline is a pipeline used to transmit special gases for the production of silicon-carbon negative electrode materials.

[0018] Exemplarily, the special gas includes but is not limited to silane gas, acetylene gas, nitrogen gas, etc.

[0019] In applications, audio data of the target transmission pipeline during operation can be continuously collected through an audio collection device (such as a microphone), and the audio collection device and the target transmission pipeline can be set independently of each other to avoid interference of pipeline vibration on the audio collection device. For example, the audio collection device is set directly above the workshop where the target transmission pipeline is located, or a support frame is set along the extension direction of the target transmission pipeline, the support frame and the target transmission pipeline do not contact each other, and the audio collection device is arranged on the support frame.

[0020] Step 102: Perform audio analysis on the audio data according to a preset first network model to obtain an audio analysis result.

[0021] The first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data.

[0022] Exemplarily, the first network model may be a neural network module, such as a CNN model, a FNN model, a RNN model, etc.

[0023] Specifically, the process of acquiring the first network model may be: Acquire an initial network model and a first data set for training the initial network model, wherein the first data set includes audio training data as positive samples and audio training data as negative samples, the audio training data as positive samples are audio training data corresponding to gas leakage situations, and the audio training data as negative samples are audio data corresponding to non-gas leakage situations; Iteratively training the initial network model according to the first data set, and when an iteration termination condition is met, generating the first network model according to a feature processing module included in the network model of the last iteration, and the initial network model is used as the network model of the first iteration; The iteration termination condition is as follows: the number of iterations is greater than or equal to a set number threshold, and / or the difference between the predicted value output by the network model of the current iteration and the true value corresponding to the batch of audio training data is less than a set difference threshold.

[0024] Specifically, the audio data is subjected to audio analysis according to the preset first network model, and the audio analysis result obtained may be: Extract features from the audio data according to the feature processing module included in the first network model (that is, the feature processing module included in the network model of the last iteration) to obtain audio features; and, according to the feature processing module, performing feature similarity comparison on the audio feature and a reference feature of the corresponding air leakage audio to obtain an audio feature similarity value; The audio feature similarity value is output as an audio analysis result.

[0025] Step 103: When the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold, output a gas leakage alarm.

[0026] In the application, the aforementioned probability threshold can be adaptively selected according to actual needs, and the value interval of the probability threshold is [0.5, 1). This application does not limit the specific value of the probability threshold.

[0027] In the present application, the audio data of the transmission pipeline during operation is analyzed through a pre-set first network model to obtain an audio analysis result indicating the probability of gas leakage in the transmission pipeline, and when the probability indicated by the audio analysis result is greater than or equal to the set probability threshold, a gas leakage alarm is output, thereby replacing the air pressure detection method used in the related art with the audio detection method using the network model to obtain more accurate detection results.

[0028] In one embodiment, the audio data is data collected by an audio collection device, there are multiple audio collection devices, and the multiple audio collection devices are arranged in an array along the extension direction of the target transmission pipeline; The performing audio analysis on the audio data according to the preset first network model to obtain an audio analysis result includes: Performing audio analysis on the plurality of audio data respectively according to the first network model to obtain a plurality of audio analysis results, wherein the plurality of audio data corresponds to the plurality of audio acquisition devices one by one; After outputting the gas leakage alarm, the method further comprises: Among at least one audio analysis result indicating that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold, determine the audio acquisition device corresponding to the audio analysis result indicating the maximum probability of gas leakage in the target transmission pipeline as the target device; According to the audio collection range of the target device, a leaking pipe section is determined in the target transmission pipeline.

[0029] In this embodiment, audio data of different pipe sections of the target transmission pipeline during operation are respectively collected by multiple audio collection devices arranged in an array, and the collected audio data are respectively sent to the first network model for audio analysis. When a gas leakage risk is detected (i.e., there is at least one audio analysis result with a probability greater than or equal to a set probability threshold), the audio analysis result with the largest indicating probability is selected from at least one audio analysis result indicating that the probability of gas leakage in the target transmission pipeline is greater than or equal to the set probability threshold, and the pipe section corresponding to the audio analysis result is determined as the leaking pipe section.

[0030] Among them, the audio collection range of the audio collection device can be understood as a conical area with the installation point of the audio collection device as the vertex and the audio perception distance set as the radius. Therefore, the aforementioned leaking pipe section can be understood as: a part of the pipe section in the target transmission pipeline that is located within the audio collection range of the target device.

[0031] In one embodiment, after determining the target device, the projection point of the target device on the ground can also be used as a reference point, and an acoustic camera can be used with the reference point as the center to obtain multiple groups of mapping data pairs, wherein the multiple groups of mapping data pairs are used to represent the mapping relationship between the spatial coordinates around the reference point and the audio data, and according to the audio data indicated by the audio analysis result corresponding to the target device, from the multiple groups of mapping data, the mapping data including the audio data and the audio data indicated by the audio analysis result whose feature similarity is greater than a set similarity threshold is used to determine the target mapping data, and then the pipe section indicated by the spatial coordinates corresponding to the target mapping data is determined as the leaking pipe section, which can achieve accurate positioning of the leaking pipe section, that is, by providing a pipe section to be detected with a more precise position and a smaller range, the workload of subsequent detection of specific crack areas is reduced, thereby improving the efficiency of surveying crack areas in pipelines.

[0032] In the application, it is preferred to arrange for an intelligent robot equipped with an acoustic camera to automatically go to the location of the leaking pipe section to collect multiple sets of mapping data pairs, so as to reduce the time that natural persons stay in the dangerous area where the leaking pipe section is located. After the leaking pipe section is determined, a natural person wearing protective equipment will go to the area where the leaking pipe section is located to conduct crack survey and crack remediation measures for the leaking pipe section.

[0033] In one embodiment, after determining the leaking pipe section in the target transmission pipeline according to the audio collection range of the target device, the method further includes: Acquire a high-definition pipe segment image of the leaking pipe segment using a high-definition image acquisition device; Crack detection is performed on the high-definition pipe segment image to obtain a crack detection result.

[0034] In one example, crack detection is performed on the high-definition pipe segment image to obtain a crack detection result, including: Performing grayscale conversion on the high-definition pipe segment image to obtain a high-definition grayscale image; Performing adaptive local thresholding processing on the high-definition grayscale image to obtain a thresholded image, which can suppress the influence of illumination on the image threshold; The thresholded image is then dilated, and the dilated image is smoothed by median filtering, and finally the smoothed image is eroded to obtain a crack detection result indicating the location of the cracks in the image.

[0035] In this embodiment, a high-definition pipe segment image of the leaking pipe segment is collected, and crack detection is performed on the collected high-definition pipe segment image by image detection, thereby obtaining a crack detection result for indicating a risk area suspected of leaking in the leaking pipe segment.

[0036] Compared with the manual crack detection method, identifying cracks in leaking pipe sections through image detection can obtain crack detection results more quickly and accurately, thereby shortening the time spent on locating crack positions, improving the efficiency of handling leaking pipe sections, and reducing the harm caused by leaking pipe sections.

[0037] In the application, after the gas leakage alarm is sounded, maintenance personnel wearing protective equipment can go to the location of the leaking pipe section with high-definition image acquisition equipment to collect high-definition images of the pipe section, or an intelligent robot equipped with high-definition image acquisition equipment can automatically go to the location of the leaking pipe section to collect high-definition images of the pipe section, so as to reduce the length of time that natural persons stay in the dangerous area where the leaking pipe section is located. After the crack area is determined, natural persons wearing protective equipment will go to the dangerous area to carry out emergency disposal of the leaking pipe section.

[0038] In one embodiment, after performing crack detection on the high-definition pipe segment image and obtaining the crack detection result, the method further includes: The crack area marked by the crack detection result in the high-definition pipe segment image is fused with the high-definition pipe segment image to obtain a crack identification image, wherein the crack area is highlighted in the crack identification image.

[0039] In this embodiment, the crack area marked by the crack detection result in the high-definition pipe segment image is highlighted in the high-definition pipe segment image, so that emergency response personnel can quickly locate the crack area through the crack identification image, thereby speeding up emergency sealing of the crack area.

[0040] In one embodiment, the step of obtaining audio data when the target transmission pipeline is running includes: Acquire multiple original sound data when the target transmission pipeline is running according to the audio acquisition device; The plurality of original sound data are sampled to obtain a plurality of sound data to be analyzed, wherein the number of the sound data to be analyzed is less than the number of the original sound data, and the plurality of sound data to be analyzed include the audio data.

[0041] In this embodiment, sampling is performed to reduce the amount of audio data input to the first network model, so as to reduce the energy consumption of the equipment caused by data transmission and model processing while ensuring the reliability of the air leakage detection result.

[0042] Illustratively, the sampling frequency of the above sampling process (such as extracting one second of original sound data as a sound data to be analyzed every 5 seconds) can be adaptively set according to actual needs, and this application does not limit the specific value of the sampling frequency.

[0043] In one embodiment, the method further comprises: Acquiring exhaust sound data when the exhaust pipe section is in operation according to an audio sensing device disposed on the inner wall of the exhaust pipe section in the target transmission pipeline; Performing particle detection on the exhaust sound data according to a preset second network model to obtain a particle detection result, wherein the second network model is used to detect the particle content of the gas flowing in the pipe section; When the particulate matter detection result indicates that the particulate matter content of the gas flowing in the exhaust emission pipe section is greater than or equal to a set content threshold, an exhaust gas risk alarm is output.

[0044] In this embodiment, an audio sensing device arranged on the inner wall of the exhaust emission pipe section in the target transmission pipeline is used to collect exhaust sound data when the exhaust emission pipe section is in operation, and a second network model that can detect the particulate matter content in the gas based on the sound data is used to analyze the exhaust sound data to quickly determine whether the particulate matter content of the gas circulating in the exhaust emission pipe section exceeds the standard (i.e., greater than or equal to the set content threshold), and output an exhaust risk alarm if it exceeds the standard, prompting the user to dispose of the exhaust gas with excessively high particulate matter content in a timely manner (such as sending the exhaust gas with excessively high particulate matter content back to the exhaust treatment device for harmless treatment).

[0045] The present application also provides a special gas safety control system for the production of silicon-carbon negative electrode materials, such as Figure 2 As shown, the special gas safety control system 200 for the production of silicon-carbon negative electrode materials includes: An acquisition module 201 is used to acquire audio data when a target transmission pipeline is in operation, wherein the target transmission pipeline is a pipeline for transmitting special gases for the production of silicon-carbon negative electrode materials; An analysis module 202 is used to perform audio analysis on the audio data according to a preset first network model to obtain an audio analysis result, wherein the first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data; The gas leakage alarm module 203 is used to output a gas leakage alarm when the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold.

[0046] In one embodiment, the audio data is data collected by an audio collection device, there are multiple audio collection devices, and the multiple audio collection devices are arranged in an array along the extension direction of the target transmission pipeline; The analysis module 202 includes: an audio analysis unit, configured to perform audio analysis on the plurality of audio data respectively according to the first network model to obtain a plurality of audio analysis results, wherein the plurality of audio data corresponds to the plurality of audio acquisition devices in a one-to-one correspondence; The special gas safety control system 200 for the production of silicon-carbon negative electrode materials also includes: a target device determination module, configured to determine, as the target device, an audio acquisition device corresponding to an audio analysis result indicating that the probability of gas leakage in the target transmission pipeline is the highest among at least one audio analysis result indicating that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold; The leaking pipe section determination module is used to determine the leaking pipe section in the target transmission pipeline according to the audio collection range of the target device.

[0047] In one embodiment, the special gas safety control system 200 for silicon-carbon negative electrode material production further includes: An image acquisition module, used for acquiring a high-definition pipe segment image of the leaking pipe segment using a high-definition image acquisition device; The crack detection module is used to perform crack detection on the high-definition pipe segment image to obtain a crack detection result.

[0048] In one embodiment, the special gas safety control system 200 for silicon-carbon negative electrode material production further includes: The image fusion module is used to fuse the crack area marked by the crack detection result in the high-definition pipe segment image with the high-definition pipe segment image to obtain a crack identification image, wherein the crack area is highlighted in the crack identification image.

[0049] In one embodiment, the acquisition module 201 is specifically used for: Acquire multiple original sound data when the target transmission pipeline is running according to the audio acquisition device; The plurality of original sound data are sampled to obtain a plurality of sound data to be analyzed, wherein the number of the sound data to be analyzed is less than the number of the original sound data, and the plurality of sound data to be analyzed include the audio data.

[0050] In one embodiment, the special gas safety control system 200 for silicon-carbon negative electrode material production further includes an exhaust gas detection module, which is used to: Acquiring exhaust sound data when the exhaust pipe section is in operation according to an audio sensing device disposed on the inner wall of the exhaust pipe section in the target transmission pipeline; Performing particle detection on the exhaust sound data according to a preset second network model to obtain a particle detection result, wherein the second network model is used to detect the particle content of the gas flowing in the pipe section; When the particulate matter detection result indicates that the particulate matter content of the gas flowing in the exhaust emission pipe section is greater than or equal to a set content threshold, an exhaust gas risk alarm is output.

[0051] A special gas safety control system 200 for the production of silicon-carbon negative electrode materials provided in an embodiment of the present application can implement each process in the above-mentioned special gas safety control method embodiment for the production of silicon-carbon negative electrode materials. To avoid repetition, they will not be described here.

[0052] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0053] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0054] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0055] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as a special gas safety control method for the production of silicon-carbon negative electrode materials. For example, in some embodiments, the special gas safety control method for the production of silicon-carbon negative electrode materials may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the special gas safety control method for silicon-carbon negative electrode material production described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the special gas safety control method for silicon-carbon negative electrode material production in any other appropriate manner (e.g., by means of firmware).

[0056] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0057] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0058] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0059] As used herein, the term "machine-readable medium" refers to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0060] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0061] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0062] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0063] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0064] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0065] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A special gas safety control method for the production of silicon-carbon negative electrode materials, characterized in that: The method comprises: Acquire audio data when a target transmission pipeline is in operation, wherein the target transmission pipeline is a pipeline used to transmit special gases for the production of silicon-carbon negative electrode materials; Performing audio analysis on the audio data according to a preset first network model to obtain an audio analysis result, wherein the first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data; When the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold, a gas leakage alarm is output.

2. The method according to claim 1, characterized in that The audio data is data collected based on an audio collection device, there are multiple audio collection devices, and the multiple audio collection devices are arranged in an array along the extension direction of the target transmission pipeline; The performing audio analysis on the audio data according to the preset first network model to obtain an audio analysis result includes: Performing audio analysis on the plurality of audio data respectively according to the first network model to obtain a plurality of audio analysis results, wherein the plurality of audio data corresponds to the plurality of audio acquisition devices one by one; After outputting the gas leakage alarm, the method further comprises: Among at least one audio analysis result indicating that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold, determine the audio acquisition device corresponding to the audio analysis result indicating the maximum probability of gas leakage in the target transmission pipeline as the target device; According to the audio collection range of the target device, a leaking pipe section is determined in the target transmission pipeline.

3. The method according to claim 2, characterized in that After determining the leaking pipe section in the target transmission pipeline according to the audio collection range of the target device, the method further includes: Acquire a high-definition pipe segment image of the leaking pipe segment using a high-definition image acquisition device; Crack detection is performed on the high-definition pipe segment image to obtain a crack detection result.

4. The method according to claim 3, characterized in that After performing crack detection on the high-definition pipe segment image and obtaining the crack detection result, the method further includes: The crack region marked by the crack detection result in the high-definition pipe segment image is fused with the high-definition pipe segment image to obtain a crack identification image, wherein the crack region is highlighted in the crack identification image.

5. The method according to claim 1, characterized in that The step of obtaining audio data when the target transmission pipeline is running includes: Acquire multiple original sound data when the target transmission pipeline is running according to the audio acquisition device; The plurality of original sound data are sampled to obtain a plurality of sound data to be analyzed, wherein the number of the sound data to be analyzed is less than the number of the original sound data, and the plurality of sound data to be analyzed include the audio data.

6. The method according to claim 1, characterized in that The method further comprises: Acquiring exhaust sound data when the exhaust pipe section is in operation according to an audio sensing device disposed on the inner wall of the exhaust pipe section in the target transmission pipeline; Performing particle detection on the exhaust sound data according to a preset second network model to obtain a particle detection result, wherein the second network model is used to detect the particle content of the gas flowing in the pipe section; When the particulate matter detection result indicates that the particulate matter content of the gas flowing in the exhaust emission pipe section is greater than or equal to a set content threshold, an exhaust gas risk alarm is output.

7. A special gas safety control system for the production of silicon-carbon negative electrode materials, characterized in that: The system comprises: An acquisition module, used to acquire audio data when a target transmission pipeline is in operation, wherein the target transmission pipeline is a pipeline used to transmit special gases for the production of silicon-carbon negative electrode materials; an analysis module, configured to perform audio analysis on the audio data according to a preset first network model to obtain an audio analysis result, wherein the first network model is used to detect the probability of gas leakage in the pipeline corresponding to the audio data; The gas leakage alarm module is used to output a gas leakage alarm when the audio analysis result indicates that the probability of gas leakage in the target transmission pipeline is greater than or equal to a set probability threshold.

8. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 6 when executed by the processor.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

Images