Coal conveying production voice system control platform

Abstract

The invention discloses a coal conveying production voice system control platform, which comprises a voice acquisition module, a voice recognition module, an instruction analysis module, a control logic module, an equipment execution module, a feedback module, a communication module and a safety protection module, noise reduction and enhancement processing are carried out; the voice recognition module is connected with the voice acquisition module, and the voice recognition module is used for converting an acquired voice instruction into a text instruction. When the scheme is implemented, the equipment is quickly controlled through the voice instruction, so that manual operation steps are reduced, the response speed is increased, and the operation efficiency is improved; by combining voiceprint recognition and authority management, misoperation or illegal instructions are prevented, so that the security of the system during implementation is improved; and by adopting noise reduction and voice enhancement technologies, the recognition accuracy in a high-noise environment is ensured, so that the system can adapt to various working conditions.

Description

Technical Field

[0001] The present application relates to the technical field of coal transportation production, and in particular to a coal transportation production voice system control platform. Background Art

[0002] The traditional coal handling process involves the coordinated operation of multiple devices to complete the coal transportation task. Currently, these devices are primarily controlled by manual buttons or computer interfaces. However, manual control methods have significant limitations: the system response is slow, making it difficult to make precise adjustments quickly under complex and changing operating conditions, thus affecting overall production efficiency. Furthermore, the harsh environment of coal handling operations, with high levels of noise and dust, poses significant challenges to operators. Prolonged exposure to such conditions can lead to fatigue, increasing the risk of operational errors and posing a potential threat to operational safety.

[0003] On the other hand, although computer interface control has improved the convenience of operation to a certain extent, the system lacks voice interaction function, which means that operators still face certain inconveniences when interacting with the equipment.

[0004] In order to solve the above problems, this application proposes an innovative coal transportation production voice system control platform. Summary of the Invention

[0005] To this end, the present application provides a coal transportation production voice system control platform to solve the problems of slow manual operation response and insufficient intelligence of computer interface control in the existing technology.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In the first aspect, a coal transportation production voice system control platform includes a voice acquisition module, a voice recognition module, a command parsing module, a control logic module, a device execution module, a feedback module, a communication module, and a safety protection module.

[0008] The voice acquisition module is used to collect the voice instructions of operators at the coal transportation production site in real time and perform noise reduction and enhancement processing;

[0009] The speech recognition module is connected to the speech acquisition module, and the speech recognition module is used to convert the collected voice instructions into text instructions and optimize the recognition accuracy based on the deep learning model;

[0010] The instruction parsing module is connected to the speech recognition module, and is used to perform semantic analysis on text instructions and extract key operation instructions;

[0011] The control logic module is connected to the instruction parsing module, and the control logic module is used to generate corresponding device control signals according to key operation instructions;

[0012] The equipment execution module is connected to the control logic module, and the equipment execution module is used to receive control signals and drive the coal transportation production equipment to perform corresponding actions;

[0013] The feedback module is used to collect equipment execution status information and provide feedback to the operator through voice or visual interface;

[0014] The communication module is used to realize data interaction between modules and support remote monitoring and data storage;

[0015] The security protection module is used to verify the authority of voice commands and filter out unauthorized operations.

[0016] Preferably, the voice acquisition module includes a microphone, a noise reduction unit and a voice enhancement unit;

[0017] The microphone is used to collect the operator's voice in a targeted manner;

[0018] The noise reduction unit is used to eliminate background noise interference in the voice collected by the microphone;

[0019] The speech enhancement unit is used to improve the signal-to-noise ratio of the operator's speech signal.

[0020] Preferably, the speech recognition module adopts an end-to-end deep learning model, which includes an acoustic model, a language model and an adaptive training unit;

[0021] Acoustic models are used to extract speech features;

[0022] Language models are used to optimize semantic understanding;

[0023] The adaptive training unit is used to optimize the recognition accuracy based on coal handling production specific terminology.

[0024] Preferably, the instruction parsing module includes a natural language processing unit, a keyword extraction algorithm and a context association unit:

[0025] The natural language processing unit is used to analyze the semantics of instructions;

[0026] Keyword extraction algorithms are used to identify key operating instructions;

[0027] The context association unit is used to optimize instruction parsing in combination with the production environment.

[0028] Preferably, the control logic module includes a rule engine, a priority scheduling unit and an exception handling unit;

[0029] The rule engine is used to match instructions with device control logic;

[0030] The priority scheduling unit is used to handle multiple instructions concurrently;

[0031] The exception handling unit is used to detect and handle illegal instructions.

[0032] Preferably, the equipment execution module includes a PLC controller, an execution status monitoring unit and a fault warning unit;

[0033] PLC controller is used to drive coal conveyor belt, crusher, coal feeder and other equipment;

[0034] The execution status monitoring unit is used to provide real-time feedback on the equipment operation status;

[0035] The fault warning unit is used to issue an alarm when the equipment is abnormal.

[0036] Preferably, the feedback module includes a speech synthesis unit, a visual interface and an alarm unit;

[0037] The speech synthesis unit is used to convert the execution results into speech broadcast;

[0038] The visual interface is used to display the operating status of the equipment;

[0039] The alarm unit is used to issue audible and visual alarms in abnormal situations.

[0040] Preferably, the security protection module includes a voice recognition unit, a command authority management unit and an operation log recording unit;

[0041] Voice recognition unit, used to verify the identity of the operator;

[0042] Instruction permission management unit, used to limit the operation scope of different roles;

[0043] The operation log recording unit is used to store all voice commands and execution records.

[0044] Compared with the prior art, this application has at least the following beneficial effects:

[0045] 1. Quickly control equipment through voice commands, reduce manual operation steps, improve response speed and thus improve operational efficiency;

[0046] 2. Combine voiceprint recognition and permission management to prevent misoperation or illegal instructions, thereby increasing the security of the system implementation;

[0047] 3. Use noise reduction and speech enhancement technology to ensure recognition accuracy in high-noise environments, making the system adaptable to use in various working conditions;

[0048] 4. Support natural language interaction to reduce the learning cost of operators;

[0049] 5. Provide execution status feedback through voice and visual interfaces to promptly detect and handle abnormal situations;

[0050] 6. Modular design supports integration with other industrial control systems and facilitates functional expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more intuitively illustrate the prior art and the present application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be considered as limiting conditions for implementing the present application; for example, based on the technical concepts disclosed in this application and the exemplary drawings, those skilled in the art are capable of easily making routine adjustments or further optimizations to the addition / reduction / attribution division, specific shapes, positional relationships, connection methods, dimensional ratios, etc. of certain units (components).

[0052] Figure 1 This is a module diagram of a coal transportation production voice system control platform for this application. DETAILED DESCRIPTION

[0053] The present application will be further described below in detail through specific embodiments in conjunction with the accompanying drawings.

[0054] like Figure 1 As shown in the figure, a coal transportation production voice system control platform includes a voice acquisition module, a voice recognition module, a command parsing module, a control logic module, an equipment execution module, a feedback module, a communication module and a safety protection module.

[0055] The voice acquisition module is used to collect the voice instructions of operators at the coal transportation production site in real time and perform noise reduction and enhancement processing, that is, to reduce the impact of environmental noise on the voice instructions and enhance the voice instructions at the same time;

[0056] The speech recognition module is connected to the speech acquisition module and is used to convert the acquired speech instructions into text instructions. The text instruction format can improve the accuracy of subsequent recognition, increase the accuracy of semantic analysis, and optimize the recognition accuracy based on the deep learning model;

[0057] The instruction parsing module is connected to the speech recognition module, and is used to perform semantic analysis on text instructions and then extract key operation instructions based on the content and semantics of the text instructions;

[0058] The control logic module is connected to the instruction parsing module, and the control logic module is used to generate corresponding device control signals according to key operation instructions;

[0059] The equipment execution module is connected to the control logic module, and the equipment execution module is used to receive control signals and drive the coal transportation production equipment to perform corresponding actions;

[0060] The feedback module is used to collect equipment execution status information and provide feedback to the operator through voice or visual interface;

[0061] The communication module is used to realize data interaction between modules and support remote monitoring and data storage;

[0062] The security protection module is used to verify the authority of voice commands, filter out unauthorized operations, and ensure the legitimacy of each operation.

[0063] This solution, when implemented, enables intelligent control of coal handling equipment through voice interaction, improving operational efficiency and reducing the need for manual intervention. The voice acquisition module ensures clear capture of operational instructions, while the voice recognition module converts them into processable text. The instruction parsing module extracts key operations, the control logic module generates equipment control signals, the equipment execution module drives mechanical operation, the feedback module provides real-time status updates, the communication module supports remote monitoring, and the safety protection module ensures the legality of operations. This architecture is suitable for high-noise and high-dust industrial environments, enabling efficient and safe voice control.

[0064] The voice acquisition module includes a microphone, a noise reduction unit and a voice enhancement unit;

[0065] The microphone is used to collect the operator's voice in a targeted manner;

[0066] The noise reduction unit is used to eliminate background noise interference in the voice collected by the microphone;

[0067] The speech enhancement unit is used to improve the signal-to-noise ratio of the operator's speech signal.

[0068] This module uses a highly sensitive microphone array to capture operator voice signals in a targeted manner. A noise reduction unit eliminates background noise (such as mechanical roar and dust interference). Voice enhancement technology improves the signal-to-noise ratio, ensuring clear and intelligible voice commands. This provides high-quality input signals for subsequent voice recognition, preventing misidentification due to environmental interference. The module can be deployed near key equipment such as coal conveyors and crushers. It supports far-field voice pickup and anti-reverberation processing, making it suitable for complex industrial scenarios.

[0069] The speech recognition module adopts an end-to-end deep learning model, which includes an acoustic model, a language model and an adaptive training unit;

[0070] Acoustic models are used to extract speech features;

[0071] Language models are used to optimize semantic understanding;

[0072] The adaptive training unit is used to optimize the recognition accuracy based on coal handling production specific terminology.

[0073] Based on end-to-end deep learning models (such as Transformer or RNN), this module converts speech signals into text instructions. The acoustic model extracts speech features, the language model optimizes semantic understanding, and the adaptive training unit specifically optimizes coal transportation industry terminology (such as "speed up," "emergency stop," and "coal type switching") to improve the recognition rate of specialized vocabulary. Its function is to address the terminology adaptation issues of traditional speech recognition in industrial scenarios, reduce misjudgments caused by accents and noise, and ensure the accuracy of control instructions.

[0074] The instruction parsing module includes a natural language processing unit, a keyword extraction algorithm and a context association unit:

[0075] The natural language processing unit is used to analyze the semantics of instructions;

[0076] Keyword extraction algorithms are used to identify key operating instructions;

[0077] The context association unit is used to optimize instruction parsing in combination with the production environment.

[0078] Natural language processing (NLP) technology analyzes the semantics of text instructions. A keyword extraction algorithm identifies core operations (e.g., "Start belt conveyor No. 1"). A context-sensitive unit optimizes the parsing results by combining equipment status and environmental parameters (e.g., coal flow rate, fault alarms). This function translates ambiguous natural language into precise control commands, such as parsing "increase coal delivery speed" into "increase belt conveyor speed by 10%," thus avoiding ambiguous operations.

[0079] The control logic module includes a rule engine, a priority scheduling unit and an exception handling unit;

[0080] The rule engine is used to match instructions with device control logic;

[0081] The priority scheduling unit is used to handle multiple instructions concurrently;

[0082] The exception handling unit is used to detect and handle illegal instructions.

[0083] This module includes a rules engine (for matching instructions with device control logic), a priority scheduling unit (for handling multiple instruction conflicts), and an exception handling unit (for intercepting illegal instructions). Its function is to ensure that generated control signals comply with safety production regulations, for example, by prioritizing "emergency stop" instructions or prohibiting speed increases when the device is overloaded. This aims to improve system reliability and safety and prevent accidents caused by misoperation.

[0084] The equipment execution module includes a PLC controller, an execution status monitoring unit and a fault warning unit;

[0085] PLC controller is used to drive coal conveyor belt, crusher, coal feeder and other equipment;

[0086] The execution status monitoring unit is used to provide real-time feedback on the equipment operation status;

[0087] The fault warning unit is used to issue an alarm when the equipment is abnormal.

[0088] Coal conveying equipment (such as conveyors and crushers) is driven by a PLC controller. The execution status monitoring unit provides real-time feedback on operating data (speed, temperature, etc.), and the fault warning unit triggers an alarm when an anomaly occurs. During implementation, it converts voice commands into actual equipment actions and monitors the execution results. For example, if the belt slips, the speed will automatically decrease and a voice prompt "Belt No. 2 is operating abnormally" will be issued. The fault alarm unit can also issue an alarm for "Belt No. 2 is operating abnormally."

[0089] The feedback module includes a speech synthesis unit, a visual interface and an alarm unit;

[0090] The speech synthesis unit is used to convert the execution results into speech broadcasts. The use of speech synthesis technology to broadcast the execution results (such as "Crusher No. 1 has started") can further facilitate the staff to grasp the status of the equipment;

[0091] The visual interface is used to display the equipment operating status and equipment operating parameters, making it easy to view the equipment status intuitively and comprehensively;

[0092] The alarm unit is used to send out sound and light alarms in abnormal situations to remind people of faults.

[0093] When the above technical solution is implemented, a closed-loop interaction can be formed, allowing operators to confirm the status of instruction execution in real time, reducing repeated operations or misjudgments caused by information delays.

[0094] The security protection module includes a voice recognition unit, a command authority management unit and an operation log recording unit;

[0095] The voice recognition unit is used to verify the identity of the operator. Through the setting of the voice recognition unit, the identity of the verification person can be identified. On the one hand, the operation authority of different operators can be verified, and on the other hand, it can prevent irrelevant personnel from arbitrarily controlling the equipment.

[0096] The instruction authority management unit is used to limit the operation scope of different roles;

[0097] The operation log recording unit is used to store all voice commands and execution records for subsequent review and tracing.

[0098] Operator identity is verified through voice recognition, and a permissions management unit limits the scope of commands issued by different roles (for example, only the team leader can execute "system restart"). An operation log records all commands for future reference. This prevents unauthorized operation, meets industrial safety compliance requirements, and provides data support for accident tracing.

[0099] The technical features of the above embodiments can be combined arbitrarily (as long as there is no contradiction in the combination of these technical features). In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described; these embodiments that are not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A coal transportation production voice system control platform, characterized in that: It includes voice acquisition module, voice recognition module, instruction parsing module, control logic module, device execution module, feedback module, communication module and security protection module. The voice acquisition module is used to collect the voice instructions of operators at the coal transportation production site in real time and perform noise reduction and enhancement processing; The speech recognition module is connected to the speech acquisition module, and the speech recognition module is used to convert the collected voice instructions into text instructions and optimize the recognition accuracy based on the deep learning model; The instruction parsing module is connected to the speech recognition module, and is used to perform semantic analysis on text instructions and extract key operation instructions; The control logic module is connected to the instruction parsing module, and the control logic module is used to generate corresponding device control signals according to key operation instructions; The equipment execution module is connected to the control logic module, and the equipment execution module is used to receive control signals and drive the coal transportation production equipment to perform corresponding actions; The feedback module is used to collect equipment execution status information and provide feedback to the operator through voice or visual interface; The communication module is used to realize data interaction between modules and support remote monitoring and data storage; The security protection module is used to verify the authority of voice commands and filter out unauthorized operations.

2. A coal transportation production voice system control platform according to claim 1, characterized in that: The voice acquisition module includes a microphone, a noise reduction unit and a voice enhancement unit; The microphone is used to collect the operator's voice in a targeted manner; The noise reduction unit is used to eliminate background noise interference in the voice collected by the microphone; The speech enhancement unit is used to improve the signal-to-noise ratio of the operator's speech signal.

3. A coal transportation production voice system control platform according to claim 1, characterized in that: The speech recognition module adopts an end-to-end deep learning model, which includes an acoustic model, a language model and an adaptive training unit; Acoustic models are used to extract speech features; Language models are used to optimize semantic understanding; The adaptive training unit is used to optimize the recognition accuracy based on coal handling production specific terminology.

4. A coal transportation production voice system control platform according to claim 1, characterized in that: The instruction parsing module includes a natural language processing unit, a keyword extraction algorithm and a context association unit: The natural language processing unit is used to analyze the semantics of instructions; Keyword extraction algorithms are used to identify key operating instructions; The context association unit is used to optimize instruction parsing in combination with the production environment.

5. A coal transportation production voice system control platform according to claim 1, characterized in that: The control logic module includes a rule engine, a priority scheduling unit and an exception handling unit; The rule engine is used to match instructions with device control logic; The priority scheduling unit is used to handle multiple instructions concurrently; The exception handling unit is used to detect and handle illegal instructions.

6. A coal transportation production voice system control platform according to claim 1, characterized in that: The equipment execution module includes a PLC controller, an execution status monitoring unit and a fault warning unit; PLC controller is used to drive coal conveyor belt, crusher, coal feeder and other equipment; The execution status monitoring unit is used to provide real-time feedback on the equipment operation status; The fault warning unit is used to issue an alarm when the equipment is abnormal.

7. A coal transportation production voice system control platform according to claim 1, characterized in that: The feedback module includes a speech synthesis unit, a visual interface and an alarm unit; The speech synthesis unit is used to convert the execution results into speech broadcast; The visual interface is used to display the operating status of the equipment; The alarm unit is used to issue audible and visual alarms in abnormal situations.

8. A coal transportation production voice system control platform according to claim 1, characterized in that: The security protection module includes a voice recognition unit, a command authority management unit and an operation log recording unit; The voice recognition unit is used to verify the identity of the operator; The instruction authority management unit is used to limit the operation scope of different roles; The operation log recording unit is used to store all voice commands and execution records.

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