Mining lamp based on power line carrier single lamp control technology

Abstract

The invention discloses a mining lamp based on a power line carrier single lamp control technology. The mining lamp comprises a mining lamp light source; a power line carrier communication network; the control module is electrically connected with the industrial and mining lamp light source and is in communication connection with a management platform through the power line carrier communication network, and the management platform is configured to obtain electrical parameter information corresponding to the industrial and mining lamp light source and environment parameter information corresponding to the industrial and mining lamp light source; calling an artificial intelligence model to generate target illumination based on the electrical parameter information, the environmental parameter information and a preset historical energy consumption database; and a control instruction generated according to the target illumination is sent to the control module, and the control instruction is used for indicating the control module to perform dimming control on the mining lamp light source according to the target illumination, so that the power consumption of the mining lamp can be reduced, and the energy-saving rate can be improved.

Description

Technical Field

[0001] This application relates to the field of industrial and mining lamp technology, and in particular to an industrial and mining lamp based on power line carrier single-lamp control technology. Background Technology Industrial and mining lamps can be used in large industrial plants. Currently, they are usually controlled to operate according to a set illuminance, so that the lighting conditions in the plant can reach a relatively stable state.

[0002] However, since industrial and mining lamps often consume a lot of power when operating at set illuminance levels, the total power consumption of the factory due to lighting is also high. Summary of the Invention

[0003] To address the aforementioned technical issues, this application proposes an industrial and mining lamp based on power line carrier single-lamp control technology, which can reduce power consumption and improve energy efficiency.

[0004] This application embodiment provides an industrial and mining lamp based on power line carrier single-lamp control technology, the industrial and mining lamp comprising: Industrial and mining lamp light sources; Power line carrier communication networks; and A control module is electrically connected to the industrial and mining lamp light source and communicates with a management platform via the power line carrier communication network. The management platform is configured to: acquire electrical parameter information and environmental parameter information corresponding to the industrial and mining lamp light source; generate a target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database; and send a control command generated according to the target illuminance to the control module, wherein the control command is used to instruct the control module to perform dimming control on the industrial and mining lamp light source according to the target illuminance.

[0005] Optionally, the control module includes: The main controller, which controls the industrial lamp light source, periodically sends heartbeat packets to the backup controller; and The backup controller is used to determine whether the number of heartbeat packets received within the most recent preset time period has reached the target number. If it has not reached the target number, it determines that there is a control fault and controls the mining lamp light source to switch from being controlled by the main controller to being controlled by the backup controller. When the control fault is detected to be eliminated, it controls the mining lamp light source to switch from being controlled by the backup controller to being controlled by the main controller.

[0006] Optionally, the power line carrier communication network includes a phase-to-phase coupling module, the phase-to-phase coupling module comprising: Three-phase power lines; Multiple coupling transformers, each corresponding to a controller in the control module; and Multiple impedance matching networks, each of which corresponds to a controller in the control module; In this control module, each controller is sequentially connected to one or more phases of the three-phase power line through the impedance matching network and coupling transformer corresponding to that controller.

[0007] Optionally, each of the impedance matching networks includes: Variable capacitor array; and Variable inductor array; The impedance of each impedance matching network is determined using an improved particle swarm optimization algorithm. The particle position of the improved particle swarm optimization algorithm is determined by the capacitance value of the variable capacitor array and the inductance value of the variable inductor array. The fitness function of the improved particle swarm optimization algorithm is determined based on the impedance of each impedance matching network and the impedance of the electric field line to which each impedance matching network is electrically connected.

[0008] Optionally, each of the plurality of coupling transformers is connected in series with the power line to which it is electrically connected, and a common-mode choke is connected in series.

[0009] Optionally, a differential amplifier circuit is connected in series between each of the plurality of coupling transformers and the power line to which it is electrically connected.

[0010] Optionally, the artificial intelligence model is a Long Short-Term Memory (LSTM) neural network model; The step of generating target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. The electrical parameter information, the environmental parameter information, and the historical energy consumption information are input into the LSTM neural network model to obtain the target illuminance output by the LSTM neural network model.

[0011] Optionally, the step of generating the target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: The electrical parameter information is used to extract a first frequency feature and a second frequency feature. Attention is calculated based on the first frequency feature and the second frequency feature to obtain a comprehensive feature. The first frequency corresponding to the first frequency feature is the driving frequency corresponding to the industrial and mining lamp light source, and the second frequency corresponding to the second frequency feature is an integer multiple of the driving frequency. Based on the comprehensive features, the environmental parameter information, and the historical energy consumption database, an artificial intelligence model is invoked to generate the target illuminance.

[0012] Optionally, the artificial intelligence model is a large model; The step of generating target illuminance by calling an artificial intelligence model based on the comprehensive features, the environmental parameter information, and the historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. Based on the comprehensive features and the environmental parameter information, the large model is invoked to generate the first illuminance prediction result; The first illuminance prediction result is adjusted based on the historical energy consumption information to obtain the second illuminance prediction result; Determine the first difference information between the first illuminance prediction result and the second illuminance prediction result; The target illuminance is generated based on the first distinguishing information, the comprehensive features, and the environmental parameter information.

[0013] Optionally, generating the target illuminance based on the first distinguishing information, the comprehensive features, and the environmental parameter information includes: Self-attention calculation is performed based on the first difference information to obtain the second difference information; The second distinguishing information is fused into the comprehensive feature to obtain the target feature, and the second distinguishing information is fused into the environmental parameter information to obtain the target environmental parameter information; Based on the target features and the target environmental parameter information, the target illumination is generated by calling the large model.

[0014] In summary, the embodiments of this application have at least the following beneficial effects: According to the embodiments of this application, an industrial and mining lamp based on power line carrier single-lamp control technology includes: an industrial and mining lamp light source; a power line carrier communication network; and a control module electrically connected to the industrial and mining lamp light source and communicating with a management platform through the power line carrier communication network. The management platform is configured to: acquire electrical parameter information and environmental parameter information corresponding to the industrial and mining lamp light source; generate a target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database; and send a control command generated according to the target illuminance to the control module. The control command instructs the control module to perform dimming control on the industrial and mining lamp light source according to the target illuminance. In this way, the electrical parameter information and environmental parameter information of the industrial and mining lamp light source can be comprehensively considered, combined with the historical energy consumption database, to generate a target illuminance adapted to the actual situation of the industrial and mining lamp through an artificial intelligence model, thereby achieving dynamic dimming control of a single industrial and mining lamp light source, reducing power consumption and improving energy efficiency. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of an industrial and mining lamp based on power line carrier single-lamp control technology provided in an embodiment of this application; Figure 2 This is a schematic diagram of the workflow of the dual-redundant controller architecture provided in the embodiments of this application; Figure 3 This is a schematic diagram of the cross-phase coupling process provided in the embodiments of this application; Figure 4 This is a flowchart illustrating the improved particle swarm optimization algorithm provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating the workflow of each component in the controller provided in the embodiments of this application; Figure 6 This is a schematic diagram of the fault protection logic provided in the embodiments of this application; Figure 7 This is a schematic diagram of the adaptive frequency hopping process provided in the embodiments of this application; Figure 8 This is a schematic diagram of the dynamic route construction process provided in the embodiments of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments / examples are only a part of the embodiments / examples of this application, and not all of the embodiments / examples. Based on the embodiments / examples in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more. In the description of this application, the term "comprising" and its variations are open-ended, meaning "including but not limited to." The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment / example" means "at least one embodiment / example"; the term "another embodiment / example" means "at least one additional embodiment / example"; the term "some embodiments / examples" means "at least some embodiments / examples."

[0018] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0019] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing specific embodiments only and is not intended to limit the application. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0020] Firstly, embodiments of this application provide an industrial and mining lamp based on power line carrier single-lamp control technology, which can be found in [reference needed]. Figure 1 This illustration shows a structural diagram of an industrial and mining lamp based on power line carrier single-lamp control technology according to an embodiment of this application. The industrial and mining lamp includes: Industrial and mining lamp light source; for example, industrial and mining lamp light source can refer to the light source board of an industrial and mining lamp, such as a light-emitting diode (LED) light source board; Power line carrier communication networks; and A control module is electrically connected to the industrial and mining lamp light source and communicates with a management platform via the power line carrier communication network. The management platform is configured to: acquire electrical parameter information and environmental parameter information corresponding to the industrial and mining lamp light source; generate a target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database; and send a control command generated according to the target illuminance to the control module, wherein the control command is used to instruct the control module to perform dimming control on the industrial and mining lamp light source according to the target illuminance.

[0021] In some examples, the electrical parameter information corresponding to the industrial and mining lamp light source may include at least one of the following: voltage, current, and power factor. The acquisition accuracy of the electrical parameter information may be ±1%. The electrical parameter information may be obtained by an electrical parameter sensor that collects data from the industrial and mining lamp light source in real time. This electrical parameter sensor may communicate with the management platform via a power line carrier communication network, and / or the electrical parameter sensor may also transmit the collected electrical parameter information to the control module, which in turn transmits it to the management platform via the power line carrier communication network.

[0022] In some examples, the environmental parameter information can be environmental parameter information obtained by detecting the environment in which the industrial and mining lamp light source is located. For example, a sensor can be set up in the environment in which the industrial and mining lamp light source is located so as to detect the environment in which the industrial and mining lamp light source is located. The sensor set up can include at least one of the following: an infrared sensor for detecting the flow of people in the environment, and an illuminance sensor for detecting ambient light (such as natural light).

[0023] In some examples, the historical energy consumption database may include historical energy consumption information under different conditions, which may refer to different environmental parameters. This historical energy consumption information may be historical data such as regional electricity consumption, regional light intensity trends, and regional pedestrian traffic in the environment where industrial and mining lamp sources are located within a historical period (e.g., the past 7 days).

[0024] In some examples, the control command can carry a target illuminance, thereby enabling the control module to generate a dimming command based on the target illuminance to control the dimming of industrial and mining lamp light sources.

[0025] In some examples, the management platform may be a set of energy management platforms deployed in the cloud, serving as an energy management platform.

[0026] In one optional implementation, the control module includes: The main controller, which controls the industrial lamp light source, periodically sends heartbeat packets to the backup controller; and The backup controller is used to determine whether the number of heartbeat packets received within the most recent preset time period has reached the target number. If it has not reached the target number, it determines that there is a control fault and controls the mining lamp light source to switch from being controlled by the main controller to being controlled by the backup controller. When the control fault is detected to be eliminated, it controls the mining lamp light source to switch from being controlled by the backup controller to being controlled by the main controller.

[0027] In some examples, the control module employs a dual-redundant controller architecture, ensuring the continuity and reliability of the control link in the industrial and mining lamp system through hot standby switching between the primary and backup controllers. Each controller in the control module (e.g., a primary controller and a backup controller) can utilize an ARM Cortex-M7 processor to run a real-time operating system. Each controller can be equipped with a power line interface, which can integrate a high-performance power line communication (PLC) modem for communication over the power line. The backup controller can also be equipped with a heartbeat monitoring circuit, a key detection component in the dual-redundant controller architecture. Its core function is to detect the primary and backup status of the primary and backup controllers through power line carrier signals. Specifically, the heartbeat monitoring circuit can capture heartbeat packets in the power line carrier communication network in real time and feed back the reception status of these packets to the backup controller, providing hardware-level detection evidence for the backup controller to determine whether the primary controller has experienced a control failure.

[0028] In some examples, see Figure 2 The diagram illustrates the workflow of the dual-redundant controller architecture provided in this embodiment.

[0029] 1) After the system starts up, the control module enters the initial process of dual redundancy control. The main controller sends the heartbeat packet to the power line carrier communication network in the form of a power line carrier signal through the power line interface according to the preset cycle. The backup controller listens to the heartbeat packet signal in real time through the power line interface, and the heartbeat monitoring circuit detects the transmission and reception status of the heartbeat packet.

[0030] 2) The backup controller continuously checks whether it receives a heartbeat packet from the main controller. If the backup controller receives the heartbeat packet, it determines that the main controller is operating normally and there is no control fault. At this time, the main controller continues to work, maintains control over the industrial and mining lamp light source, and performs dimming control on the industrial and mining lamp light source according to the control instructions issued by the management platform. If the backup controller does not receive the heartbeat packet, it triggers the status detection process of the heartbeat monitoring circuit. The heartbeat monitoring circuit further confirms the unreceived heartbeat packet status and feeds back the detection result to the backup controller.

[0031] 3) Based on the feedback from the heartbeat monitoring circuit, the backup controller determines whether it has failed to receive heartbeat packets for three consecutive times. If it has not failed to receive heartbeat packets for three consecutive times, it is determined that the main controller only has a temporary signal transmission abnormality and no substantial control fault. The backup controller returns to normal monitoring state and continues to monitor the heartbeat packets sent by the main controller in real time. If it has failed to receive heartbeat packets for three consecutive times, the backup controller determines that the main controller has a control fault and immediately starts the main / backup switchover process.

[0032] 4) Master / Standby Switchover Execution Phase: When a control fault is detected in the master controller, the standby controller takes over control authority within 200ms, switching the control of the industrial and mining lamp light source from being controlled by the master controller to being controlled by the standby controller. After the switchover is complete, the standby controller replaces the master controller, receives control commands issued by the management platform, and performs dimming control on the industrial and mining lamp light source, ensuring the normal operation of the entire industrial and mining lamp control system.

[0033] 5) Fault Recovery and Role Switching Phase: After the standby controller takes over control, it continuously monitors the status of the main controller via the heartbeat monitoring circuit. When the control fault of the main controller is detected to be eliminated (i.e., the heartbeat packet sent by the main controller is received again), the control module initiates the automatic role switching process, switching the control of the mining lamp light source from being controlled by the standby controller to being controlled by the main controller. The main controller regains control of the mining lamp light source, and the standby controller returns to normal monitoring status, completing one complete cycle of dual redundancy control.

[0034] In this embodiment, a distributed dual-redundant controller architecture is adopted. The main controller and the backup controller can monitor each other via power lines. When the main controller fails, the backup controller can automatically take over the control of all industrial and mining lamps within 200ms to prevent system paralysis. At the same time, each industrial and mining lamp has a built-in local cache module to store the latest instruction status, which can maintain the basic lighting strategy even in the event of extreme communication interruption, ensuring production safety.

[0035] In one optional implementation, the power line carrier communication network includes a phase-coupling module, the phase-coupling module comprising: Three-phase power lines; Multiple coupling transformers, each corresponding to a controller in the control module; and Multiple impedance matching networks, each of which corresponds to a controller in the control module; In this control module, each controller is sequentially connected to one or more phases of the three-phase power line through the impedance matching network and coupling transformer corresponding to that controller.

[0036] In some examples, cross-phase coupling modules can be used to enable cross-phase transmission of power line carrier signals to ensure the reliability of the communication link and the efficiency of signal transmission.

[0037] In some examples, three-phase power lines can serve as the physical carrier for signal transmission. Three-phase power lines (L1, L2, L3) can be used to provide a transmission channel for power line carrier communication between the management platform and the control module.

[0038] In some examples, the coupling transformer can be a broadband signal coupling transformer. Here, as the core component of signal coupling, the broadband signal coupling transformer can be used to couple power line carrier signals to the three-phase power lines, achieving electrical isolation and signal transmission. In this embodiment, multiple coupling transformers correspond to each controller in the control module (for example, each controller in the control module can correspond one-to-one with each coupling transformer). Each controller in the control module can be electrically connected to one or more phases of the three-phase power lines through the coupling transformer corresponding to that controller.

[0039] In some examples, the impedance matching network can be an adaptive impedance matching network, which can be used to dynamically adjust the impedance of the signal transmission link and reduce signal reflection loss. In this embodiment, multiple impedance matching networks correspond to each controller in the control module (for example, each controller in the control module can correspond one-to-one with each impedance matching network). Each controller in the control module can be sequentially connected to one or more phases of the three-phase power line through its corresponding impedance matching network and coupling transformer.

[0040] In this embodiment, when cross-phase communication is required, the cross-phase coupling module couples the power line carrier signal to the three-phase power lines through a broadband signal coupling transformer, enabling signal transmission between different phase lines. When cross-phase communication is not required, the signal can be directly transmitted to the target line. The adaptive impedance matching network can dynamically adjust according to the impedance changes of the three-phase power lines to achieve impedance matching, thereby reducing signal reflection loss and improving signal transmission efficiency and communication quality.

[0041] In some examples, see Figure 3 The diagram illustrates a cross-phase coupling workflow provided in an embodiment of this application.

[0042] 1) Signal input: The three-phase power lines L1, L2, and L3 input power line carrier signals as signal sources for cross-phase coupling.

[0043] 2) Cross-phase communication judgment: Determine whether cross-phase communication is required.

[0044] If cross-phase communication is required: a broadband signal coupling transformer couples the carrier signal to the three-phase power line; an adaptive impedance matching network dynamically adjusts the impedance to reduce signal reflection loss; the carrier signal is then coupled to the three-phase line to complete the cross-phase signal injection.

[0045] If cross-phase communication is not required: transmit signals directly to the target line without additional coupling processing.

[0046] 3) Cross-phase coupling completed: Regardless of whether cross-phase communication is required, the signal is successfully transmitted to the target line, and the cross-phase coupling process ends.

[0047] In one alternative implementation, each of the impedance matching networks includes: Variable capacitor array; and Variable inductor array; The impedance of each impedance matching network is determined using an improved particle swarm optimization algorithm. The particle position of the improved particle swarm optimization algorithm is determined by the capacitance value of the variable capacitor array and the inductance value of the variable inductor array. The fitness function of the improved particle swarm optimization algorithm is determined based on the impedance of each impedance matching network and the impedance of the electric field line to which each impedance matching network is electrically connected.

[0048] In this embodiment, a dynamic adaptive impedance matching network can be added before coupling the coupling transformer. Compared with the impedance matching network with fixed impedance in some examples, the impedance can be dynamically adjusted in real time.

[0049] When implementing dynamic impedance matching, the control unit can monitor changes in power line impedance in real time and automatically adjust the variable capacitor array and variable inductor array in the impedance matching network to achieve conjugate matching of Z_match = Z_line* (where Z_match is the impedance of the impedance matching network and Z_line* is the conjugate complex number of the power line impedance). In this embodiment, when the impedance of the impedance matching network is equal to the conjugate complex number of the power line impedance, the signal reflection coefficient Γ approaches 0, which can significantly improve signal transmission efficiency. In addition, the resonant frequency can be automatically adjusted according to the communication frequency band (2MHz-12MHz) to improve transmission performance under different interference environments.

[0050] In some examples, a variable capacitor array can consist of multiple high-voltage ceramic capacitors, with the number of capacitors connected being switched via high-speed relays or solid-state relays to achieve dynamic adjustment of the capacitance value. A variable inductor array can employ inductors with adjustable core positions (or digital inductor chips), with the inductance value being changed via stepper motors or digital control signals to achieve dynamic adjustment of the inductance value.

[0051] In some examples, high-precision voltage transformers and current transformers can also be set up to acquire voltage V(t) and current I(t) signals on the power line in real time, and then calculate the current power line impedance Z_current=V(t) / I(t).

[0052] In related technologies, conventional impedance matching techniques often suffer from slow speed and a tendency to get trapped in local optima. This application introduces an improved particle swarm optimization algorithm, transforming impedance matching into a mathematical optimization problem of finding the minimum signal reflection coefficient. (See also...) Figure 4 The diagram shows a flowchart of the improved particle swarm optimization algorithm provided in the embodiments of this application. The specific process is as follows.

[0053] 1) The current power line voltage V(t) and current I(t) are collected in real time through the impedance measurement front end, and the current power line impedance Z_current=V(t) / I(t) is calculated as the input reference of the algorithm.

[0054] 2) The fitness function is used to evaluate the merits of each candidate impedance matching scheme, and can be expressed as: F(x) = max(P_tx) or F(x) = min(∣Γ∣) The signal reflection coefficient Γ is calculated using the formula: Γ = (Z_line - Z_match) / (Z_line + Z_match). In this formula, Z_line is the electric field line impedance (i.e., Z_current), and Z_match is the candidate impedance of the impedance matching network (determined by the capacitance C of the variable capacitor array and the inductance L of the variable inductor array).

[0055] 3) Encode the capacitance value C of the variable capacitor array and the inductance value L of the variable inductor array into the position vector X=(X_c,X_l) of the particles, where X_c corresponds to the combination of capacitance values ​​and X_l corresponds to the combination of inductance values; randomly initialize a group of particles, that is, generate multiple different C / L combinations as initial candidate schemes.

[0056] 4) Calculate the fitness value of each particle based on the current electric field line impedance Z_current and the candidate impedance Z_match corresponding to each particle, and evaluate its impedance matching effect.

[0057] 5) Based on the fitness value of the particles, update the individual historical best position (pBest) of each particle and the global best position (gBest) of the entire particle swarm to guide the particles to iterate towards a better solution.

[0058] 6) Determine whether the current particle's fitness value satisfies the preset convergence conditions (such as fitness change being less than a threshold, iteration count reaching the upper limit, etc.): If the convergence condition is not met, return to step (4) and continue iterative optimization; If the convergence condition is met, proceed to the next step.

[0059] 7) Output the optimal capacitance value C_opt and optimal inductance value L_opt corresponding to the global optimal solution gBest, as the target parameters of the impedance matching network.

[0060] 8) Based on the optimal output parameters, control the actuator to complete the impedance adjustment: Control the relay array to connect to the capacitor combination corresponding to the optimal capacitance value C_opt; The stepper motor is controlled to adjust the variable inductor array to the optimal inductance value L_opt, thereby achieving conjugate matching between the impedance of the impedance matching network and the power line impedance.

[0061] In this embodiment, a three-phase independent matching + common-mode coupling architecture can be adopted to replace the single-channel design in related technologies, thereby improving the load balancing capability and redundancy of communication. Specifically, this embodiment designs independent impedance matching networks and coupling transformers for the three phases L1-N, L2-N, and L3-N respectively. The impedance matching network of each phase includes a corresponding variable capacitor array and a variable inductor array. The coupling transformer is connected to the power line of that phase, and a multiplexer (MUX) is used to realize the selection and switching of signals between phases. The target phase is selected for signal transmission according to communication requirements. A phase detection circuit can also be added to monitor the phase difference of the three-phase voltage in real time, providing data support for phase synchronization.

[0062] Thus, when a phase is overloaded (impedance is too low), the control system corresponding to the industrial and mining lamp can automatically switch the corresponding communication to other phases through a multiplexer to avoid single-phase overload affecting the overall communication quality. In addition, the phase detection circuit can detect the phase difference of the three-phase voltage and dynamically adjust the transmission timing of the carrier signal to achieve phase alignment of cross-phase communication and reduce signal interference. Furthermore, when any phase communication is interrupted, the control system corresponding to the industrial and mining lamp can automatically switch the corresponding communication to other phases through a multiplexer to ensure communication continuity and improve system reliability.

[0063] In one alternative implementation, each of the plurality of coupling transformers is connected in series with a common-mode choke between itself and the power line to which it is electrically connected.

[0064] In one alternative implementation, a differential amplifier circuit is connected in series between each of the plurality of coupling transformers and the power line to which it is electrically connected.

[0065] In this embodiment, a common-mode choke and a differential amplifier circuit can be added to the secondary output of the coupling transformer to improve the system's common-mode interference immunity. Specifically, a common-mode choke can be connected in series between the secondary of the coupling transformer and the three-phase power lines to suppress common-mode noise; differential signal transmission can be used instead of single-ended signal transmission to improve interference immunity; and a common-mode feedback control circuit can be added to dynamically adjust the parameters of the common-mode choke.

[0066] Understandably, a common-mode choke presents high impedance to common-mode noise (such as power frequency interference) on three-phase power lines, while presenting low impedance to differential-mode signals (power line carrier communication signals). This effectively filters out common-mode noise and amplifies useful differential-mode signals through a differential amplifier circuit, while simultaneously suppressing common-mode interference and improving the signal-to-noise ratio. In this way, the common-mode feedback control circuit can monitor the common-mode voltage in real time and dynamically adjust the parameters of the common-mode choke to adapt to noise suppression requirements under different interference environments.

[0067] In some examples, phase-split carrier coupling and ground-carrier coupling can be used instead of traditional phase-to-ground / phase-to-phase coupling to reduce signal attenuation and interference. Specifically, for three-phase systems using split conductors, the power line carrier signal can be coupled between the split conductors. When phase conductors use split conductors, coupling the power line carrier signal between the split conductors can reduce signal transmission attenuation and increase communication distance. In systems with overhead ground wires, the power line carrier signal can be coupled to the ground wire. In systems with overhead ground wires, coupling the power line carrier signal to the ground wire can reduce interference to the phase conductors and improve signal purity. Phase-to-phase couplers can be added to achieve direct signal transmission between different phases. Through phase-to-phase couplers, signals can be directly transmitted between different phases, avoiding signal loss caused by indirect coupling through the neutral wire and improving signal transmission efficiency.

[0068] In some examples, the controller may include core components such as a carrier communication unit, a local buffer, and a dimming driver. These components work together to receive power line carrier signals, store instructions, and execute dimming actions. (See also...) Figure 5 The diagram illustrates the workflow of each component in the controller provided in this embodiment of the application. The specific component functions and overall workflow are as follows.

[0069] The carrier communication unit uses a digital signal processing (DSP) chip as its core processing component. Its core function is to receive the carrier signal transmitted by the power line carrier communication network. It can also run a dynamic routing algorithm through the DSP chip to parse, filter, and optimize the routing of dimming control commands in the carrier signal, ensuring that the control commands can be accurately and efficiently extracted and transmitted to the subsequent processing stage.

[0070] The local cache can be built using non-volatile memory (ferroelectric RAM, FRAM), whose core function is to store the most recent dimming control command received by the industrial and mining lamp light source. FRAM has the characteristic of not losing data when power is off, and can provide command reference for the industrial and mining lamp light source when communication is interrupted or the equipment is restarted, ensuring the continuity of lighting control.

[0071] Among them, the dimming drive uses pulse width modulation (PWM) output as the core control method. Its core function is to adjust the duty cycle of the PWM signal according to the parsed dimming control command, and adjust the output current to the industrial and mining lamp light source according to the duty cycle, so as to achieve precise control of the illuminance of the industrial and mining lamp light source.

[0072] In some examples, to address communication interruptions in power line carrier communication networks and prevent safety hazards caused by uncontrolled lighting in industrial and mining settings, the control module incorporates dedicated fault protection logic. This logic uses the duration of the communication interruption as a criterion to execute differentiated lighting control strategies. (See also...) Figure 6 The diagram shows a fault protection logic provided in an embodiment of this application. The specific logic flow is as follows.

[0073] 1) Start-up judgment process: The control module monitors the communication status between itself and the management platform in real time. When the power line carrier communication is interrupted, the fault protection judgment process is started immediately.

[0074] 2) Communication interruption duration judgment: The core control unit of the control module times the duration of the communication interruption and determines whether the communication interruption time is less than 5 minutes. If the determination result is yes (communication interruption time < 5 minutes), it is determined to be a temporary communication anomaly. The control module executes the strategy of maintaining the last state, that is, it performs dimming control according to the illuminance corresponding to the most recent valid dimming control instruction stored in FRAM, so as to keep the current lighting state unchanged and wait for communication to be restored. If the judgment result is negative (communication interruption time ≥ 5 minutes), it is judged as a continuous communication failure. The control module executes the safety mode activation strategy, and automatically adjusts the illuminance of the light source to 50% through dimming control to ensure the basic operation and safe passage requirements of the mining scene.

[0075] 3) Process End: After the control module executes the corresponding strategy, the fault protection logic process is completed; if subsequent communication is restored, the control module will automatically exit the fault protection mode and re-receive and execute the latest control instructions issued by the management platform.

[0076] In some examples, the power line carrier communication network is the core transmission link used by the control module, and its communication reliability is crucial for the control of industrial and mining lamps. To adapt to the complex electromagnetic environment of industrial and mining scenarios, the power line carrier communication network can adopt two core communication methods: adaptive frequency hopping and dynamic route construction. These methods enable intelligent selection of communication frequency bands and dynamic optimization of communication routes. The following provides a detailed explanation of these two processes.

[0077] Understandably, the core objective of the adaptive frequency hopping process is to select the communication frequency band with the strongest anti-interference capability and the best transmission quality from the available frequency bands for power line carrier communication, and to switch promptly when the frequency band quality deteriorates, ensuring the stable transmission of control commands and electrical parameter information. (See also...) Figure 7 The diagram shows an adaptive frequency hopping process provided in an embodiment of this application. The specific execution steps are as follows.

[0078] 1) Initial Frequency Band Scan: After the power line carrier communication network is started, a comprehensive initial scan of the preset carrier communication frequency bands (2-30MHz) is first performed. This scan process covers all available frequency bands for power line carrier communication in industrial and mining scenarios, providing complete basic data for subsequent frequency band quality assessment.

[0079] 2) Calculate the Signal-to-Noise Ratio (SNR) for each frequency band: Based on the results of the initial frequency band scan, the signal processing unit of the power line carrier communication network performs signal analysis on each sub-frequency band within the scan range and accurately calculates the signal-to-noise ratio (SNR) for each sub-frequency band. As a core indicator for evaluating the quality of frequency band communication, the SNR directly reflects the ratio of useful signals to interference signals within a sub-frequency band; a higher value indicates a better communication environment for that frequency band.

[0080] 3) Selecting the optimal frequency band with an SNR > 20dB: Based on the signal-to-noise ratio (SNR) calculation results, the power line carrier communication network selects frequency bands with an SNR greater than 20dB from all sub-bands, and determines the frequency band with the best communication quality from these, using it as the current operating frequency band for power line carrier communication. This threshold setting ensures that the selected operating frequency band can effectively resist electromagnetic interference in industrial and mining scenarios, providing a basic guarantee for signal transmission.

[0081] 4) Sending test frames to verify transmission quality: After determining the optimal frequency band, the power line carrier communication network will send test data frames to that frequency band. By receiving the feedback results of the test frames, the actual transmission quality of the optimal frequency band is verified, with a focus on detecting the integrity and stability of data transmission.

[0082] 5) Bit Error Rate Determination and Frequency Band Switching: The power line carrier communication network performs statistical analysis on the transmission bit error rate of the test frames. If the bit error rate is determined to be greater than 10... -5If the error rate is ≤10%, it indicates that the communication quality of the current optimal frequency band can no longer meet the system requirements. At this point, the system will immediately trigger a frequency band switching mechanism, switching to the previously selected suboptimal frequency band and re-executing the test frame verification process; if the bit error rate is ≤10... -5 If the optimal frequency band is confirmed to be available, the current operating frequency band will be maintained for normal communication.

[0083] In this embodiment, adaptive frequency hopping technology is employed to monitor power line channel quality in real time, dynamically select the optimal communication frequency band, and effectively avoid the impact of pulse interference and load fluctuations. Furthermore, forward error correction coding (FEC) and spread spectrum communication technology are introduced to reduce the bit error rate to 10%. -6 The following ensures communication reliability in complex industrial environments. Furthermore, through a cross-phase coupling module design, seamless signal transmission between three-phase power lines is achieved, overcoming single-phase limitations and expanding the control range by up to 300%.

[0084] Understandably, the core of the dynamic route construction process is to build the optimal data transmission path for the communication corresponding to the control module and update the route in real time according to changes in link quality. This solves the communication problems caused by complex power line links and large signal attenuation in industrial and mining scenarios. (See also...) Figure 8 The diagram illustrates the dynamic route construction process provided in this embodiment of the application, and the specific execution steps are as follows.

[0085] 1) Terminal Power-On Broadcast Route Request (RREQ): After the control module powers on, it can broadcast a route request message (RREQ) to the surrounding area via the power line carrier communication network. This message contains core information such as the control module's identity and communication requirements, and is used to initiate a route construction request to find a transmission path that can establish a communication connection with the management platform.

[0086] 2) Relay Node Route Response (RREP): After receiving a route request message (RREQ) broadcast by the control module, a relay node in the power line carrier communication network will reply with a route response message (RREP) based on its own communication status. This response message contains key data such as the relay node's identity information, link status with the management platform, and signal transmission strength, providing a basis for the construction of the routing table.

[0087] 3) Controller builds routing table: The control module collects all routing response messages (RREP) from relay nodes and, combining them with core parameters such as hop count and signal strength, constructs a complete routing table using routing algorithms. This routing table indicates the optimal transmission path between each relay node and the management platform, containing key information such as relay nodes, hop count, and signal strength, providing path guidance for data transmission.

[0088] 4) Periodic route updates: To adapt to the dynamic changes in power line link status in industrial and mining scenarios, the control module updates the routing table according to a preset period (every 10 minutes). By re-collecting routing request and response messages and refreshing the link parameters of each transmission path, the routing table is ensured to always reflect the current optimal communication path, avoiding a decrease in communication efficiency due to link aging or load changes.

[0089] 5) Link quality detection and rerouting trigger: In addition to periodic updates, the control module also monitors the signal strength of each communication link in real time. If the signal attenuation of a link exceeds 3dB, it is determined that the communication quality of that link has significantly deteriorated and cannot meet the data transmission requirements. At this time, the system will immediately trigger the rerouting mechanism, re-execute the process of "terminal broadcasting route request - relay node replying with route response - controller building routing table", and re-plan the optimal communication path for the control module to ensure the continuity and reliability of communication.

[0090] In this embodiment, a dynamic routing algorithm is employed to automatically construct the optimal communication path based on the network topology, supporting multi-level relays and mesh networking. This design overcomes the routing limitations of traditional analog chips, achieving up to 32 relay hops to ensure full-area coverage in large factories and obstacle-ridden scenarios. The protocol stack adopts a lightweight design, controlling communication latency to within 50ms, meeting the requirements for real-time dimming and emergency control.

[0091] It is understood that, as shown in Table 1 below, a comparison between the embodiments of this application and related technologies is presented.

[0092] Table 1

[0093] In one optional implementation, the artificial intelligence model is a Long Short-Term Memory (LSTM) neural network model; The step of generating target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. The electrical parameter information, the environmental parameter information, and the historical energy consumption information are input into the LSTM neural network model to obtain the target illuminance output by the LSTM neural network model.

[0094] In this embodiment, the LSTM neural network model has the ability to model and predict long-term dependencies in time-series data. It can fully integrate the time-series change characteristics of electrical parameter information, environmental parameter information, and historical energy consumption information to output target illuminance that meets the lighting needs of industrial and mining sites, ensuring the accuracy and rationality of dimming control.

[0095] In some examples, electrical parameters, environmental parameters, and historical energy consumption information are input into an LSTM neural network model. The LSTM model can then perform feature extraction and time-series prediction calculations on the input information to obtain the target illuminance output by the LSTM model. Specifically, electrical parameters, environmental parameters, and historical energy consumption information can be fused to form a structured input feature vector, which is then used as the time-series input sequence of the LSTM neural network model. This time-series input sequence fully reflects the correlation between the current operating state and historical behavior patterns. The LSTM neural network model can capture long-term dependencies in the time-series input sequence through its internal memory units and gating mechanisms, such as the diurnal periodicity of lighting demand, the dynamic changes in pedestrian traffic, and / or the impact trend of equipment aging on energy efficiency. After processing by the LSTM neural network model, it can output a numerical result, namely the target illuminance. This target illuminance represents the optimal illuminance level recommended under current environmental conditions and equipment status, balancing safety lighting requirements and energy-saving goals, and is measured in lux (lx).

[0096] In some examples, an LSTM neural network model can employ a single-layer LSTM architecture followed by a fully connected output layer. The overall structure of an LSTM neural network model can include: The input layer is used to receive a feature vector composed of electrical parameter information, environmental parameter information, and historical energy consumption information; An LSTM layer, containing up to 32 LSTM units (i.e., a hidden state dimension of 32), can be used to extract dynamic dependencies in time series data. Through its forget gate, input gate, and output gate mechanisms, the LSTM layer can selectively remember or forget historical information, effectively capturing the patterns of changing lighting needs over time (e.g., reducing artificial illumination when natural light is sufficient during the day, and increasing brightness during peak nighttime traffic). Fully connected layers can be used to map the output of LSTM layers to a single numerical value, namely the target illumination. In this way, fully connected layers can compress the high-dimensional temporal features extracted by LSTM into a single illumination value, realizing an end-to-end mapping from multi-dimensional input to target illumination.

[0097] Accordingly, the main model parameters of an LSTM neural network model may include: Time step: 10, which means that the model makes predictions based on historical data from the most recent 10 time points; Input feature dimensions: 8, which are the input feature dimensions corresponding to voltage, current, power factor, pedestrian flow, ambient illuminance, timestamp, weekday identifier, and historical average power, respectively; Number of hidden cells in LSTM: 32; Output dimension: 1 (target illuminance, unit: lux); Activation function: LSTM can use Sigmoid and Tanh gating mechanisms internally, and the output layer has no activation function (linear output); Optimizer: Adam, learning rate set to 0.001; and Loss function: mean squared error.

[0098] In some examples, high-precision power metering modules can be installed on industrial and mining lamp light sources to monitor parameters such as voltage, current, and power factor of each lamp in real time, and transmit these parameters back to the management platform via a carrier channel. Based on big data analysis, energy consumption optimization strategies can be generated, such as dynamically adjusting illuminance according to workshop traffic and natural light intensity to achieve energy savings of ≥35%. Furthermore, standard interfaces are reserved to support integration with factory MES systems and environmental sensors, expanding industrial IoT functions such as equipment status monitoring and fault early warning.

[0099] In some examples, historical energy consumption information may include data such as voltage, current, active power, power factor, and corresponding dimming ratio and actual illuminance value recorded during the past operation of the industrial and mining lamp light source or similar industrial and mining lamp light sources under similar environmental conditions.

[0100] In one optional implementation, the step of generating the target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: The electrical parameter information is used to extract a first frequency feature and a second frequency feature. Attention is calculated based on the first frequency feature and the second frequency feature to obtain a comprehensive feature. The first frequency corresponding to the first frequency feature is the driving frequency corresponding to the industrial and mining lamp light source, and the second frequency corresponding to the second frequency feature is an integer multiple of the driving frequency. Based on the comprehensive features, the environmental parameter information, and the historical energy consumption database, an artificial intelligence model is invoked to generate the target illuminance.

[0101] In this embodiment, the management platform first performs frequency domain analysis on the electrical parameter information. Specifically, a first frequency feature and a second frequency feature are extracted from the electrical parameter information. The first frequency feature corresponds to the driving frequency of the industrial lamp light source (e.g., the operating frequency of an LED driver power supply, typically 20kHz). The second frequency feature corresponds to an integer multiple of the driving frequency (e.g., harmonic components such as 40kHz and 60kHz). Thus, by extracting the fundamental frequency and its harmonic components, the operating state, load characteristics, and potential anomalies (such as switching noise and increased current ripple) of the driving circuit can be effectively captured.

[0102] Subsequently, the management platform performs attention calculations based on the first and second frequency characteristics to dynamically weight the importance of different frequency bands, thereby obtaining a comprehensive feature. This comprehensive feature can more comprehensively characterize the electrical operating status of industrial and mining lamp light sources, and is particularly suitable for identifying inconspicuous faults caused by drive aging or power grid interference.

[0103] In one alternative implementation, the artificial intelligence model is a large model; The step of generating target illuminance by calling an artificial intelligence model based on the comprehensive features, the environmental parameter information, and the historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. Based on the comprehensive features and the environmental parameter information, the large model is invoked to generate the first illuminance prediction result; The first illuminance prediction result is adjusted based on the historical energy consumption information to obtain the second illuminance prediction result; Determine the first difference information between the first illuminance prediction result and the second illuminance prediction result; The target illuminance is generated based on the first distinguishing information, the comprehensive features, and the environmental parameter information.

[0104] In one optional implementation, generating the target illuminance based on the first distinguishing information, the integrated features, and the environmental parameter information includes: Self-attention calculation is performed based on the first difference information to obtain the second difference information; The second distinguishing information is fused into the comprehensive feature to obtain the target feature, and the second distinguishing information is fused into the environmental parameter information to obtain the target environmental parameter information; for example, the fusion can be achieved by feature vector concatenation, element-wise addition / multiplication, or gating mechanism; Based on the target features and the target environmental parameter information, the target illumination is generated by calling the large model.

[0105] In some examples, target features, target environmental parameters, and preset inference prompts can be input into a large model to obtain the target illuminance output by the large model. The inference prompts can be used to instruct the large model to infer based on the target features and target environmental parameters (or, combined features and environmental parameters) to generate the target illuminance (or the first illuminance prediction result).

[0106] In this embodiment, the artificial intelligence model is a large model, which has stronger contextual understanding and generalization performance, and is suitable for multi-source heterogeneous data fusion in complex industrial scenarios. Specifically, the management platform can call the large model to perform forward inference based on the comprehensive features and the environmental parameter information, and output a first illuminance prediction result. This result can be used to reflect the ideal illuminance level independently determined by the large model under the current equipment status and environmental conditions.

[0107] The management platform can further adjust the first illuminance prediction result based on the historical energy consumption information (e.g., through weighted averaging, bias correction, or rule constraints) to obtain a second illuminance prediction result. This adjustment process incorporates historical experience knowledge, which can suppress the risk of overfitting of large models in sparse regions of training data.

[0108] The management platform can determine a first distinguishing information between the first illuminance prediction result and the second illuminance prediction result. This distinguishing information quantifies the difference between "model reasoning" and "historical experience" and can be used to assess the reliability of the current decision.

[0109] In this embodiment, the comprehensive feature is obtained by attention calculation of the first frequency feature (fundamental frequency) and the second frequency feature (harmonics) extracted from the electrical parameter information, representing the current electrical operating state of the industrial and mining lamp light source (such as drive efficiency, current ripple, potential aging, etc.). The second distinguishing information is a weighted difference vector obtained by self-attention calculation of the "first distinguishing information" (i.e., the difference between the large model's predicted illuminance and historical experience illuminance), highlighting which dimensions of deviation are more critical (e.g., the model excessively increases brightness when there is low traffic). Thus, by fusing the second distinguishing information into the comprehensive feature to obtain the target feature, the electrical state feature can perceive the deviation between the current model decision and historical reasonable behavior, thereby automatically suppressing unreasonable components during re-inference.

[0110] In this embodiment, environmental parameter information can reflect external lighting requirements. Incorporating the second distinguishing information means that the understanding of the environmental context must also be corrected for deviations, so that the environmental parameters are no longer static inputs, but rather dynamic contexts validated through experience, thus improving the ability to judge actual lighting needs.

[0111] In summary, the final input to the large model is no longer the original features, but the enhanced features (target features and target environment parameter information) after difference feedback correction. This is equivalent to allowing the large model to perform a second inference, but this time it is done under the premise of "knowing where the previous one might have gone wrong". This can improve the reliability and accuracy of the second inference, so as to avoid the blindness of single forward inference. In high safety requirements such as industrial and mining scenarios, it can significantly reduce the risk of mis-adjustment of light.

[0112] Secondly, embodiments of this application provide an industrial and mining lamp system based on power line carrier single-lamp control technology, comprising: The industrial and mining lamp based on power line carrier single-lamp control technology as described in any one of the first aspects above; and Management platform.

[0113] In summary, the embodiments of this application have at least the following beneficial effects: According to the embodiments of this application, an industrial and mining lamp based on power line carrier single-lamp control technology includes: an industrial and mining lamp light source; a power line carrier communication network; and a control module electrically connected to the industrial and mining lamp light source and communicating with a management platform through the power line carrier communication network. The management platform is configured to: acquire electrical parameter information and environmental parameter information corresponding to the industrial and mining lamp light source; generate a target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database; and send a control command generated according to the target illuminance to the control module. The control command instructs the control module to perform dimming control on the industrial and mining lamp light source according to the target illuminance. In this way, the electrical parameter information and environmental parameter information of the industrial and mining lamp light source can be comprehensively considered, combined with the historical energy consumption database, to generate a target illuminance adapted to the actual situation of the industrial and mining lamp through an artificial intelligence model, thereby achieving dynamic dimming control of a single industrial and mining lamp light source, reducing power consumption and improving energy efficiency.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware platforms, or it can be implemented entirely by hardware. Based on this understanding, all or part of the technical solutions of this application that contribute to the background technology can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0115] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A mining lamp based on power line carrier single-lamp control technology, characterized in that, The industrial and mining lamp includes: Industrial and mining lamp light sources; Power line carrier communication networks; and A control module is electrically connected to the industrial and mining lamp light source and communicates with a management platform via the power line carrier communication network. The management platform is configured to: acquire electrical parameter information and environmental parameter information corresponding to the industrial and mining lamp light source; generate a target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database; and send a control command generated according to the target illuminance to the control module, wherein the control command is used to instruct the control module to perform dimming control on the industrial and mining lamp light source according to the target illuminance.

2. The industrial and mining lamp according to claim 1, characterized in that, The control module includes: The main controller, which controls the industrial lamp light source, periodically sends heartbeat packets to the backup controller; and The backup controller is used to determine whether the number of heartbeat packets received within the most recent preset time period has reached the target number. If it has not reached the target number, it determines that there is a control fault and controls the mining lamp light source to switch from being controlled by the main controller to being controlled by the backup controller. When the control fault is detected to be eliminated, it controls the mining lamp light source to switch from being controlled by the backup controller to being controlled by the main controller.

3. The industrial and mining lamp according to claim 1, characterized in that, The power line carrier communication network includes a phase-coupling module, which includes: Three-phase power lines; Multiple coupling transformers, each corresponding to a controller in the control module; and Multiple impedance matching networks, each of which corresponds to a controller in the control module; In this control module, each controller is sequentially connected to one or more phases of the three-phase power line through the impedance matching network and coupling transformer corresponding to that controller.

4. The industrial and mining lamp according to claim 3, characterized in that, Each of the impedance matching networks includes: Variable capacitor array; and Variable inductor array; The impedance of each impedance matching network is determined using an improved particle swarm optimization algorithm. The particle position of the improved particle swarm optimization algorithm is determined by the capacitance value of the variable capacitor array and the inductance value of the variable inductor array. The fitness function of the improved particle swarm optimization algorithm is determined based on the impedance of each impedance matching network and the impedance of the electric field line to which each impedance matching network is electrically connected.

5. The industrial and mining lamp according to claim 3, characterized in that, Each of the plurality of coupling transformers has a common-mode choke connected in series with the power line to which it is electrically connected.

6. The industrial and mining lamp according to claim 3, characterized in that, Each of the plurality of coupling transformers is connected in series with the power line to which it is electrically connected, and a differential amplifier circuit is connected in series.

7. The industrial and mining lamp according to claim 1, characterized in that, The artificial intelligence model is a Long Short-Term Memory (LSTM) neural network model. The step of generating target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. The electrical parameter information, the environmental parameter information, and the historical energy consumption information are input into the LSTM neural network model to obtain the target illuminance output by the LSTM neural network model.

8. The industrial lamp according to claim 1, characterized in that, The step of generating target illuminance by calling an artificial intelligence model based on the electrical parameter information, the environmental parameter information, and a preset historical energy consumption database includes: The electrical parameter information is used to extract a first frequency feature and a second frequency feature. Attention is calculated based on the first frequency feature and the second frequency feature to obtain a comprehensive feature. The first frequency corresponding to the first frequency feature is the driving frequency corresponding to the industrial and mining lamp light source, and the second frequency corresponding to the second frequency feature is an integer multiple of the driving frequency. Based on the comprehensive features, the environmental parameter information, and the historical energy consumption database, an artificial intelligence model is invoked to generate the target illuminance.

9. The industrial and mining lamp according to claim 8, characterized in that, The artificial intelligence model is a large-scale model; The step of generating target illuminance by calling an artificial intelligence model based on the comprehensive features, the environmental parameter information, and the historical energy consumption database includes: Historical energy consumption information matching the environmental parameter information is obtained from the historical energy consumption database. Based on the comprehensive features and the environmental parameter information, the large model is invoked to generate the first illuminance prediction result; The first illuminance prediction result is adjusted based on the historical energy consumption information to obtain the second illuminance prediction result; Determine the first difference information between the first illuminance prediction result and the second illuminance prediction result; The target illuminance is generated based on the first distinguishing information, the comprehensive features, and the environmental parameter information.

10. The industrial and mining lamp according to claim 9, characterized in that, The step of generating the target illuminance based on the first difference information, the comprehensive features, and the environmental parameter information includes: Self-attention calculation is performed based on the first difference information to obtain the second difference information; The second distinguishing information is fused into the comprehensive feature to obtain the target feature, and the second distinguishing information is fused into the environmental parameter information to obtain the target environmental parameter information; Based on the target features and the target environmental parameter information, the target illumination is generated by calling the large model.

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