AMI driving method, circuit, device and controller based on high-speed communication

Abstract

The embodiment of the present application provides an AMI driving method, circuit, device and controller based on high-speed communication. The method is applied to the controller of the AMI control circuit, and the method includes: obtaining a remote device address corresponding to the received measurement data; sending the measurement data to an encoder to encode the measurement data according to a preset AMI encoding rule and output AMI encoding information; generating target parameter information according to the node load information and communication parameter information of each communication node; sending the AMI encoding information, the target parameter information and the modulation mode to a modulator to control the modulator to modulate the AMI encoding information into a high-speed communication signal; sending the high-speed communication signal and the remote device address to a driver to generate an AMI control signal; determining a target communication node among multiple communication nodes to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, so as to complete the control of the remote device.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to an AMI driving method, circuit, device and controller based on high-speed communication. Background Art

[0002] Advanced Metering Infrastructure (Alternate Mark Inversion, AMI) coding is a typical bipolar code, and the width of each rectangular pulse of the AMI bipolar code is fixed. It can be used in a bus system such as the Home Bus System (HBS) that controls and manages various communications, home appliances, and security equipment in a residence.

[0003] In the existing AMI-based control method, the signal is modulated and demodulated between the MCU (MicroControlUnit) and the AMI control circuit so that only a single rectangular pulse width exists in the transmitted AMI code to complete the control of multiple devices such as in the HBS system, thereby realizing a whole-house smart ecosystem.

[0004] The scheme of using auxiliary circuits to modulate and demodulate signals often causes signal errors due to certain delays and interferences caused by the electronic devices of the auxiliary circuits themselves, resulting in unsatisfactory control effects of existing AMI driving methods.

[0005] Therefore, it is urgent to design a technical solution to solve the above technical problems. Summary of the invention

[0006] The embodiments of the present application provide an AMI driving method, circuit, device and controller based on high-speed communication, which can solve the problem that the control effect of the existing AMI driving method is not ideal due to the delay and interference of the electronic components of the auxiliary circuit itself to the signal, which often causes signal errors. The method provided can realize high-speed and accurate transmission of AMI control signals.

[0007] In a first aspect, an embodiment of the present application provides an AMI driving method based on high-speed communication, the method is used to control multiple remote devices; the method is applied to a controller of an AMI control circuit, the AMI control circuit also includes an encoder, a modulator, a driver and multiple communication nodes; the method includes:

[0008] When receiving measurement data sent by an external sensor, obtaining a remote device address corresponding to the measurement data;

[0009] Send the measurement data to the encoder to control the encoder to encode the measurement data according to the preset AMI encoding rules and output AMI encoding information;

[0010] Acquire node load information and communication parameter information of multiple communication nodes, and generate target parameter information according to the node load information and communication parameter information of each communication node; the communication parameter information includes at least a communication rate and a driving level;

[0011] Sending the AMI coding information, target parameter information and modulation mode to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode;

[0012] Sending the high-speed communication signal and the remote device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal;

[0013] The operating status information of each communication node is obtained, and a target communication node is determined among multiple communication nodes according to the operating status information, so as to control the target communication node to send an AMI control signal to the remote device according to the remote device address under the target parameter information, thereby completing the control of the remote device.

[0014] In some embodiments, generating target parameter information based on node load information and communication parameter information of each communication node includes: obtaining the encoding information size corresponding to the AMI encoding information; inputting the encoding information size, node load information and communication parameter information into a pre-trained load prediction model, the load prediction model outputting a transmission prediction result corresponding to the communication parameter information; the transmission prediction result includes at least processing delay and transmission error rate; and generating the target parameter information based on the transmission prediction result and the communication parameter information.

[0015] Exemplarily, the load prediction model includes a supervised learning model, which is any one of a linear regression model or a support vector machine model; before the encoding information size, node load information and communication parameter information are input into the pre-trained load prediction model, it also includes: obtaining historical AMI control records, the historical AMI control records include historical node load information, historical communication rate, historical information size, historical drive level and historical transmission results; the historical transmission results at least include historical processing delay and historical transmission error rate; the historical node load information, historical communication rate and historical information size are input into the supervised learning model to be trained, and the supervised learning model outputs historical predicted processing delay and historical predicted transmission error rate; the training of the supervised learning model is completed based on the cross-validation method according to the historical processing delay history and predicted processing delay, historical transmission error rate and historical predicted transmission error rate.

[0016] Exemplarily, the target parameter information includes a target communication rate and a target drive level; generating the target parameter information according to the node load information and the communication parameter information of each communication node includes: obtaining a maximum tolerable error rate and a target processing delay; obtaining the target communication rate according to the maximum tolerable error rate, the communication rate, and the transmission error rate; obtaining the target drive level according to the target processing delay, the drive level, and the processing delay; wherein the expression corresponding to the target communication rate includes:

[0017]

[0018] Among them, the S target is the target communication rate, S current is the communication rate, E pred is the transmission error rate, E max is the maximum tolerable error rate, α1 is the first adjustment coefficient; wherein the expression corresponding to the target driving level includes:

[0019] V target =V current +α2×(T pred -T target );

[0020] Among them, the V target is the target drive level, V current is the driving level, T pred is the processing delay, T target is the target processing delay and α2 is the second adjustment coefficient.

[0021] In some embodiments, before sending the AMi encoding information, target parameter information and modulation mode to the modulator, it also includes: acquiring a data category corresponding to the measurement data; determining priority information corresponding to the measurement data according to the data category; wherein the priority information includes at least a first priority, a second priority and a third priority, the first priority is higher than the second priority, and the second priority is higher than the third priority; determining the modulation mode corresponding to the measurement data according to the priority information; wherein the modulation mode corresponding to the measurement data of the first priority includes any one of 64-order quadrature amplitude modulation and 256-order quadrature amplitude modulation, the modulation mode corresponding to the measurement data of the second priority includes any one of 16-order quadrature amplitude modulation and 8-order quadrature amplitude modulation, and the modulation mode corresponding to the measurement data of the third priority includes any one of quadrature phase shift keying and binary phase shift keying.

[0022] Exemplarily, the data categories include control category, monitoring category, business category and non-real-time category; determining the priority information corresponding to the measurement data based on the data category includes: extracting features from the measurement data to obtain feature information corresponding to the measurement data; wherein the feature information includes at least data sensitivity and data packet size; determining the priority information based on the data category and the feature information.

[0023] In some embodiments, before controlling the target communication node to send the AMI control signal to the remote device according to the remote device address, it also includes: performing error detection on the AMI control signal according to a preset error detection algorithm to obtain an error detection result corresponding to the AMI control signal; the error detection algorithm includes any one of a cyclic redundancy check, a parity check and a hash check; if it is determined according to the error detection result that the AMI control signal has an error, error feedback information is generated; the error feedback information includes the error type existing in the AMI control signal and the data position corresponding to the error type; obtaining an error correction algorithm corresponding to the error type; the error correction algorithm includes at least any one of a forward error correction code and / or a convolutional code algorithm; and completing the update of the AMI control signal according to the error correction algorithm, data position and error type.

[0024] In a second aspect, an AMI control circuit is used to control multiple remote devices; it includes: an encoder, a modulator, a driver, a controller and multiple communication nodes;

[0025] Among them, the controller is used to obtain the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor; the controller sends the measurement data to the encoder to control the encoder to encode the measurement data according to the preset AMI encoding rules and output AMI encoding information; the controller obtains the node load information and communication parameter information of multiple communication nodes, and generates target parameter information according to the node load information and communication parameter information of each communication node; the communication parameter information at least includes the communication rate and the driving level; the controller sends the AMI encoding information, the target parameter information and the modulation method to the modulator to control the modulator to modulate the AMI encoding information into a high-speed communication signal according to the communication rate and the modulation method; the controller sends the high-speed communication signal and the remote device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal; the controller obtains the operating status information of each communication node, determines the target communication node among the multiple communication nodes according to the operating status information, and controls the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, thereby completing the control of the remote device.

[0026] The present application also provides an AMI driving device, which is used to control multiple remote devices; a controller applied to an AMI control circuit, wherein the AMI control circuit also includes an encoder, a modulator, a driver, and multiple communication nodes; including:

[0027] A signal acquisition module is used to obtain the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor;

[0028] The encoding output module is used to send the measurement data to the encoder to control the encoder to encode the measurement data according to the preset AMI encoding rules and output AMI encoding information;

[0029] A parameter updating module, used to obtain node load information and communication parameter information of multiple communication nodes, and generate target parameter information according to the node load information and communication parameter information of each communication node; the communication parameter information at least includes a communication rate and a driving level;

[0030] A signal modulation module, used to send AMI coding information, target parameter information and modulation mode to the modulator, so as to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode;

[0031] A driver generation module, used for sending a high-speed communication signal and a remote device address to a driver, so as to control the driver to generate an AMI control signal corresponding to the high-speed communication signal;

[0032] The control completion module is used to obtain the operating status information of each communication node, determine the target communication node among multiple communication nodes according to the operating status information, and control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information to complete the control of the remote device.

[0033] In a third aspect, the present application further provides a controller, comprising a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the AMI driving method as described in the first aspect is implemented.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the AMI driving method as described in the first aspect.

[0035] The embodiment of the present application provides an AMI driving method, circuit, device and controller based on high-speed communication, the method is used to control multiple remote devices; the method is applied to the controller of the AMI control circuit, and the AMI control circuit also includes an encoder, a modulator, a driver and multiple communication nodes; the method includes: when receiving measurement data sent by an external sensor, obtaining the remote device address corresponding to the measurement data; sending the measurement data to the encoder to control the encoder to encode the measurement data according to a preset AMI encoding rule and output AMI encoding information; obtaining node load information and communication parameter information of multiple communication nodes, and according to the node load information and communication parameter information of each communication node Generate target parameter information; the communication parameter information includes at least a communication rate and a driving level; send the AMI coding information, the target parameter information and the modulation method to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and the modulation method; send the high-speed communication signal and the remote device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal; obtain the operating status information of each communication node, determine the target communication node among the multiple communication nodes according to the operating status information, and control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, so as to complete the control of the remote device.

[0036] Furthermore, the provided method can, after receiving the measurement data sent by the external sensor and the remote device address corresponding to the measurement data, first perform AMI encoding on the measurement data and then determine the target parameter information of this transmission according to the node load and communication parameter information of the current multiple communication nodes. Then, by inputting the AMI encoding information, the target parameter information and the modulation mode into the modulator, the AMI encoding signal can be modulated into a high-speed communication signal, and finally the AMI control signal is generated through the driver to control the target node to send the AMI drive signal to the remote device, thereby completing the high-speed, real-time and accurate control of the remote device.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic block diagram of the structure of the AMI control circuit shown in the embodiment of the present application;

[0039] Figure 2 A schematic diagram of the process flow of the AMI driving method shown in the embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of an AMI drive device shown in an embodiment of the present application;

[0041] Figure 4 This is a schematic diagram of the structure of a controller shown in an embodiment of the present application.

[0042] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0043] The technical solution of the embodiment of the present application is introduced below.

[0044] Please refer to Figure 1 , Figure 1 This is a schematic block diagram of the structure of an AMI control circuit provided in an embodiment of the present application. Figure 1 As shown, the provided AMI control circuit 10 includes an encoder 11 , a modulator 12 , a driver 13 , a controller 14 and a plurality of communication nodes 15 .

[0045] The controller 14 is used to obtain the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor; the controller 14 sends the measurement data to the encoder 11 to control the encoder 11 to encode the measurement data according to the preset AMI encoding rules and output AMI encoding information; the controller 14 obtains the node load information and communication parameter information of the multiple communication nodes 15, and generates the target parameter information according to the node load information and communication parameter information of each communication node 15; the communication parameter information at least includes the communication rate and the driving level; the controller 14 converts the AMI encoding information, the target parameter information and the modulation information into a single data frame; the controller 14 generates the target parameter information according to the node load information and the communication parameter information of each communication node 15; the communication parameter information at least includes the communication rate and the driving level; the controller 14 generates the target parameter information according to the AMI encoding information, the target parameter information and the modulation information ... The controller 14 sends the high-speed communication signal and the remote device address to the driver 13 to control the driver 13 to generate an AMI control signal corresponding to the high-speed communication signal; the controller 14 obtains the operating status information of each communication node 15, and determines the target communication node among the multiple communication nodes 15 according to the operating status information, so as to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, so as to complete the control of the remote device.

[0046] Specifically, after receiving the measurement data sent by the external sensor and the remote device address corresponding to the measurement data, the AMI control circuit 10 first performs AMI encoding on the measurement data and then determines the target parameter information of this transmission according to the node load and communication parameter information of the current multiple communication nodes 15 according to the controller 14. Then, by inputting the AMI encoding information, the target parameter information and the modulation mode into the modulator 12, the AMI encoding signal can be modulated into a high-speed communication signal, and finally the AMI control signal is generated through the driver to control the target node to send the AMI drive signal to the remote device, thereby completing the high-speed, real-time and accurate control of the remote device.

[0047] Among them, the remote devices can be multiple devices in a factory or multiple household appliances in a home, and thus remote control of any device can be achieved based on sensors arranged in the scene. The embodiment of the present application does not limit the application scenario of the provided AMI control circuit 10.

[0048] In some embodiments, the controller 14 is a microcontroller unit (MCU), such as an MCU equipped with a CotexM0 core, and the MCU can generate and transmit the measurement signal to the AMI control signal.

[0049] It should be noted that, in some embodiments, discrete components and logic gate devices are provided in the controller 14, and the functional implementation of the provided method can be achieved through the combination design of discrete components and logic gate devices.

[0050] In some embodiments, the distance between the provided AMI control circuit 10 and the remote device is within 1 km, and the number of communication nodes 15 is 256. Thus, ultra-long-distance multi-node communication is achieved, that is, up to 256 devices can be controlled simultaneously.

[0051] Please refer to Figure 2 , Figure 2 The flowchart of an AMI driving method provided in an embodiment of the present application is shown in FIG. The AMI driving method in an embodiment of the present application can be applied to a controller of an AMI control circuit provided in any embodiment of the present application, and the controller can be an MCU. Figure 2 As shown, the AMI driving method of this embodiment includes steps S101 to S106, which are described in detail as follows:

[0052] Step S101, when receiving measurement data sent by an external sensor, obtaining a remote device address corresponding to the measurement data.

[0053] Specifically, when any type of external sensor in any scenario transmits measurement data to the controller, the controller first needs to identify the target device corresponding to the data. The purpose of this step is to obtain the address of the remote device associated with these measurement data so that the information can be correctly sent to the device in the subsequent steps.

[0054] Step S102: sending the measurement data to the encoder to control the encoder to encode the measurement data according to a preset AMI encoding rule and output AMI encoding information.

[0055] Specifically, after determining the remote device address corresponding to the measurement data, the controller transmits the measurement data to the encoder. The encoder converts the measurement data into a specific encoding format according to the pre-set AMI (Advanced Metering Infrastructure) encoding rules for subsequent communication and processing.

[0056] Among them, the AMI encoding rules refer to a series of predefined standards and protocols followed by the encoder when encoding the measurement data. These rules ensure that the data can be accurately and efficiently transmitted and decoded in the AMI network. The AMI encoding rules provided in the embodiment of the present application include the following:

[0057] 1. Data compression and formatting: AMI encoding rules usually specify how to compress and format measurement data. Due to transmission bandwidth limitations, the encoder may need to compress large amounts of measurement data into smaller data packets. The encoding rules specify the compression algorithm and define the format of the data, such as byte order, identifier arrangement, etc., to ensure that the data can be correctly restored when decoded.

[0058] 2. Data segmentation and packaging: AMI encoding rules also specify how to divide the measurement data into multiple parts for packaging. Especially when the amount of data is large, the encoder needs to divide the data into multiple small packets so that they can be transmitted one by one in the network. The encoding rules define the structure of each data packet, including header information, data part, and tail information, etc., to ensure that these data packets can be reassembled into complete data at the receiving end.

[0059] 3. Encryption and security: To protect the privacy and security of measurement data, AMI encoding rules usually include provisions for data encryption. The encoder may need to use a specific encryption algorithm (such as AES, DES, etc.) to encrypt the data. The encoding rules will specify the specific steps for data encryption, as well as the generation, distribution and management of keys to prevent unauthorized access and data tampering.

[0060] 4. Timestamp and synchronization: To ensure the timeliness of data, AMI encoding rules may require adding timestamps during the encoding process. The encoder needs to add time information according to the rules so that the receiver can sort and synchronize data according to the timestamp to ensure the real-time and consistency of the data.

[0061] In summary, the AMI encoding rules set in the embodiments of the present application are the key to ensure that the measurement data can be reliably transmitted and processed in the AMI system. By following these rules, the encoder converts the original measurement data into encoding information that meets the requirements of the AMI network, ensuring the integrity, security and availability of the data.

[0062] Step S103, acquiring node load information and communication parameter information of multiple communication nodes, and generating target parameter information according to the node load information and communication parameter information of each communication node; the communication parameter information at least includes a communication rate and a driving level.

[0063] Specifically, the controller needs to obtain the current load information and communication parameters (such as communication rate and drive level) from multiple communication nodes of the AMI control circuit. Based on this information, the controller will generate a set of optimized target parameter information to determine the best communication method to ensure the efficiency and reliability of data transmission.

[0064] In some embodiments, generating target parameter information based on node load information and communication parameter information of each communication node includes: obtaining the encoding information size corresponding to the AMI encoding information; inputting the encoding information size, node load information and communication parameter information into a pre-trained load prediction model, the load prediction model outputting a transmission prediction result corresponding to the communication parameter information; the transmission prediction result includes at least processing delay and transmission error rate; and generating the target parameter information based on the transmission prediction result and the communication parameter information.

[0065] In this embodiment, the controller generates target parameter information through the following steps to ensure that the communication node can efficiently and reliably transmit the AMI coded information: the controller first obtains the size of the AMI coded information to be transmitted. The coded information size refers to the number of bytes or bits of the data packet. This is to evaluate the resources required during the data transmission process, as well as the possible delays and error rates. Then, the controller inputs the size of the coded information, the load information of the communication node, and the communication parameter information (such as communication rate, drive level, etc.) into a pre-trained load prediction model. This model can predict the performance that may occur when the communication node processes and transmits this data under current conditions through machine learning or other algorithms. The load prediction model outputs a set of prediction results based on the input information, which include at least two key indicators: processing delay and transmission error rate. Finally, the controller generates target parameter information based on the transmission prediction results and the communication parameter information. The target parameter information is key data for optimizing the performance of the communication node, including possible operations such as adjusting the communication rate, modifying the drive level, or selecting other communication nodes to ensure that the delay in the data transmission process is minimized and the error rate is reduced to an acceptable range.

[0066] The processing delay refers to the time required for a communication node to complete data processing and be ready for transmission after receiving the data. The transmission error rate refers to the proportion of errors that may occur during data transmission. This indicator reflects the reliability of the data.

[0067] Through the above embodiments, the controller can dynamically adjust the communication parameters to adapt to the current network environment and load conditions, ensuring that the AMI coded information can be reliably and efficiently transmitted in a complex network environment. This method enhances the adaptive ability of the communication node, improves the response speed of the overall method and the accuracy of data transmission.

[0068] Exemplarily, the load prediction model includes a supervised learning model, which is any one of a linear regression model or a support vector machine model; before the encoding information size, node load information and communication parameter information are input into the pre-trained load prediction model, it also includes: obtaining historical AMI control records, the historical AMI control records include historical node load information, historical communication rate, historical information size, historical drive level and historical transmission results; the historical transmission results at least include historical processing delay and historical transmission error rate; the historical node load information, historical communication rate and historical information size are input into the supervised learning model to be trained, and the supervised learning model outputs historical predicted processing delay and historical predicted transmission error rate; the training of the supervised learning model is completed based on the cross-validation method according to the historical processing delay history and predicted processing delay, historical transmission error rate and historical predicted transmission error rate.

[0069] Furthermore, the provided method can utilize the supervised learning model to improve the accuracy and applicability of the load prediction model, thereby better generating target parameter information. The linear regression model is used to predict the linear relationship between one or more independent variables (such as node load information, communication rate, etc.) and dependent variables (such as processing delay, transmission error rate). The support vector machine (SVM) model is a more complex model that can handle linear and nonlinear prediction problems, and is particularly suitable for processing high-dimensional data and finding the best classification boundary or prediction function. Therefore, the controller can intelligently select the corresponding supervised learning model type according to the complexity of the data.

[0070] Before inputting the current encoding information size, node load information, and communication parameter information into the pre-trained load prediction model, the controller needs to first obtain historical AMI control records. These historical records include: Historical node load information: the load conditions of communication nodes in different time periods in the past Historical communication rate: the rate used for past communications. Historical information size: the size of data packets transmitted in the past. Historical drive level: the drive level used during communication. Historical transmission results: including historical processing delays and historical transmission error rates, reflecting the actual effects of past transmissions. The controller inputs the above historical data into the supervised learning model to be trained. Based on these input data, the model outputs the corresponding historical predicted processing delays and historical predicted transmission error rates.

[0071] Cross-validation is a commonly used model evaluation technique that divides the data into multiple subsets, trains and tests the model on different subsets to ensure that the model has good generalization ability on unseen data. Through the cross-validation method, the model will compare these historical prediction results with the actual historical processing delay and historical transmission error rate. The purpose of this process is to optimize the model parameters so that the model can more accurately predict current and future load conditions. The trained and verified supervised learning model will be used for current load prediction. By inputting the current encoding information size, node load information and communication parameter information, the model can output the prediction results of the current processing delay and transmission error rate. These prediction results will serve as the basis for generating target parameter information, helping the system to better adjust communication parameters and optimize the efficiency and reliability of data transmission.

[0072] By introducing a supervised learning model of historical data, the accuracy and robustness of the load prediction model are enhanced. By learning from historical data and improving the prediction ability, the system can more accurately predict the current and future communication loads, thereby optimizing the transmission process of AMI control signals. This approach not only improves the prediction performance of the model, but also improves the adaptability of the entire system in a dynamic network environment.

[0073] Exemplarily, the target parameter information includes a target communication rate and a target drive level; generating the target parameter information according to the node load information and the communication parameter information of each communication node includes: obtaining a maximum tolerable error rate and a target processing delay; obtaining the target communication rate according to the maximum tolerable error rate, the communication rate, and the transmission error rate; obtaining the target drive level according to the target processing delay, the drive level, and the processing delay; wherein the expression corresponding to the target communication rate includes:

[0074]

[0075] Among them, the S target is the target communication rate, S current is the communication rate, E pred is the transmission error rate, E max is the maximum tolerable error rate, α1 is the first adjustment coefficient; wherein the expression corresponding to the target driving level includes:

[0076] V target =V current +α2×(T pred -T target );

[0077] Among them, the V target is the target drive level, V current is the driving level, T pred is the processing delay, T target is the target processing delay and α2 is the second adjustment coefficient.

[0078] Then, by calculating the target communication rate and target drive level, the performance parameters of the communication nodes are dynamically adjusted to optimize data transmission under different network environments. The reasonable selection of the adjustment coefficient is crucial to balancing the transmission rate and reliability, processing speed and energy consumption. In this way, the system can better adapt to load changes and environmental conditions, ensuring the efficiency and quality of data transmission.

[0079] The target communication rate and target drive level are generated by the load prediction results to optimize the performance of the communication node. target Refers to the rate at which communication nodes should transmit data to ensure efficient and reliable data transmission. Target drive level V target Refers to the driving voltage level that the communication node should adopt during data transmission to ensure the speed and quality of data processing. Maximum Tolerable Error Rate E max It refers to the maximum error rate that the system can accept during transmission. In actual operation, the system needs to maximize the communication rate while ensuring data reliability. Target processing delay T targetIt refers to the expected data processing time. The target processing delay is to ensure the timeliness and efficiency of data processing.

[0080] The expression corresponding to the target communication rate adjusts the current communication rate to achieve the desired transmission reliability. When the predicted transmission error rate is close to or exceeds the maximum tolerance error rate, the target communication rate will be reduced accordingly to reduce the probability of errors. Conversely, if the transmission error rate is low, the communication rate can be appropriately increased to improve transmission efficiency.

[0081] The expression corresponding to the target drive level optimizes the data processing time by adjusting the current drive level. If the predicted processing delay is greater than the target processing delay, the drive level needs to be increased to speed up data processing. Conversely, if the predicted processing delay is shorter, the drive level can be appropriately reduced to reduce energy consumption and possible overdriving.

[0082] It should be noted that the communication rate and the driving level may be the average values ​​of the driving levels and the communication rates of multiple communication nodes, or may be the values ​​corresponding to a random communication node, and the embodiments of the present application do not limit this.

[0083] The first adjustment coefficient determines the sensitivity of the target communication rate to changes in the predicted transmission error rate. If the first adjustment coefficient is large, it means that it is very sensitive to changes in the error rate, that is, when the error rate increases slightly, the communication rate will be greatly reduced. This is suitable for scenarios with extremely high requirements for transmission reliability. If the first adjustment coefficient is small, the system reacts more gently to changes in the error rate, which is suitable for scenarios with high requirements for the rate and can tolerate a certain error rate.

[0084] The second adjustment factor affects how much the target drive level adjusts to the predicted processing delay. If the factor is large, the system will quickly adjust the drive level when the predicted processing delay differs greatly from the target processing delay, in order to respond quickly. A smaller factor means that the adjustment to the processing delay is slower and more cautious, which is suitable for scenarios where the processing delay does not fluctuate much.

[0085] Step S104, sending the AMI coding information, target parameter information and modulation mode to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode.

[0086] Specifically, the controller transmits the AMI coding information and target parameter information generated in the above steps to the modulator, and instructs the modulator to convert this information into a high-speed communication signal using a specific modulation method. The purpose of this step is to convert the data into a signal form suitable for high-speed transmission in the network, so as to improve the speed of the provided method.

[0087] In some embodiments, before sending the AMI coding information, target parameter information and modulation mode to the modulator, it also includes: acquiring a data category corresponding to the measurement data; determining priority information corresponding to the measurement data according to the data category; wherein the priority information includes at least a first priority, a second priority and a third priority, the first priority is higher than the second priority, and the second priority is higher than the third priority; determining the modulation mode corresponding to the measurement data according to the priority information; wherein the modulation mode corresponding to the measurement data of the first priority includes any one of 64-order quadrature amplitude modulation and 256-order quadrature amplitude modulation, the modulation mode corresponding to the measurement data of the second priority includes any one of 16-order quadrature amplitude modulation and 8-order quadrature amplitude modulation, and the modulation mode corresponding to the measurement data of the third priority includes any one of quadrature phase shift keying and binary phase shift keying.

[0088] Before data transmission, how to select the appropriate modulation method according to the category and priority information of the measured data to optimize the efficiency and reliability of data transmission. Before data transmission, the controller will first obtain the data category corresponding to the measured data that needs to be transmitted. The data category may be distinguished according to the importance, real-time or other characteristics of the data, such as control data, status data, alarm data, etc. According to the data category, the controller will determine the priority information of the measured data. The priority information is used to reflect the urgency of data transmission and the requirements for transmission reliability. The priority is divided into three levels: First priority: the highest priority, usually used for critical data that requires high reliability and high transmission rate. Second priority: medium priority, suitable for data with higher transmission requirements but not as high as the first priority. Third priority: the lowest priority, used for general data with lower transmission rate and reliability requirements.

[0089] Finally, the controller selects appropriate modulation methods for different priority measurement data based on the priority information to achieve a balance between data transmission efficiency and reliability: First priority modulation methods: including 64-order quadrature amplitude modulation (64-QAM) and 256-order quadrature amplitude modulation (256-QAM). These modulation methods can provide higher data transmission rates under higher signal-to-noise ratio conditions, and are suitable for critical data with extremely high data transmission rate requirements. Second priority modulation methods: including 16-order quadrature amplitude modulation (16-QAM) and 8-order quadrature amplitude modulation (8-QAM). These modulation methods achieve a good balance between transmission rate and signal-to-noise ratio, and are suitable for medium-priority data that requires reliable but not necessarily the highest rate transmission. Third priority modulation methods: including quadrature phase shift keying (QPSK) and binary phase shift keying (BPSK). These modulation methods can still maintain high transmission reliability in a lower signal-to-noise ratio environment, but the data transmission rate is lower, and are suitable for data that does not require high rate but still has certain requirements for reliability.

[0090] Among them, the first priority data selects higher-order QAM (such as 64-QAM and 256-QAM) because the transmission rate and data reliability are crucial. These modulation methods can achieve higher bit rates. The second priority data selects medium-order QAM (such as 16-QAM and 8-QAM). These modulation methods achieve a good balance between rate and error rate and are suitable for data with moderate reliability requirements. The third priority data: select PSK methods (such as QPSK and BPSK). These modulation methods can still guarantee a low error rate under harsh channel conditions and are ideal for low-priority data. By selecting a suitable modulation method based on the category and priority information of the measured data before data transmission, the efficiency and adaptability of the communication system can be significantly improved. By selecting a high-order modulation method for high-priority data, efficient and reliable data transmission can be achieved; by selecting a low-order modulation method for low-priority data, the basic reliability of data transmission can be guaranteed while saving transmission resources. This method can ensure that the system achieves the best data transmission performance under different load and environmental conditions.

[0091] Exemplarily, the data categories include control category, monitoring category, business category and non-real-time category; determining the priority information corresponding to the measurement data based on the data category includes: extracting features from the measurement data to obtain feature information corresponding to the measurement data; wherein the feature information includes at least data sensitivity and data packet size; determining the priority information based on the data category and the feature information.

[0092] By further expanding the process of prioritizing measurement data, this paper describes in detail how to determine the priority of measurement data based on data category and feature information. The control category, monitoring category, business category, and non-real-time category involved are described separately:

[0093] Control data: Control signals related to key system operations, such as device on / off commands or parameter adjustments. This type of data usually requires high real-time performance and high reliability.

[0094] Monitoring data: including system status, environmental parameters and other monitoring data, usually used for real-time monitoring and fault detection.

[0095] Business data: data related to normal business operations, such as user requests and service responses. Although important, the real-time requirements may be slightly lower than those of control data.

[0096] Non-real-time data: involves logs, statistical data, etc., which usually do not require high real-time performance and can be transmitted at a lower priority.

[0097] For the characteristic information of data, data sensitivity is used to reflect the importance of data to system operation or business. Highly sensitive data may have a significant impact on the security and stability of the system and require a higher priority. The size of the data packet affects the resource usage of the transmission. Larger data packets may require more bandwidth, and this factor will also be taken into account when determining the priority. The controller first extracts the characteristics of the measured data and extracts the characteristic information corresponding to the data category, such as data sensitivity and data packet size. Based on the category of the measured data and the extracted characteristic information, the system can determine the priority of the data. For example: control data generally has high data sensitivity, so it is usually assigned the first priority. Monitoring data may be assigned the second priority due to large data packets or low sensitivity. Business data is assigned the second or third priority according to specific business needs. Non-real-time data is usually assigned the lowest third priority because they have the lowest real-time requirements for the controller.

[0098] After determining the priority information, the controller will select the appropriate data modulation method based on the priority. Then, by introducing data categories and feature information, the division of priorities and the selection of modulation methods are further refined. By identifying the data category and extracting feature information, the controller can more accurately determine the priority of the data and select the most appropriate modulation method based on the priority, thereby optimizing the efficiency and reliability of data transmission. This method improves the flexibility and adaptability of the system in processing different types of data, ensuring that critical data is processed first, while data with low timeliness requirements makes reasonable use of system resources.

[0099] Step S105 , sending the high-speed communication signal and the remote device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal.

[0100] Specifically, the high-speed communication signal modulated by the modulator is sent to the driver together with the address of the target device. The driver generates a corresponding AMI control signal based on the received information and prepares to send it to the remote device to execute the corresponding operation or command.

[0101] It should be noted that the AMI control signal includes any one of a modulation mode control signal, a coding rate control signal, a power control signal, a frequency selection control signal, a time synchronization control signal, and a channel state information feedback signal. The AMI control signal type covers all aspects of the adaptive adjustment of the communication system, and the server can confirm it according to the data category mentioned in the above embodiment, which is not limited in the embodiment of the present application.

[0102] Step S106, obtaining the operating status information of each communication node, determining the target communication node among multiple communication nodes according to the operating status information, so as to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, thereby completing the control of the remote device.

[0103] Specifically, the controller monitors the operating status of each communication node and selects a most suitable target communication node based on the status information. This target communication node will use the target parameter information generated previously and send the AMI control signal to the corresponding remote device according to the remote device address, thereby achieving precise control of the remote device.

[0104] In some embodiments, before controlling the target communication node to send the AMI control signal to the remote device according to the remote device address, it also includes: performing error detection on the AMI control signal according to a preset error detection algorithm to obtain an error detection result corresponding to the AMI control signal; the error detection algorithm includes any one of a cyclic redundancy check, a parity check and a hash check; if it is determined according to the error detection result that the AMI control signal has an error, error feedback information is generated; the error feedback information includes the error type existing in the AMI control signal and the data position corresponding to the error type; obtaining an error correction algorithm corresponding to the error type; the error correction algorithm includes at least any one of a forward error correction code and / or a convolutional code algorithm; and completing the update of the AMI control signal according to the error correction algorithm, data position and error type.

[0105] During the communication process, the AMI control signal may be affected by noise, interference or other factors during transmission, resulting in data errors in the signal. In order to ensure the normal operation of the system, the controller needs to perform error detection before sending the AMI control signal to the remote device.

[0106] In this embodiment, the controller detects the AMI control signal according to a preset error detection algorithm to obtain information on whether the signal has errors. An error detection and correction mechanism is introduced to improve the reliability of the AMI control signal during transmission. The error detection algorithm provided is described as follows: Cyclic Redundancy Check (CRC): CRC is a commonly used error detection technology that generates a check code for the data and recalculates and compares the data at the receiving end to detect whether there are errors. Parity check: Parity check detects single-bit errors by adding a check bit to the data. The check bit is determined based on the number of "1"s in the data and is usually used for simple error detection. Hash check: Hash check generates a fixed-length hash value for the data to verify the integrity and correctness of the data. If the data changes during transmission, the hash value will also change, thereby detecting an error.

[0107] After error detection, if the error detection result indicates that there is an error in the AMI control signal, the controller will generate error feedback information. The error feedback information includes two parts: Error type: describes the specific type of error detected in the AMI control signal, such as bit flipping, data loss, etc. Data location: indicates the specific location of the erroneous data in the AMI control signal, which is convenient for subsequent error correction processing. The error correction algorithm provided is explained: Forward Error Correction Code (FEC): Forward error correction code is a technology that adds redundant data before data transmission, allowing the receiver to correct some errors without retransmitting data. Common FEC algorithms include Hamming code, Reed-Solomon code, etc. Convolutional code algorithm: Convolutional code is a technology that converts an input data stream into an encoded output stream. By memorizing the input data of the previous state, continuous errors can be effectively corrected. It is widely used in wireless communications and can achieve efficient decoding and error correction in combination with the Viterbi algorithm.

[0108] Based on the detected error type, data location and selected error correction algorithm, the controller system will update and correct the erroneous AMI control signal to ensure that the signal sent to the remote device is correct. Through the above error correction process, the errors generated during the transmission process can be corrected to the greatest extent, improving the reliability of communication and the integrity of data.

[0109] Furthermore, an error detection and correction mechanism is introduced to improve the reliability of AMI control signals during transmission. By using methods such as cyclic redundancy check, parity check or hash check, the system can effectively detect errors in the signal and correct them through error correction algorithms such as forward error correction code or convolutional code. This process not only ensures that remote devices can receive correct control signals, but also improves the robustness and adaptability of the system in complex communication environments.

[0110] In order to execute the AMI driving method corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 3 , Figure 3 The structure block diagram of an AMI driving device 200 provided in an embodiment of the present application is shown. The device is applied to a controller of an AMI control circuit provided in any embodiment of the present application. For ease of description, only the parts related to the present embodiment are shown. The AMI driving device 200 provided in an embodiment of the present application includes:

[0111] The signal acquisition module 201 is used to acquire the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor;

[0112] The encoding output module 202 is used to send the measurement data to the encoder to control the encoder to encode the measurement data according to a preset AMI encoding rule and output AMI encoding information;

[0113] The parameter updating module 203 is used to obtain node load information and communication parameter information of multiple communication nodes, and generate target parameter information according to the node load information and communication parameter information of each communication node; the communication parameter information at least includes a communication rate and a driving level;

[0114] The signal modulation module 204 is used to send the AMI coding information, target parameter information and modulation mode to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode;

[0115] A driver generating module 205, configured to send the high-speed communication signal and the remote device address to the driver, so as to control the driver to generate an AMI control signal corresponding to the high-speed communication signal;

[0116] The control completion module 206 is used to obtain the operating status information of each of the communication nodes, and determine the target communication node among the multiple communication nodes based on the operating status information, so as to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, so as to complete the control of the remote device.

[0117] The above-mentioned AMI driving device 200 can implement the AMI driving method of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment and will not be described in detail in this embodiment.

[0118] Figure 4 This is a schematic diagram of the structure of a controller provided in one embodiment of the present application. Figure 4 As shown, the controller 14 of this embodiment includes: at least one processor 140 ( Figure 4 Only one is shown in the figure), a memory 141, and a computer program 142 stored in the memory 141 and executable on the at least one processor 140, wherein the processor 140 implements the steps of any of the above method embodiments when executing the computer program 142.

[0119] The controller 14 may be a microcontroller mounted on a smart phone, a tablet computer, a desktop computer, or a cloud server. The controller may include, but is not limited to, a processor 140 and a memory 141. Those skilled in the art will appreciate that Figure 4 It is only an example of the controller 14 and does not constitute a limitation on the controller 14. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0120] The processor 140 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] In some embodiments, the memory 141 may be an internal storage unit of the controller 14, such as a hard disk or memory of the controller 14. In other embodiments, the memory 141 may also be an external storage device of the controller 14, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the controller 14. Further, the memory 141 may also include both an internal storage unit of the controller 14 and an external storage device. The memory 141 is used to store an operating system, an application program, a boot loader, data, and other programs, such as the program code of the computer program. The memory 141 may also be used to temporarily store data that has been output or is to be output.

[0122] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0123] An embodiment of the present application provides a computer program product. When the computer program product runs on a controller, the controller implements the steps in the above-mentioned method embodiments when executing the controller.

[0124] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An AMI driving method based on high-speed communication, characterized in that: The method is used to control a plurality of remote devices; the method is applied to a controller of an AMI control circuit, the AMI control circuit further comprising an encoder, a modulator, a driver and a plurality of communication nodes; the method comprises: When receiving measurement data sent by an external sensor, obtaining a remote device address corresponding to the measurement data; Sending the measurement data to the encoder to control the encoder to encode the measurement data according to a preset AMI encoding rule and output AMI encoding information; Obtain node load information and communication parameter information of multiple communication nodes, wherein the communication parameter information at least includes a communication rate and a driving level; generate target parameter information according to the node load information and communication parameter information of each communication node, including: obtaining the encoding information size corresponding to the AMI encoding information; inputting the encoding information size, the node load information and the communication parameter information into a pre-trained load prediction model, and the load prediction model outputs a transmission prediction result corresponding to the communication parameter information; the transmission prediction result at least includes a processing delay and a transmission error rate; generating target parameter information according to the transmission prediction result and the communication parameter information; or, obtaining a maximum tolerable error rate and a target processing delay; obtaining a target communication rate according to the maximum tolerable error rate, the communication rate and the transmission error rate; obtaining a target driving level according to the target processing delay, the driving level and the processing delay; wherein the expression corresponding to the target communication rate includes: Among them, S target is the target communication rate, S current is the communication rate, E pred is the transmission error rate, E max is the maximum tolerable error rate, α1 is the first adjustment coefficient; wherein the expression corresponding to the target driving level includes: V target =V current +α2×(T pred -T target ); Among them, V target is the target drive level, V current is the driving level, T pred To handle delay, T target is the target processing delay, α2 is the second adjustment coefficient; Sending the AMI coding information, target parameter information and modulation mode to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode; Sending the high-speed communication signal and the remote device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal; Acquire the operating status information of each of the communication nodes, and determine the target communication node among the multiple communication nodes according to the operating status information, so as to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, thereby completing the control of the remote device.

2. The method according to claim 1, characterized in that The load prediction model includes a supervised learning model, which is any one of a linear regression model and a support vector machine model; before inputting the encoding information size, the node load information and the communication parameter information into the pre-trained load prediction model, it also includes: Acquire historical AMI control records, wherein the historical AMI control records include historical node load information, historical communication rate, historical information size, historical drive level, and historical transmission results; the historical transmission results include at least historical processing delay and historical transmission error rate; Inputting the historical node load information, the historical communication rate and the historical information size into the supervised learning model to be trained, the supervised learning model outputting the historical predicted processing delay and the historical predicted transmission error rate; The cross-validation-based method completes the training of the supervised learning model according to the historical processing delay history and predicted processing delay, historical transmission error rate and historical predicted transmission error rate.

3. The method according to claim 1, characterized in that Before sending the AMI coding information, target parameter information and modulation mode to the modulator, the method further includes: Obtaining a data category corresponding to the measurement data; Determine priority information corresponding to the measurement data according to the data category; wherein the priority information includes at least a first priority, a second priority and a third priority, the first priority is higher than the second priority, and the second priority is higher than the third priority; Determine the modulation mode corresponding to the measurement data according to the priority information; wherein the modulation mode corresponding to the measurement data of the first priority includes any one of 64-order quadrature amplitude modulation and 256-order quadrature amplitude modulation, the modulation mode corresponding to the measurement data of the second priority includes any one of 16-order quadrature amplitude modulation and 8-order quadrature amplitude modulation, and the modulation mode corresponding to the measurement data of the third priority includes any one of quadrature phase shift keying and binary phase shift keying.

4. The method according to claim 3, characterized in that The data categories include control category, monitoring category, business category and non-real-time category; and determining the priority information corresponding to the measurement data according to the data category includes: Extracting features from the measurement data to obtain feature information corresponding to the measurement data; wherein the feature information includes at least data sensitivity and data packet size; The priority information is determined according to the data category and the feature information.

5. The method according to claim 1, characterized in that Before controlling the target communication node to send the AMI control signal to the remote device according to the remote device address, the method further includes: Performing error detection on the AMI control signal according to a preset error detection algorithm to obtain an error detection result corresponding to the AMI control signal; the error detection algorithm includes any one of a cyclic redundancy check, a parity check, and a hash check; If it is determined according to the error detection result that the AMI control signal has an error, error feedback information is generated; the error feedback information includes the error type of the AMI control signal and the data position corresponding to the error type; Obtaining an error correction algorithm corresponding to the error type; the error correction algorithm at least includes any one of a forward error correction code and / or a convolutional code algorithm; The updating of the AMI control signal is completed according to the error correction algorithm, data location and error type.

6. An AMI control circuit, characterized in that: The circuit is used to control multiple remote devices; it includes: an encoder, a modulator, a driver, a controller and multiple communication nodes; Wherein, the controller is used to obtain the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor; the controller sends the measurement data to the encoder to control the encoder to encode the measurement data according to the preset AMI encoding rule and output AMI encoding information; the controller obtains the node load information and communication parameter information of multiple communication nodes, and the communication parameter information at least includes the communication rate and the driving level; generates the target parameter information according to the node load information and the communication parameter information of each communication node, including: obtaining the encoding information size corresponding to the AMI encoding information; inputting the encoding information size, the node load information and the communication parameter information into the pre-trained load prediction model, and the load prediction model outputs the transmission prediction result corresponding to the communication parameter information; the transmission prediction result at least includes the processing delay and the transmission error rate; generates the target parameter information according to the transmission prediction result and the communication parameter information; or, obtains the maximum tolerable error rate and the target processing delay; obtains the target communication rate according to the maximum tolerable error rate, the communication rate and the transmission error rate; obtains the target driving level according to the target processing delay, the driving level and the processing delay; wherein the expression corresponding to the target communication rate includes: Among them, S target is the target communication rate, S current is the communication rate, E pred is the transmission error rate, E max is the maximum tolerable error rate, α1 is the first adjustment coefficient; wherein the expression corresponding to the target driving level includes: V target =V current +α2×(T pred -T target ); Among them, V target is the target drive level, V current is the driving level, T pred To handle delay, T target is the target processing delay, α2 is the second adjustment coefficient; The controller sends the AMI coding information, target parameter information and modulation mode to the modulator to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode; the controller sends the high-speed communication signal and the long-distance device address to the driver to control the driver to generate an AMI control signal corresponding to the high-speed communication signal; the controller obtains the operating status information of each of the communication nodes, determines the target communication node among the multiple communication nodes according to the operating status information, and controls the target communication node to send the AMI control signal to the long-distance device according to the long-distance device address under the target parameter information, thereby completing the control of the long-distance device.

7. An AMI driving device based on high-speed communication, characterized in that: The device is used to control multiple remote devices; a controller applied to an AMI control circuit, wherein the AMI control circuit also includes an encoder, a modulator, a driver and multiple communication nodes; and includes: A signal acquisition module, used to acquire the remote device address corresponding to the measurement data when receiving the measurement data sent by the external sensor; A coding output module, used for sending the measurement data to the encoder to control the encoder to encode the measurement data according to a preset AMI coding rule and output AMI coding information; A parameter updating module, used for obtaining node load information and communication parameter information of multiple communication nodes, wherein the communication parameter information at least includes a communication rate and a driving level; generating target parameter information according to the node load information and communication parameter information of each communication node, including: obtaining the encoding information size corresponding to the AMI encoding information; inputting the encoding information size, the node load information and the communication parameter information into a pre-trained load prediction model, and the load prediction model outputs a transmission prediction result corresponding to the communication parameter information; the transmission prediction result at least includes a processing delay and a transmission error rate; generating target parameter information according to the transmission prediction result and the communication parameter information; or, obtaining a maximum tolerable error rate and a target processing delay; obtaining a target communication rate according to the maximum tolerable error rate, the communication rate and the transmission error rate; obtaining a target driving level according to the target processing delay, the driving level and the processing delay; wherein the expression corresponding to the target communication rate includes: Among them, S target is the target communication rate, S current is the communication rate, E pred is the transmission error rate, E max is the maximum tolerable error rate, α1 is the first adjustment coefficient; wherein the expression corresponding to the target driving level includes: V target =V current +α2×(T pred -T target ); Among them, V target is the target drive level, V current is the driving level, T pred To handle delay, T target is the target processing delay, α2 is the second adjustment coefficient; A signal modulation module, used for sending the AMI coding information, target parameter information and modulation mode to the modulator, so as to control the modulator to modulate the AMI coding information into a high-speed communication signal according to the communication rate and modulation mode; A driver generating module, used for sending the high-speed communication signal and the remote device address to the driver, so as to control the driver to generate an AMI control signal corresponding to the high-speed communication signal; A control completion module is used to obtain the operating status information of each of the communication nodes, and determine the target communication node among the multiple communication nodes according to the operating status information, so as to control the target communication node to send the AMI control signal to the remote device according to the remote device address under the target parameter information, so as to complete the control of the remote device.

8. A controller, characterized in that: The controller includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 1 to 5 when executing the computer program.

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