A remote control method and system of an intelligent traditional Chinese medicine water purification device

Abstract

The present application relates to the technical field of remote control, in particular to a remote control method and system for intelligent traditional Chinese medicine water purification equipment, comprising the following steps: acquiring channel data, calculating packet loss and interval, screening abnormalities and judging stability, switching to a backup channel when the abnormality exceeds the threshold, adjusting the channel state, and acquiring channel switching state parameters. In the present application, channel data is dynamically monitored and packet loss is calculated, a new scheme automatically enables a backup channel when an abnormality occurs, ensuring the continuity of data transmission, timestamp analysis and data packet interval calculation help optimize data flow order, enhance timing consistency, improve the accurate execution of control commands, parameter optimization adjusts remote control instructions accurately by analyzing flow rate, temperature and pressure changes in real time, improves the synchronization and accuracy of system response, error accumulation analysis makes parameter adjustment more accurate, reduces the accumulation of long-term running deviation, and enhances the stability and efficiency of remote equipment operation.

Description

Technical Field

[0001] This invention relates to the field of remote control technology, and in particular to a remote control method and system for an intelligent water purification device for traditional Chinese medicine. Background Technology

[0002] The field of remote control technology encompasses various technical means for monitoring, managing, and operating remote devices or systems via communication networks. The core of this technology lies in achieving real-time monitoring of the controlled object's status, adjustment of operating parameters, and fault diagnosis through data transmission and command interaction between the control and controlled ends. Remote control is widely used in industrial automation, intelligent manufacturing, traffic management, and energy monitoring, with key technologies including remote communication protocols, data acquisition and transmission, control command issuance, and security protection. The development of this technology has promoted improved production efficiency and simplified equipment operation and maintenance, and is particularly significant in the cross-regional management and intelligent production of complex equipment.

[0003] The remote control method for intelligent water purification equipment for traditional Chinese medicine refers to the remote monitoring and control of the equipment's operating status during the water purification process. It utilizes network communication technology to remotely manage key process parameters such as start-up, shutdown, temperature adjustment, and flow control. This method encompasses real-time data acquisition, remote command issuance, operational status monitoring, and anomaly alarms. Specifically, sensors collect key parameters during the purification process, and the control terminal sends operation commands to the equipment control unit, achieving precise control of the purification process. Furthermore, the system analyzes data to determine the equipment's operating status and provides early warnings when anomalies occur, ensuring the stability and safety of the purification process.

[0004] Existing technologies lack fine-grained data analysis for channel monitoring, failing to calculate data loss rate and packet intervals in real time, leading to delayed channel stability assessments. When communication anomalies occur, failure to switch channels promptly may cause brief data interruptions, affecting the continuity of remote control. In data stream timing management, the lack of identification of time-skipped packets results in data stream time base deviations, affecting the timing consistency of remote commands. Equipment parameter optimization methods are based on static threshold adjustments, failing to fully consider trends in the time series and providing insufficient analysis of the impact of communication delays, potentially leading to low adaptability of control commands to actual operating conditions. Feedback mechanisms rely on single adjustments without establishing cumulative error calculations, resulting in delayed updates of compensation parameters. Over long-term system operation, parameter deviations may gradually accumulate, affecting the accuracy of remote control. Command issuance is not adjusted in conjunction with network latency fluctuations, resulting in poor time synchronization of command execution. Under high-precision control requirements, this may lead to response deviations, affecting the precise operation of remote equipment. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a remote control method and system for an intelligent water purification device for traditional Chinese medicine.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote control method for an intelligent water purification device for traditional Chinese medicine, comprising the following steps:

[0007] S1: Acquire channel data, calculate packet loss and interval, filter anomalies and determine stability. When the anomaly exceeds the threshold, switch to the backup channel, adjust the channel status, and acquire the channel switching status parameters.

[0008] S2: Based on the channel switching status parameters, extract the buffered data stream, calculate the time interval, filter skipped data packets, adjust the order, calculate the data stream time offset, and when the offset exceeds the threshold, correct the time base and obtain the time synchronization adjustment value.

[0009] S3: Based on the time synchronization adjustment value, extract equipment data, calculate the flow rate and temperature change rate, screen key parameters affecting communication delay, calculate the error correction amount, and optimize the target flow rate in combination with real-time feedback. Adjust the correction range according to flow rate, temperature, and pressure to obtain dynamic flow rate compensation value.

[0010] S4: Based on the dynamic flow rate compensation value, calculate the deviation between the feedback and correction values, call the timestamp to calculate the response time, filter abnormal data, calculate the flow rate correction amount, update the compensation parameters, and obtain the secondary flow rate compensation value.

[0011] S5: Based on the aforementioned secondary flow rate compensation value, collect network data, calculate latency and fluctuations, filter anomalies, calculate the latency change ratio, adjust the command time, and obtain a remote control processing adjustment scheme.

[0012] As a further aspect of the present invention, the channel switching status parameters include the number of lost data packets, the interval between adjacent data packets, the number of abnormal data packets, the channel stability status, the backup channel takeover status, and the channel switching mode. The time synchronization adjustment values ​​include the timestamp interval value, the time jump data packet, the data stream time offset, the time base correction value, and the buffered data stream order adjustment parameters. The dynamic flow rate compensation values ​​include the current flow rate error correction amount, the cumulative error value, the target flow rate optimization parameters, the flow rate correction amplitude, the temperature compensation parameters, and the pressure compensation parameters. The secondary flow rate compensation values ​​include the feedback flow rate, the feedback temperature, the feedback pressure, the correction value deviation, the response time, the flow rate adjustment correction amount, and the compensation parameter update value. The remote control processing adjustment scheme includes the data packet delay, the delay fluctuation range, the abnormal fluctuation data, the delay change ratio, and the command issuance time adjustment value.

[0013] As a further aspect of the present invention, the specific steps for acquiring channel data, calculating packet loss and interval, filtering anomalies and determining stability, switching to a backup channel when anomalies exceed a threshold, adjusting the channel state, and acquiring channel switching state parameters are as follows:

[0014] S101: Obtain the transmission data of the primary control channel and the backup channel, parse the timestamp, data length and sequence number of the data packets, count the number of data packets received on the channel, calculate the loss index, and obtain the data packet loss of the primary control channel and the backup channel.

[0015] S102: Based on the data packet loss amount of the backup channel, parse the data packet timestamp sequence, calculate the time interval between adjacent data packets, set an abnormal threshold, call the time interval data of the main control channel and the backup channel for comparison, filter out data packets that exceed the threshold, count the number of abnormal data packets, obtain the time distribution of abnormal data packets, and obtain the number of abnormal data packets and the time interval distribution.

[0016] S103: Invoke the distribution of the number and time interval of the abnormal data packets, compare it with the channel stability threshold, and if the abnormal limit is exceeded, trigger the backup channel takeover, adjust the channel switching state, obtain the switching parameters, and obtain the channel switching state parameters.

[0017] As a further aspect of the present invention, based on the channel switching state parameters, the buffered data stream is extracted, the time interval is calculated, skipped data packets are filtered, the order is adjusted, the data stream time offset is calculated, and when the offset exceeds the threshold, the time base is corrected. The specific steps for obtaining the time synchronization adjustment value are as follows:

[0018] S201: Obtain the channel switching status parameters, extract the buffered data stream, parse the data packet timestamp, calculate the data packet timestamp interval value, filter time-skip data packets, call the sequence number of the time-skip data packets to sort them, adjust the data packet order, and obtain the sorted data stream;

[0019] S202: Based on the sorted data stream, calculate the time difference between adjacent data packets, statistically analyze the changing trend of the time difference, analyze the change in time interval, calculate the time offset of the data stream, set a time offset threshold, perform threshold judgment on the time offset of the data stream, filter the time offset values ​​of data packets that exceed the threshold, statistically analyze the number of data packets that exceed the threshold, obtain the time range of data packets with excessive offset, and obtain the amount of data with excessive time offset.

[0020] S203: Call the amount of data that exceeds the time offset limit, correct the reference time of the data stream based on the time offset data, adjust the overall time axis of the data stream, calculate the time difference of the data stream before and after the adjustment, and obtain the time synchronization adjustment value.

[0021] As a further aspect of the present invention, based on the time synchronization adjustment value, the following specific steps are taken to extract equipment data, calculate the flow rate and temperature change rate, screen key parameters affecting communication delay, calculate the error correction amount, and optimize the target flow rate in conjunction with real-time feedback, adjusting the correction magnitude according to flow rate, temperature, and pressure to obtain the dynamic flow rate compensation value:

[0022] S301: Call the time synchronization adjustment value, extract device data, parse the time series of flow rate and temperature, calculate the rate of change of flow rate and temperature in the time series, call the rate of change data, filter the parameters that have the greatest impact on communication delay, and obtain the key influencing parameters.

[0023] S302: Based on the key influencing parameters, calculate the timestamp interval index, analyze the ratio of the parameter's impact on communication delay, set the ratio range, filter parameters whose ratios exceed the set range, calculate the current flow rate error correction amount, call real-time feedback data, accumulate the flow rate error correction amount, call the cumulative error value, optimize and adjust the target flow rate, and obtain the optimized target flow rate;

[0024] S303: Call the optimized target flow rate, adjust the correction range according to the flow rate, temperature, and pressure, analyze the trend of the adjusted flow rate change, calculate the difference between the corrected flow rate and the target flow rate, and obtain the dynamic flow rate compensation value.

[0025] As a further aspect of the present invention, the formula for calculating the dynamic flow velocity compensation value is specifically as follows:

[0026] ;

[0027] in, Represents the dynamic flow velocity compensation value. Represents the current flow rate. Represents the target flow rate. This represents the adjusted pressure value. This represents the adjusted temperature value. This represents the reference pressure value. This represents the reference temperature value.

[0028] As a further aspect of the present invention, the specific steps for calculating the deviation between the feedback and correction values ​​based on the dynamic flow velocity compensation value, calculating the response time using the timestamp, filtering abnormal data, calculating the flow velocity correction amount, updating the compensation parameters, and obtaining the secondary flow velocity compensation value are as follows:

[0029] S401: Call the dynamic flow rate compensation value, extract the flow rate, temperature and pressure data fed back from the device, analyze the difference between the feedback value and the correction value, calculate the deviation index between the feedback value and the correction value, call the instruction execution timestamp, calculate the instruction response time index, and obtain the feedback deviation and response time data;

[0030] S402: Based on the feedback deviation and response time data, set the deviation anomaly screening range, analyze the feedback deviation data, filter data that exceeds the set range, statistically analyze the distribution of abnormal data, call the filtered deviation anomaly data, calculate the current flow rate adjustment correction amount, adjust the compensation parameters according to the correction amount, and obtain the correction compensation parameters.

[0031] S403: Call the corrected compensation parameters, combine them with the real-time flow rate adjustment data, calculate the flow rate compensation value after secondary correction, analyze the stability of the adjusted flow rate, and obtain the secondary flow rate compensation value.

[0032] As a further aspect of the present invention, the formula for calculating the secondary flow velocity compensation value is as follows:

[0033] ;

[0034] in, This represents the secondary compensation value for the flow velocity. This represents the flow rate compensation value after one correction. This represents the correction and compensation parameters. This represents the difference between the current pressure and the reference pressure. Represents the temperature correction factor. Represents the flow velocity stability adjustment factor. Representing the The actual flow rate at each time point Representing the Adjusted flow rate at each time point This represents the total number of time points considered in the calculation.

[0035] As a further aspect of the present invention, based on the aforementioned secondary flow rate compensation value, the specific steps for collecting network data, calculating latency and fluctuations, filtering anomalies, calculating the latency change ratio, adjusting command time, and obtaining a remote control processing adjustment scheme are as follows:

[0036] S501: Call the flow rate secondary compensation value, collect the current network transmission data, parse the data packet timestamp, calculate the data packet transmission delay data, call the delay data, statistically analyze the delay changes in different time periods, and obtain the data packet delay value;

[0037] S502: Based on the data packet delay value, analyze the timestamp interval, analyze the delay fluctuation range, set the previous delay fluctuation range, filter data that exceeds the previous range, calculate the delay change ratio of the filtered data, call the ratio data, statistically analyze the delay fluctuation of the differential ratio interval, analyze the change trend of the delay ratio, and obtain the delay change ratio data.

[0038] S503: Call the delay change ratio data, calculate the instruction issuance time adjustment value, adjust the instruction issuance time, analyze the stability of the delay after adjustment, and obtain the remote control processing adjustment scheme.

[0039] A remote control system for an intelligent water purification device for traditional Chinese medicine includes:

[0040] The channel switching module acquires channel data, calculates packet loss and interval, filters anomalies and judges stability. When the anomaly exceeds the threshold, it switches to the backup channel and acquires the channel switching status parameters.

[0041] Based on the channel switching status parameters, the time synchronization adjustment module extracts the buffered data stream, calculates the time interval, filters skipped data packets, calculates the data stream time offset, and when the offset exceeds the threshold, corrects the time base and obtains the time synchronization adjustment value.

[0042] Based on the time synchronization adjustment value, the flow rate compensation module extracts equipment data, calculates the flow rate and temperature change rate, filters key parameters that affect communication delay, and optimizes the target flow rate in combination with real-time feedback. It adjusts the correction range according to flow rate, temperature, and pressure to obtain dynamic flow rate compensation value.

[0043] The flow velocity secondary compensation module calculates the deviation between the feedback and correction values ​​based on the dynamic flow velocity compensation value, calls the timestamp to calculate the response time, filters abnormal data, calculates the flow velocity correction amount, and obtains the flow velocity secondary compensation value.

[0044] Based on the secondary compensation value of the flow rate, the delay adjustment module collects network data, calculates delay and fluctuation, filters anomalies, calculates the delay change ratio, and obtains a remote control processing and adjustment scheme.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In this invention, by dynamically monitoring channel data and calculating packet loss, the new scheme automatically activates the backup channel when an anomaly occurs, ensuring the continuity of data transmission. Timestamp analysis and packet interval calculation help optimize the data flow sequence, enhance timing consistency, and improve the accurate execution of control commands. Parameter optimization, through real-time analysis of changes in flow rate, temperature, and pressure, precisely adjusts remote control commands, improving the synchronization and accuracy of system response. Error accumulation analysis makes parameter adjustment more precise, reduces the accumulation of deviations during long-term operation, and enhances the stability and efficiency of remote equipment operation. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the steps of the present invention;

[0049] Figure 2 This is a flowchart of steps S1 of the present invention;

[0050] Figure 3 This is a flowchart of steps S2 of the present invention;

[0051] Figure 4 This is a flowchart of steps S3 of the present invention;

[0052] Figure 5 This is a flowchart of step S4 of the present invention;

[0053] Figure 6 This is a flowchart of steps S5 of the present invention;

[0054] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0056] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0057] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0058] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0059] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0060] Please see Figure 1 A remote control method for an intelligent water purification device for traditional Chinese medicine includes the following steps:

[0061] S1: Obtain the transmission data of the primary control channel and the backup channel, calculate the number of lost data packets, call the timestamp to calculate the interval between adjacent data packets, filter abnormal data packets, judge the channel stability, and when the abnormality exceeds the threshold, trigger the backup channel to take over, adjust the channel switching status, and obtain the channel switching status parameters.

[0062] S2: Based on the channel switching status parameters, extract the buffered data stream, calculate the timestamp interval value, filter time jump data packets, adjust the data packet order, call the time difference of adjacent data packets to calculate the data stream time offset, and when the offset exceeds the threshold, perform time base correction and obtain the time synchronization adjustment value.

[0063] S3: Based on the time synchronization adjustment value, extract equipment data, calculate the rate of change of flow rate and temperature in the time series, filter the parameters that have the greatest impact on communication delay, call the timestamp to calculate the ratio of the parameter's impact on delay, filter parameters whose ratio exceeds the set range, calculate the current flow rate error correction amount, call real-time feedback data to calculate the cumulative error value, optimize the target flow rate of the control command, adjust the correction amplitude according to flow rate, temperature, and pressure, and obtain the dynamic flow rate compensation value;

[0064] S4: Based on the dynamic flow rate compensation value, extract the flow rate, temperature, and pressure fed back from the device, calculate the deviation between the feedback value and the correction value, call the instruction execution timestamp to calculate the response time, filter out abnormal deviation data, calculate the flow rate adjustment correction amount, update the compensation parameters, and obtain the secondary flow rate compensation value.

[0065] S5: Based on the secondary compensation value of flow rate, collect the current network transmission data, calculate the data packet delay, call the timestamp to calculate the delay fluctuation range, filter the data whose fluctuation exceeds the previous range, calculate the delay change ratio, adjust the command issuance time, and obtain the remote control processing adjustment plan.

[0066] Channel switching status parameters include the number of lost data packets, the interval between adjacent data packets, the number of abnormal data packets, channel stability status, backup channel takeover status, and channel switching mode. Time synchronization adjustment values ​​include timestamp interval, time jump data packets, data stream time offset, time base correction value, and buffered data stream order adjustment parameters. Dynamic flow rate compensation values ​​include current flow rate error correction amount, cumulative error value, target flow rate optimization parameters, flow rate correction amplitude, temperature compensation parameters, and pressure compensation parameters. Flow rate secondary compensation values ​​include feedback flow rate, feedback temperature, feedback pressure, correction value deviation, response time, flow rate adjustment correction amount, and compensation parameter update value. Remote control processing adjustment schemes include data packet delay, delay fluctuation range, abnormal fluctuation data, delay change ratio, and command issuance time adjustment value.

[0067] Please see Figure 2 The specific steps of S1 are as follows:

[0068] S101: Obtain the transmission data of the primary control channel and the backup channel, parse the timestamp, data length and sequence number of the data packets, count the number of data packets received on the channel, calculate the loss index, and obtain the data packet loss of the primary control channel and the backup channel.

[0069] First, real-time data capture is performed on the monitoring systems of the primary and backup channels to obtain data packets for both channels. Each data packet contains multiple fields, with key fields including a timestamp, packet length, and sequence number. The timestamp field allows for precise calculation of the time difference between packet transmission and reception, yielding the time interval. The packet length field is used to assess the amount of data transmitted, while the sequence number verifies packet integrity. In practice, the channel data stream is first parsed to extract the timestamp, length, and sequence number of each data packet, which are then sorted chronologically. By comparing the ascending order of the sequence numbers, it can be determined whether any data packets have been lost. If a sequence number is skipped, the packet is considered lost, and the loss is calculated as the ratio of the number of lost packets to the total number of received packets. For example, assuming the received sequence numbers are 1, 2, 4, and 5, then packet with sequence number 3 is considered lost, resulting in a loss of one packet. Furthermore, by statistically analyzing the number of lost packets, the packet loss indicators for the primary and backup channels can be derived, reflecting the stability of the two channels. Finally, the loss of the primary control channel and the backup channel is obtained to evaluate the reliability of data transmission.

[0070] S102: Based on the data packet loss amount of the backup channel, parse the data packet timestamp sequence, calculate the time interval between adjacent data packets, set an abnormal threshold, call the time interval data of the primary control channel and the backup channel for comparison, filter data packets that exceed the threshold, count the number of abnormal data packets, obtain the time distribution of abnormal data packets, and obtain the number of abnormal data packets and the time interval distribution.

[0071] First, based on the packet loss rate of the backup channel, the timestamp sequence of the data packets is analyzed to obtain the time interval between each pair of adjacent data packets. Specifically, the formula for calculating the timestamp difference is:

[0072] ;

[0073] in, and These are the timestamps of adjacent data packets. For each pair of adjacent data packets, their transmission interval (in seconds) is calculated. Based on historical data, an abnormal time interval threshold is set (e.g., exceeding 100ms is considered abnormal). Then, the data packet time intervals on the backup channel are compared with those on the primary control channel to filter out abnormal data packets exceeding the set threshold. For example, if the time intervals of most data packets on the primary control channel are between 50ms and 60ms, while a data packet on the backup channel has a time interval of 200ms, this data packet is considered abnormal. Next, the number of these abnormal data packets exceeding the threshold is counted, and a time distribution graph of these abnormal packets is plotted. Assuming there are 15 abnormal packets, and their time distribution is concentrated within a certain period (e.g., 3:00 PM to 3:30 PM), it can be concluded that a significant transmission anomaly occurred during this period. Finally, the number of abnormal data packets and their time interval distribution are obtained, further providing a basis for subsequent channel management decisions.

[0074] S103: Invoke the distribution of the number and time interval of abnormal data packets, compare it with the channel stability threshold, and if the abnormal limit is exceeded, trigger the backup channel takeover, adjust the channel switching status, obtain the switching parameters, and obtain the channel switching status parameters.

[0075] First, the system retrieves the number and time interval distribution of abnormal data packets obtained in the previous step and compares this data with a pre-set channel stability threshold. For example, the threshold might be set as follows: if more than 10 abnormal data packets are detected, or if 5% of their time intervals exceed 100ms, the channel is considered unstable. During the comparison, if the number of abnormal data packets or their time interval distribution exceeds the threshold, the system considers the stability of the primary control channel threatened and triggers a backup channel takeover. The trigger condition for backup channel takeover can be determined by comparing parameters such as packet loss, latency, and bandwidth of the current channel. During the switchover, the system automatically adjusts the channel switching state, transferring the data stream from the primary control channel to the backup channel and acquiring relevant switching parameters, such as switching time (in seconds) and signal strength changes. These parameters are used to verify the efficiency and stability of the switchover. For example, if the signal strength stabilizes from -80dBm to -60dBm within 2 seconds after the switchover, and the packet loss decreases to 0, the switchover is considered successful. Finally, the channel switching state parameters are obtained, and future channel selection and switching strategies are optimized based on these parameters.

[0076] Please see Figure 3 The specific steps of S2 are as follows:

[0077] S201: Obtain channel switching status parameters, extract buffered data stream, parse data packet timestamps, calculate data packet timestamp interval values, filter time-skip data packets, sort the time-skip data packets by their sequence numbers, adjust the data packet order, and obtain the sorted data stream.

[0078] First, the channel switching status parameters need to be obtained. These parameters reflect the current operating status of the primary and backup channels, including whether a switch has occurred and the time of the switch. This status information can be obtained in real time through the network interface or transport protocol stack. Next, data streams are extracted from the buffer. These data streams contain all data packets transmitted through the current channel. Each data packet contains a timestamp field, recording the time of transmission. By parsing these timestamps, the system can obtain the timestamp of each data packet and calculate the time interval between adjacent data packets. Specifically, assuming the timestamps of two data packets are T1 and T2 (in seconds), the time interval ΔT = T2 - T1. When the time interval is abnormal, such as exceeding the set maximum tolerance value (e.g., 10ms), it is considered that a time jump has occurred. Next, the system filters these jump data packets, extracts the data packets with abnormal time intervals, and sorts these data packets by sequence number. Assuming the data packet sequence numbers are 1, 2, 4, 5, and the data packet with sequence number 3 has jumped, the data packet with sequence number 3 is first filtered from the buffer. Then, by sorting the sequence numbers of these jump data packets, the normal order of the data packets can be restored. Finally, the order of these data packets is adjusted and they are reassembled into a data stream in the correct order, resulting in a sorted data stream that ensures the order of the data stream matches the transmission timing.

[0079] S202: Based on the sorted data stream, calculate the time difference between adjacent data packets, statistically analyze the changing trend of the time difference, analyze the change in time interval, calculate the time offset of the data stream, set a time offset threshold, perform threshold judgment on the time offset of the data stream, filter the time offset values ​​of data packets that exceed the threshold, statistically analyze the number of data packets that exceed the threshold, obtain the time range of data packets with excessive offset, and obtain the amount of data with excessive time offset.

[0080] Calculate the time difference between adjacent data packets, statistically analyze the changing trend of the time difference, analyze the change in time interval, calculate the time offset of the data stream, set a time offset threshold, perform threshold judgment on the time offset of the data stream, filter the time offset values ​​of data packets that exceed the threshold, statistically analyze the number of data packets that exceed the threshold, obtain the time range of data packets with excessive offset, and obtain the amount of data with excessive time offset.

[0081] In this process, the time difference between each pair of adjacent data packets is first calculated based on the sorted data stream. Assuming the timestamps of the sorted data packets are T1, T2, and T3, the time difference between adjacent data packets is calculated as follows:

[0082] ;

[0083] Next, the system calculates the time difference between all adjacent data packets and analyzes the trend of these time differences. For example, if the time difference between data packets gradually increases, it may indicate that there are delay fluctuations in network transmission. Then, the change in the time interval for each data packet, i.e., the increment of the adjacent time difference, is calculated. For example, if ΔT12 = 20ms and ΔT23 = 50ms, then the change in the time interval is:

[0084] ;

[0085] Next, the system calculates the time offset of the data stream, typically by comparing the actual transmission time with the ideal transmission time. The formula for calculating the time offset is:

[0086] ;

[0087] A time offset threshold (e.g., ±50ms) is set, and the time offset of each data packet is judged against this threshold to filter out those packets that exceed it. For example, if a data packet has an offset of 60ms, and the set threshold is ±50ms, then this data packet is considered to exceed the threshold. Next, the number of all data packets exceeding the threshold is counted; let's assume there are 5 packets exceeding the threshold. Furthermore, by obtaining the time range of these data packets exceeding the threshold, the specific time period of the packets with excessive offset can be determined. For example, the time range of the excessive packets is from 2:00 PM to 2:30 PM. Finally, the number and time range of the excessive packets are obtained, providing information for subsequent channel adjustment and optimization decisions.

[0088] S203: Call the data volume with excessive time offset, correct the base time of the data stream based on the time offset data, adjust the overall time axis of the data stream, calculate the time difference of the data stream before and after the adjustment, and obtain the time synchronization adjustment value;

[0089] First, the number and time range of out-of-limit data packets are obtained from the previous step, and the data stream is corrected based on this data. The correction method typically adjusts the data stream's timeline based on the average or maximum offset of the out-of-limit data packets. For example, if the average offset of five out-of-limit data packets is 80ms, the overall timeline of the data stream can be shifted backward by 80ms. After adjustment, the system calculates the time difference between the data stream before and after the adjustment to evaluate the effect of the timeline correction. For example, assuming the data stream's start time before adjustment is T0, and after adjustment it is T0+80ms, the time difference is 80ms. Finally, the time synchronization adjustment value is obtained, which is the time difference between the corrected data stream and the original data stream, thus ensuring the synchronization of the data stream with the transmission timing.

[0090] Please see Figure 4 The specific steps of S3 are as follows:

[0091] S301: Call the time synchronization adjustment value, extract device data, parse the time series of flow rate and temperature, calculate the rate of change of flow rate and temperature in the time series, call the rate of change data, filter the parameters that have the greatest impact on communication delay, and obtain the key influencing parameters.

[0092] First, the time synchronization adjustment value is called to adjust the time base of the device data stream, ensuring the time synchronization of the data stream. Then, sensor data from the device is extracted, including real-time collected information such as flow rate and temperature. Flow rate and temperature are typically acquired by sensors installed in the device and recorded as time-series data. Time-series data is usually indexed by timestamps, recording the flow rate and temperature values ​​at each moment. Next, the system needs to parse this time-series data and calculate the rate of change of flow rate and temperature. Specifically, the rate of change can be obtained by calculating the difference between adjacent time points. Assuming the flow rate is v1 at a certain moment and v2 at the next moment, the rate of change of flow rate is:

[0093] ;

[0094] Here, v1 and v2 represent the flow velocity values ​​at times T1 and T2, respectively, and T2-T1 is the time difference between these two measurements. The rate of change of temperature can be calculated in a similar manner. After calculating the rates of change of flow velocity and temperature, the system further analyzes which parameters have the greatest impact on communication latency. In this process, the data on the rates of change of flow velocity and temperature are used as input for filtering. By comparing the correlation between the rates of change of different parameters and communication latency, the parameters with the greatest impact on communication latency are selected. For example, if the rate of change of flow velocity is large at a certain moment, while the rate of change of temperature is small, then flow velocity may be the key influencing parameter. Finally, the obtained key influencing parameters will be used as input for subsequent steps to help further optimize the adjustment strategies for flow velocity and temperature.

[0095] S302: Based on key influencing parameters, calculate timestamp interval indicators, analyze the ratio of the parameter's impact on communication delay, set the ratio range, filter parameters whose ratios exceed the set range, calculate the current flow rate error correction amount, call real-time feedback data, accumulate the flow rate error correction amount, call the cumulative error value, optimize and adjust the target flow rate, and obtain the optimized target flow rate;

[0096] First, the interval between timestamps, or time difference, needs to be calculated. This process is typically achieved by comparing the intervals between adjacent timestamps. If we assume the timestamps are T1, T2, and T3, then the calculated time difference is:

[0097] ;

[0098] After calculating these time differences, the system will further analyze the ratio of the impact of each parameter (such as flow rate and temperature) on the communication delay. Assuming the rates of change of flow rate and temperature are R_flow and R_temp, respectively, their ratio of impact on communication delay can be defined as:

[0099] ;

[0100] This ratio reflects the relative impact of flow rate and temperature changes, thus affecting communication latency. Next, the system filters out parameters that exceed the set ratio range. For example, if the set ratio range is 0.5 to 2.0, but the actual ratio is 2.5, this parameter is filtered out as its impact on communication latency exceeds the normal range. Then, the system calculates the error correction for the current flow rate based on the out-of-range key parameters. This error correction is typically calculated based on the difference between the current flow rate and the target flow rate. If the target flow rate is v_target and the current flow rate is v_current, the flow rate error correction is:

[0101] ;

[0102] Next, the system calls the real-time feedback data and accumulates it based on the error correction amount to form a cumulative error value. By processing the cumulative error value, the system can perform real-time optimization and adjustment of the flow rate to obtain the optimized target flow rate v_opt. For example, assuming the current flow rate is 10 m / s, the target flow rate is 12 m / s, and the error correction amount is +2 m / s, then the optimized target flow rate is adjusted to 12 m / s.

[0103] S303: Call the optimized target flow rate, adjust the correction range according to flow rate, temperature and pressure, analyze the flow rate change trend after adjustment, calculate the difference between the corrected flow rate and the target flow rate, and obtain the dynamic flow rate compensation value.

[0104] The specific formula for calculating the dynamic velocity compensation value is as follows:

[0105] ;

[0106] in, Represents the dynamic flow velocity compensation value. Represents the current flow rate. Represents the target flow rate. This represents the adjusted pressure value. This represents the adjusted temperature value. This represents the reference pressure value. Representative reference temperature value:

[0107] In this formula, the dynamic velocity compensation value ( The compensated flow rate is calculated using adjustments to the velocity difference, pressure, and temperature. Specifically, the formula represents the difference between the target flow rate and the current flow rate, and this difference is corrected using adjusted pressure and temperature values ​​to obtain the compensated flow rate. The calculation of the compensated flow rate depends not only on the velocity difference but also on pressure and temperature conditions, which are weighted by adjustment coefficients to ensure the accuracy of the flow rate adjustment.

[0108] Parameter description and acquisition method:

[0109] (Current flow rate):

[0110] The current flow rate is measured in real time by the equipment, typically through a flow meter or similar sensor, and is measured in m³ / s. For example, in a given measurement, the equipment might report a current flow rate of 3.5 m³ / s.

[0111] (Target flow rate):

[0112] The target flow rate is the ideal flow rate value set by the system, usually based on process requirements or system objectives. The target flow rate is typically set by the system's control module according to equipment requirements or adjustment strategies. In this example, the target flow rate is set to 3.2 m³ / s.

[0113] (Adjusted pressure value):

[0114] The adjusted pressure value was obtained through real-time monitoring of a pressure sensor, and the unit is Pa (Pascal). Assuming that in this calculation, the adjusted pressure given by the real-time pressure sensor is 2.1 × Pa (to the fifth power).

[0115] (Adjusted temperature value):

[0116] The adjusted temperature value is provided by a temperature sensor and is measured in Kelvin (K). Let's assume the adjusted temperature is 295 K.

[0117] (Reference pressure value):

[0118] The reference pressure value is typically given by the equipment's initial settings or normal operating conditions under standard operating conditions. The reference pressure value is set to 2.0 × Pa.

[0119] (Reference temperature value):

[0120] The reference temperature value is usually the ambient temperature under standard conditions or the initial set temperature of the equipment, which is set to 293K.

[0121] Formula calculation derivation process:

[0122] First, calculate the velocity difference:

[0123] ;

[0124] Next, calculate the adjustment factors for pressure and temperature:

[0125] ;

[0126] Then, calculate the square root of the product of the reference pressure and the reference temperature:

[0127] ;

[0128] Substitute the above results into the formula:

[0129] ;

[0130] Calculate the compensation value:

[0131] ;

[0132] Finally, calculate the compensation flow rate:

[0133] ;

[0134] Results analysis:

[0135] The calculation results show that the dynamic velocity compensation value is 2427.15 m³ / s. This value represents the difference between the current flow velocity and the target flow velocity, after adjusting for the effects of pressure and temperature. In practical applications, this compensation value can be used for further flow velocity adjustments to ensure that the flow velocity more accurately approaches the target flow velocity during actual operation.

[0136] Please see Figure 5 The specific steps of S4 are as follows:

[0137] S401: Call the dynamic flow rate compensation value, extract the flow rate, temperature and pressure data fed back from the device, analyze the difference between the feedback value and the correction value, calculate the deviation index between the feedback value and the correction value, call the instruction execution timestamp, calculate the instruction response time index, and obtain the feedback deviation and response time data.

[0138] First, the system retrieves the dynamic flow rate compensation value obtained in the previous steps. Based on this compensation value, it extracts data such as flow rate, temperature, and pressure fed back from the device. This data comes from real-time monitoring by the device's sensors, typically indexed by timestamps, reflecting changes in flow rate, temperature, and pressure at different points in time. Next, the system needs to analyze the difference between the feedback value and the correction value. Specifically, the feedback value is the real-time data from the device, while the correction value is the ideal value calculated by the system based on previous adjustment strategies or models. The difference between the two is the deviation. For example, assuming the device reports a flow rate of 3.2 m / s at a certain moment, and the correction value is 3.0 m / s, then the deviation is 0.2 m / s. Next, the system calculates the deviation index between the feedback value and the correction value. The deviation index is a quantitative way to measure the difference between the two. Generally, the deviation index can be represented by a simple absolute difference, i.e., the difference between the feedback value and the correction value. Afterward, the system retrieves the instruction execution timestamps, which record the time when the instruction was issued and the feedback was generated. These timestamps can be used to calculate the instruction response time. For example, if a command is issued at 10:00:00 and the system receives feedback at 10:00:03, the response time is 3 seconds. Finally, the system obtains the feedback deviation and response time data, which will be used in subsequent optimization processes such as anomaly detection and flow rate adjustment.

[0139] S402: Based on feedback deviation and response time data, set the deviation anomaly screening range, parse the feedback deviation data, filter data that exceeds the set range, statistically analyze the distribution of abnormal data, call the filtered deviation anomaly data, calculate the current flow rate adjustment correction amount, adjust the compensation parameters according to the correction amount, and obtain the correction compensation parameters.

[0140] First, an anomaly filtering range needs to be set. This range can be determined using experimental data or historical records. For example, assuming the flow velocity deviation range is set to ±0.5 m / s, the system will consider any flow velocity deviation exceeding this range as abnormal data. Next, the system analyzes the feedback deviation data, comparing each deviation to see if it exceeds the set range. If a deviation is 0.8 m / s and the set deviation range is ±0.5 m / s, this deviation will be filtered out as abnormal data. Then, the system statistically analyzes the distribution of the filtered abnormal data, typically representing the proportion of abnormal data in the total data. For example, assuming 15 out of 100 sampling points have deviations exceeding the set range, the system will calculate a 15% abnormal data distribution. Next, the system calculates the flow velocity adjustment correction based on the filtered abnormal data. This correction is usually determined by calculating the average deviation value of the abnormal data. Assuming the average deviation of these 15 abnormal data points is 0.6 m / s, the system will adjust the compensation parameters according to this correction. The compensation parameters can be adjusted by adding or subtracting from this correction to obtain the corrected compensation parameters. For example, if the original compensation value is 0.5 m / s, after adjustment, the compensation parameter is 0.5 m / s + 0.6 m / s, which is the corrected compensation parameter of 1.1 m / s.

[0141] S403: Call the correction compensation parameters, combine them with real-time flow rate adjustment data, calculate the flow rate compensation value after secondary correction, analyze the stability of the adjusted flow rate, and obtain the secondary flow rate compensation value.

[0142] The specific formula for calculating the secondary compensation value of flow velocity is as follows:

[0143] ;

[0144] in, This represents the secondary compensation value for the flow velocity. This represents the flow rate compensation value after one correction. This represents the correction and compensation parameters. This represents the difference between the current pressure and the reference pressure. Represents the temperature correction factor. Represents the flow velocity stability adjustment factor. Representing the The actual flow rate at each time point Representing the Adjusted flow rate at each time point This represents the total number of time points considered in the calculation:

[0145] This formula is used to calculate the secondary correction compensation value for the flow velocity, taking into account real-time adjustment data and flow velocity stability factors. The selection of each parameter is based on actual measurements or calculations from previous steps to ensure the usability and accuracy of the data.

[0146] Parameter setting and calculation process:

[0147] —The corrected flow rate compensation value, assuming it has been previously calculated. .

[0148] —Adjust the compensation parameters, based on system configuration and performance requirements, and set them to [specific value]. This represents the sensitivity to pressure and temperature adjustments.

[0149] —The difference between the current pressure and the reference pressure is measured by a pressure sensor. Let the current pressure be... The reference pressure is ,therefore .

[0150] —Temperature correction factor, set as a constant to accommodate the impact of environmental changes on flow rate. .

[0151] —Flow rate stability adjustment factor, set to This reflects the impact of flow rate adjustment on system stability.

[0152] and —The sequence data of the actual flow velocity and the adjusted flow velocity were obtained through real-time data monitoring. For the example, it is assumed that there are three time points. The measured actual flow velocities were respectively The adjusted flow rates are respectively .

[0153] Formula derivation:

[0154] First, calculate the adjustment factors for pressure and temperature:

[0155] ;

[0156] Next, calculate the average velocity difference:

[0157] ;

[0158] Finally, combining all the calculation results, the final secondary compensation value for the flow velocity is obtained:

[0159] ;

[0160] Results analysis:

[0161] The result indicates that the secondary compensation value of the flow velocity obtained through complex calculations is... This demonstrates the system's ability to adapt to changes in actual conditions after adjustment. This compensation value helps the system achieve the target flow rate more accurately, ensuring the stability of the flow rate and the efficient operation of the system.

[0162] Please see Figure 6 The specific steps of S5 are as follows:

[0163] S501: Call the flow rate secondary compensation value, collect the current network transmission data, parse the data packet timestamp, calculate the data packet transmission delay data, call the delay data, statistically analyze the delay changes in different time periods, and obtain the data packet delay value;

[0164] First, the system calls the secondary compensation value for the flow rate and combines this compensation value with the real-time network transmission data. By extracting the timestamp information of the data packets, the system can accurately record the transmission time of each data packet. Assuming the time from the sender to the receiver for a currently transmitted data packet is t_send to t_recv, then the transmission delay of this data packet is the receiving time minus the sending time, i.e.: Transmission delay = t_recv - t_send. Next, the system calculates the transmission delay data of the data packets, which reflects the latency during network transmission. To analyze network latency, the system also needs to call and statistically analyze the latency data. Specifically, the system collects the latency values ​​of multiple data packets over a period of time and statistically analyzes the latency changes of these data packets. For example, assuming the latency of 10 data packets transmitted within 5 minutes is 20ms, 25ms, 30ms, etc., the system will statistically analyze the range of latency changes for these data. By analyzing the latency changes within different time periods, the system can obtain the latency values ​​of the data packets, thus providing a basis for subsequent latency optimization.

[0165] S502: Based on the data packet delay value, analyze the timestamp interval, analyze the delay fluctuation range, set the previous delay fluctuation range, filter data that exceeds the previous range, calculate the delay change ratio of the filtered data, call the ratio data, statistically analyze the delay fluctuation of the differential ratio range, analyze the change trend of the delay ratio, and obtain the delay change ratio data.

[0166] Based on the packet latency, the system begins analyzing the timestamp intervals of the packets, i.e., the time difference between different packets. If the timestamps of two packets are t_1 and t_2, their time interval is |t_2 - t_1|. The system parses these timestamp intervals and evaluates the latency fluctuation range between them. If the current packet latency fluctuation range is large, for example, the latency fluctuation increases from 10ms to 50ms within a certain time period, the system will set a previous latency fluctuation range as a comparison benchmark. This fluctuation range can be the maximum latency change value over a certain period of time, or the standard deviation value obtained from historical data analysis. Next, the system filters packets that exceed the previous range; these packets are considered to have abnormal latency. For example, assuming the previous fluctuation range was 10ms to 50ms, and a packet's latency is 55ms, then this packet will be filtered as abnormal data. Next, the system calculates the latency change ratio of the filtered data; this ratio reflects the latency difference between abnormal data and normal data. Specifically, the latency change ratio is the ratio of the latency of the filtered abnormal data to the latency of the normal data. For example, if the latency of an abnormal data packet is 60ms and the latency of a normal data packet is 20ms, then the latency variation ratio is 3. The system then retrieves the ratio data and analyzes the latency fluctuations within the different ratio ranges. By analyzing these fluctuations, the system can obtain the trend of the latency ratio changes, and then predict future network latency behavior based on this trend, thus obtaining the latency variation ratio data.

[0167] S503: Call the delay change ratio data, calculate the instruction issuance time adjustment value, adjust the instruction issuance time, analyze the stability of the delay after adjustment, and obtain the remote control processing adjustment scheme;

[0168] The system calls the latency change ratio data and, combined with the command issuance and feedback times, calculates the command issuance time adjustment value. This adjustment value is determined by comparing the current latency with the expected latency. For example, if the system expects the latency to be no more than 50ms, and the current latency is 60ms, the system needs to adjust the command issuance time to compensate for this 10ms difference. Based on this adjustment value, the system recalculates the command transmission time, achieving a more precise command issuance time. Next, the system analyzes the stability of the adjusted latency to assess whether it has stabilized within a reasonable range. If latency fluctuations are still too large in the adjusted transmissions, the system continues to adjust the command transmission time until the latency stabilizes. Finally, the system obtains a remote control processing adjustment scheme. Through this scheme, the system can more efficiently control device response time and network transmission efficiency, optimizing the real-time performance of the entire system.

[0169] Please see Figure 7A remote control system for an intelligent water purification device for traditional Chinese medicine includes:

[0170] The channel switching module acquires channel data, calculates packet loss and interval, filters anomalies and judges stability. When the anomaly exceeds the threshold, it switches to the backup channel and acquires the channel switching status parameters.

[0171] The time synchronization adjustment module extracts the buffered data stream based on the channel switching status parameters, calculates the time interval, filters skipped data packets, calculates the data stream time offset, and corrects the time base when the offset exceeds the threshold to obtain the time synchronization adjustment value.

[0172] The flow rate compensation module extracts equipment data based on time synchronization adjustment values, calculates the rate of change of flow rate and temperature, filters key parameters that affect communication delay, and optimizes the target flow rate in combination with real-time feedback. It adjusts the correction range according to flow rate, temperature, and pressure to obtain dynamic flow rate compensation values.

[0173] The flow velocity secondary compensation module calculates the deviation between the feedback and correction values ​​based on the dynamic flow velocity compensation value, calls the timestamp to calculate the response time, filters abnormal data, calculates the flow velocity correction amount, and obtains the flow velocity secondary compensation value.

[0174] The delay adjustment module collects network data based on the secondary compensation value of flow rate, calculates delay and fluctuation, filters anomalies, calculates the delay change ratio, and obtains a remote control processing and adjustment scheme.

[0175] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote control method for an intelligent water purification device for traditional Chinese medicine, characterized in that, Includes the following steps: S1: Acquire channel data, calculate packet loss and interval, filter anomalies and determine stability. When the anomaly exceeds the threshold, switch to the backup channel, adjust the channel status, and acquire the channel switching status parameters. S2: Based on the channel switching status parameters, extract the buffered data stream, calculate the time interval, filter skipped data packets, adjust the order, calculate the data stream time offset, and when the offset exceeds the threshold, correct the time base and obtain the time synchronization adjustment value. S3: Based on the time synchronization adjustment value, extract equipment data, calculate the flow rate and temperature change rate, screen key parameters that affect communication delay, calculate the error correction amount, and optimize the target flow rate in combination with real-time feedback. Adjust the correction range according to flow rate, temperature and pressure to obtain dynamic flow rate compensation value. S4: Based on the dynamic flow rate compensation value, calculate the deviation between the feedback and correction values, call the timestamp to calculate the response time, filter abnormal data, calculate the flow rate correction amount, update the compensation parameters, and obtain the secondary flow rate compensation value. S5: Based on the secondary compensation value of the flow velocity, collect network data, calculate the delay and fluctuation, filter anomalies, calculate the delay change ratio, adjust the command time, and obtain a remote control processing and adjustment scheme. The channel switching status parameters include the number of lost data packets, the interval between adjacent data packets, the number of abnormal data packets, the channel stability status, the backup channel takeover status, and the channel switching mode. The time synchronization adjustment values ​​include the timestamp interval value, time jump data packets, data stream time offset, time base correction value, and buffered data stream order adjustment parameters. The dynamic flow rate compensation values ​​include the current flow rate error correction amount, the cumulative error value, the target flow rate optimization parameters, the flow rate correction amplitude, the temperature compensation parameters, and the pressure compensation parameters. The secondary flow rate compensation values ​​include the feedback flow rate, the feedback temperature, the feedback pressure, the correction value deviation, the response time, the flow rate adjustment correction amount, and the compensation parameter update value. The remote control processing adjustment scheme includes data packet delay, delay fluctuation range, abnormal fluctuation data, delay change ratio, and command issuance time adjustment value.

2. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 1, characterized in that, The specific steps for acquiring channel data, calculating packet loss and interval, filtering anomalies and determining stability, switching to a backup channel when anomalies exceed a threshold, adjusting the channel status, and obtaining channel switching status parameters are as follows: S101: Obtain the transmission data of the primary control channel and the backup channel, parse the timestamp, data length and sequence number of the data packets, count the number of data packets received on the channel, calculate the loss index, and obtain the data packet loss of the primary control channel and the backup channel. S102: Based on the data packet loss amount of the backup channel, parse the data packet timestamp sequence, calculate the time interval between adjacent data packets, set an abnormal threshold, call the time interval data of the main control channel and the backup channel for comparison, filter out data packets that exceed the threshold, count the number of abnormal data packets, obtain the time distribution of abnormal data packets, and obtain the number of abnormal data packets and the time interval distribution. S103: Invoke the distribution of the number and time interval of the abnormal data packets, compare it with the channel stability threshold, and if the abnormal limit is exceeded, trigger the backup channel takeover, adjust the channel switching state, obtain the switching parameters, and obtain the channel switching state parameters.

3. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 1, characterized in that, Based on the channel switching status parameters, the specific steps for extracting the buffered data stream, calculating the time interval, filtering skipped data packets, adjusting the order, calculating the data stream time offset, and correcting the time base when the offset exceeds the threshold to obtain the time synchronization adjustment value are as follows: S201: Obtain the channel switching status parameters, extract the buffered data stream, parse the data packet timestamp, calculate the data packet timestamp interval value, filter time-skip data packets, call the sequence number of the time-skip data packets to sort them, adjust the data packet order, and obtain the sorted data stream; S202: Based on the sorted data stream, calculate the time difference between adjacent data packets, statistically analyze the changing trend of the time difference, analyze the change in time interval, calculate the time offset of the data stream, set a time offset threshold, perform threshold judgment on the time offset of the data stream, filter the time offset values ​​of data packets that exceed the threshold, statistically analyze the number of data packets that exceed the threshold, obtain the time range of data packets with excessive offset, and obtain the amount of data with excessive time offset. S203: Call the amount of data that exceeds the time offset limit, correct the reference time of the data stream based on the time offset data, adjust the overall time axis of the data stream, calculate the time difference of the data stream before and after the adjustment, and obtain the time synchronization adjustment value.

4. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 1, characterized in that, Based on the aforementioned time synchronization adjustment value, the following steps are taken to extract equipment data, calculate the flow rate and temperature change rate, screen key parameters affecting communication delay, calculate the error correction amount, and optimize the target flow rate in conjunction with real-time feedback, adjusting the correction magnitude according to flow rate, temperature, and pressure to obtain the dynamic flow rate compensation value: S301: Call the time synchronization adjustment value, extract device data, parse the time series of flow rate and temperature, calculate the rate of change of flow rate and temperature in the time series, call the rate of change data, filter the parameters that have the greatest impact on communication delay, and obtain the key influencing parameters. S302: Based on the key influencing parameters, calculate the timestamp interval index, analyze the ratio of the parameter's impact on communication delay, set the ratio range, filter parameters whose ratios exceed the set range, calculate the current flow rate error correction amount, call real-time feedback data, accumulate the flow rate error correction amount, call the cumulative error value, optimize and adjust the target flow rate, and obtain the optimized target flow rate; S303: Call the optimized target flow rate, adjust the correction range according to the flow rate, temperature, and pressure, analyze the trend of the adjusted flow rate change, calculate the difference between the corrected flow rate and the target flow rate, and obtain the dynamic flow rate compensation value.

5. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 4, characterized in that, The specific formula for calculating the dynamic flow velocity compensation value is as follows: ； in, Represents the dynamic flow velocity compensation value. Represents the current flow rate. Represents the target flow rate. This represents the adjusted pressure value. This represents the adjusted temperature value. This represents the reference pressure value. This represents the reference temperature value.

6. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 1, characterized in that, Based on the dynamic flow velocity compensation value, the specific steps for calculating the deviation between the feedback and correction values, calculating the response time using the timestamp, filtering out abnormal data, calculating the flow velocity correction amount, updating the compensation parameters, and obtaining the secondary flow velocity compensation value are as follows: S401: Call the dynamic flow rate compensation value, extract the flow rate, temperature and pressure data fed back from the device, analyze the difference between the feedback value and the correction value, calculate the deviation index between the feedback value and the correction value, call the instruction execution timestamp, calculate the instruction response time index, and obtain the feedback deviation and response time data; S402: Based on the feedback deviation and response time data, set the deviation anomaly screening range, analyze the feedback deviation data, filter data that exceeds the set range, statistically analyze the distribution of abnormal data, call the filtered deviation anomaly data, calculate the current flow rate adjustment correction amount, adjust the compensation parameters according to the correction amount, and obtain the correction compensation parameters. S403: Call the corrected compensation parameters, combine them with the real-time flow rate adjustment data, calculate the flow rate compensation value after secondary correction, analyze the stability of the adjusted flow rate, and obtain the secondary flow rate compensation value.

7. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 6, characterized in that, The formula for calculating the secondary compensation value of the flow velocity is as follows: ； in, This represents the secondary compensation value for the flow velocity. This represents the flow rate compensation value after one correction. This represents the correction and compensation parameters. This represents the difference between the current pressure and the reference pressure. Represents the temperature correction factor. Represents the flow velocity stability adjustment factor. Representing the The actual flow rate at each time point Representing the Adjusted flow rate at each time point This represents the total number of time points considered in the calculation.

8. The remote control method for the intelligent water purification equipment for traditional Chinese medicine according to claim 1, characterized in that, Based on the aforementioned secondary flow velocity compensation value, the specific steps for collecting network data, calculating latency and fluctuations, filtering anomalies, calculating the latency change ratio, adjusting command time, and obtaining a remote control processing and adjustment scheme are as follows: S501: Call the flow rate secondary compensation value, collect the current network transmission data, parse the data packet timestamp, calculate the data packet transmission delay data, call the delay data, statistically analyze the delay changes in different time periods, and obtain the data packet delay value; S502: Based on the data packet delay value, analyze the timestamp interval, analyze the delay fluctuation range, set the previous delay fluctuation range, filter data that exceeds the previous range, calculate the delay change ratio of the filtered data, call the ratio data, statistically analyze the delay fluctuation of the differential ratio interval, analyze the change trend of the delay ratio, and obtain the delay change ratio data. S503: Call the delay change ratio data, calculate the instruction issuance time adjustment value, adjust the instruction issuance time, analyze the stability of the delay after adjustment, and obtain the remote control processing adjustment scheme.

9. A remote control system for an intelligent water purification device for traditional Chinese medicine, characterized in that, A remote control method for an intelligent water purification device for traditional Chinese medicine according to any one of claims 1-8, wherein the system comprises: The channel switching module acquires channel data, calculates packet loss and interval, filters anomalies and judges stability. When the anomaly exceeds the threshold, it switches to the backup channel and acquires the channel switching status parameters. Based on the channel switching status parameters, the time synchronization adjustment module extracts the buffered data stream, calculates the time interval, filters skipped data packets, calculates the data stream time offset, and when the offset exceeds the threshold, corrects the time base and obtains the time synchronization adjustment value. Based on the time synchronization adjustment value, the flow rate compensation module extracts equipment data, calculates the flow rate and temperature change rate, filters key parameters that affect communication delay, and optimizes the target flow rate in combination with real-time feedback. It adjusts the correction range according to flow rate, temperature, and pressure to obtain dynamic flow rate compensation value. The flow velocity secondary compensation module calculates the deviation between the feedback and correction values ​​based on the dynamic flow velocity compensation value, calls the timestamp to calculate the response time, filters abnormal data, calculates the flow velocity correction amount, and obtains the flow velocity secondary compensation value. Based on the secondary compensation value of the flow rate, the delay adjustment module collects network data, calculates delay and fluctuation, filters anomalies, calculates the delay change ratio, and obtains a remote control processing and adjustment scheme.

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