A field logic control system based on RISC-V technology

Abstract

The application discloses a kind of field logic control systems based on RISC-V technology, it is related to field logic control technical field.The system includes data acquisition accuracy optimization module, data transmission synchronism optimization module and intelligent execution module.The present application obtains sensor data acquisition accuracy by quantifying the acquisition sensor data acquisition parameter, according to sensor data acquisition accuracy, whether sensor data acquisition accuracy optimization is judged, the transmission synchronism of multiple sensors is obtained by quantifying the transmission synchronism parameter of multiple sensors, according to the transmission synchronism of multiple sensors, whether the transmission synchronism optimization of multiple sensors is judged, improve field logic control data acquisition accuracy and transmission synchronism, solve the problem that the existing technology exists due to the anti-interference design of traditional control system is missing, bus protocol is not unified and executor control mechanism solidification leads to field logic control data acquisition accuracy and transmission synchronism low.

Description

Technical Field

[0001] This invention relates to the field of field logic control technology, and in particular to a field logic control system based on RISC-V technology. Background Technology

[0002] The successful development and trial of the first programmable logic controller (PLC), the PDP-14, marked the application of programmed methods in electrical control. This is widely recognized as the first PLC. Subsequently, the first DCS-8 was developed in 1971, the first PLC in 1973, and the first PLC again in 1974.

[0003] PLCs have entered the practical application stage, and computer technology has been fully incorporated into PLC technology, resulting in a leap in their functionality. The development characteristics of PLCs are more adapted to the needs of modern industry, and industries such as digital machine tools, industrial automatic control, and building automation are developing rapidly.

[0004] With technological advancements and the increasing demands of industrial control, simple PLCs have gradually evolved into DCS (Distributed Control System) and Field Control System (FCS).

[0005] To achieve a highly decentralized control system, a high-performance, low-cost underlying communication network is needed to connect field instruments, known as a "fieldbus." Simultaneously, field devices must be intelligent, possessing functions such as communication, self-diagnosis and protection, data computation, and measurement and control input / output.

[0006] A fieldbus control system is a real-time network control system that interconnects the lowest-level field devices of an automation system using an open fieldbus network. Structurally, it is more distributed and can be divided into two layers: the field control network and the management coordination network.

[0007] For example, Chinese invention patent CN101266475A discloses a programmable logic control method based on fieldbus communication, which includes: an I / O driver implementation method: by introducing I / O drivers, the logic controller can conveniently access multiple data sources concurrently, while enhancing the openness and flexibility of logic data access; a PLE function block scanning algorithm: an automatic function block scanning algorithm that does not require manual setting and is suitable for tree networks and ring networks; a logic function block based on the electrical control system designed in conjunction with the characteristics of power plant electrical control; and a logic configuration method based on I / O point numbers that overcomes the limitations of traditional logic configuration, making the entire configuration process more intuitive and readable, and facilitating global data sharing and quality participation in calculations. Using these methods can effectively improve the system's flexibility and ease of use.

[0008] For example, the logic programmable controller system disclosed in Chinese invention patent CN116736789A includes: a remote control module, a power supply module, and a 5G data transmission module. It also features voltage acquisition circuits, temperature acquisition circuits, speed acquisition circuits, a data receiving unit, a database unit, and a data analysis unit. The data receiving unit, database unit, and data analysis unit are application software installed in the management personnel's internet device. The remote control module, power supply module, 5G data transmission module, and PLC are installed in an electrical control box and electrically connected. With this invention, remote personnel can specifically understand the control status of the PLC and the operating data of the on-site electrical equipment. The software unit can compare various input data with data in its own database, and can promptly prompt technicians to take targeted measures when anomalies occur.

[0009] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In the high-temperature, high-fume environment of a kitchen, traditional sensors are prone to data drift due to signal interference (such as electromagnetic noise and oil fume adhesion) (e.g., false alarms of MQ-2 oil fume concentration). Furthermore, when multiple types of sensors (such as PT100 and FLAME) are connected through a non-standard bus, there are data transmission synchronization issues. When actuators (such as exhaust fan relays) frequently start and stop (e.g., fluctuations in kitchen oil fume concentration), the mechanical delay of traditional relays can cause lag in action. Moreover, when multiple actuators are linked (e.g., exhaust + fresh air + alarm), the lack of priority control (e.g., relay contact contention) may result in some devices not responding (e.g., alarm sound and light not being triggered). There are problems with the accuracy of field logic control data acquisition and low synchronization due to the lack of anti-interference design in traditional control systems, inconsistent bus protocols, and rigid actuator control mechanisms. Summary of the Invention

[0010] This application provides a field logic control system based on RISC-V technology, which solves the problem of low real-time response of field logic control caused by insufficient functional modularity and solidified linkage logic in the prior art, and improves the real-time response of field logic control.

[0011] On one hand, a RISC-V-based field logic control system is provided, including: a data acquisition accuracy optimization module, a data transmission synchronization optimization module, and an intelligent execution module. It adopts a processor core based on the open-source RISC-V instruction set architecture, integrating field control-specific instruction set extensions, including real-time interrupt response instructions and bus communication acceleration instructions. The processor core connects to a local logic operation unit and a dual-bus interface module. Specifically, the data acquisition accuracy optimization module quantifies the accuracy of data acquisition based on sensor data acquisition parameters collected in a high-temperature kitchen environment, obtaining a quantified data acquisition accuracy result. Based on this result, it determines whether to perform data acquisition accuracy optimization. The data transmission synchronization optimization module... The quantification module is used to quantify the synchronization of the equipment's operating status based on the synchronization parameters of multi-sensor transmission, and obtain the quantification result of data transmission synchronization. Based on the obtained quantification result of data transmission synchronization, it determines whether to optimize data transmission synchronization. The intelligent execution module is used for a solid-state relay array based on the RISC-V architecture. The solid-state relay array is connected to the actuator to reduce mechanical delay. The solid-state relay has a built-in instruction parsing unit, which can identify the priority instructions marked by the standardized transmission module. After receiving the RISC-V main control execution instruction, it controls the actuator action response time from instruction reception to the actual completion of the actuator action within the preset actuator action response time, and improves the linkage response completion rate in multi-actuator linkage scenarios.

[0012] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By quantifying the acquisition parameters of the acquired sensor data, the accuracy of sensor data acquisition is obtained. Based on the accuracy of sensor data acquisition, it is determined whether to optimize the accuracy of sensor data acquisition. By quantifying the synchronization parameters of multi-sensor transmission, the synchronization of multi-sensor transmission is obtained. Based on the synchronization of multi-sensor transmission, it is determined whether to optimize the synchronization of multi-sensor transmission. This improves the accuracy of field logic control data acquisition and the synchronization of transmission. It solves the problems of low accuracy of field logic control data acquisition and the synchronization of transmission caused by the lack of anti-interference design, inconsistent bus protocols and rigid actuator control mechanisms in existing technologies.

[0013] 2. Quantifying the accuracy of data collection based on the obtained data collection results determines whether data collection accuracy optimization is necessary. This significantly improves data quality and the reliability of system decisions. By quantitatively evaluating deviations, noise, or missing data in the front-end data collection process in real time, data distortion and errors can be effectively reduced, ensuring a highly consistent data source for subsequent data analysis. Simultaneously, it avoids misjudgments, omissions, or system performance degradation caused by inaccurate data collection, and reduces additional costs such as repeated collection, manual intervention, or system rollbacks caused by low-quality data. Ultimately, this achieves intelligent, precise, and efficient data collection, laying a solid foundation for the reliable operation of the entire data chain.

[0014] 3. Based on the obtained quantitative results of data transmission synchronization, determine whether to optimize data transmission synchronization. By quantitatively evaluating data transmission synchronization, the system can monitor the consistency, integrity, and timeliness of data during transmission in real time, thereby maximizing transmission efficiency while ensuring synchronization. This optimization not only shortens data transmission time but also improves the overall system throughput, thereby enhancing the accuracy of on-site logic control data acquisition and transmission synchronization. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a field logic control system based on RISC-V technology, provided as an embodiment of this application. Detailed Implementation

[0016] This application provides a field logic control system based on RISC-V technology, which solves the problems of low accuracy and transmission synchronization of field logic control data acquisition caused by the lack of anti-interference design, inconsistent bus protocols, and rigid actuator control mechanisms in existing technologies. By quantifying the acquisition parameters of the acquired sensor data to obtain the sensor data acquisition accuracy, and determining whether to optimize the sensor data acquisition accuracy based on the sensor data acquisition accuracy, and by quantifying the multi-sensor transmission synchronization parameters to obtain the multi-sensor transmission synchronization, and determining whether to optimize the multi-sensor transmission synchronization based on the multi-sensor transmission synchronization, the accuracy and transmission synchronization of field logic control data acquisition are improved.

[0017] The technical solution in this application aims to address the aforementioned problems of low accuracy in field logic control data acquisition and low transmission synchronization caused by the lack of anti-interference design in traditional control systems, inconsistent bus protocols, and rigid actuator control mechanisms. The overall approach is as follows: The accuracy of data acquisition is quantified by collecting sensor data parameters in a high-temperature kitchen environment. Based on this quantified result, it is determined whether data acquisition accuracy optimization is needed. Similarly, the synchronization of equipment operation is quantified using multi-sensor transmission synchronization parameters. Based on this quantified result, it is determined whether data transmission synchronization optimization is needed. A solid-state relay array based on the RISC-V architecture is connected to the actuator. It can identify priority instructions marked by standardized transmission modules. Upon receiving the RISC-V main control execution instruction, the actuator's action response time from instruction reception to actual action completion is controlled within a preset actuator action response time. In multi-actuator linkage scenarios, this improves the linkage response completion rate, thereby enhancing the accuracy of on-site logic control data acquisition and transmission synchronization.

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] like Figure 1 The diagram shows a schematic of a field logic control system based on RISC-V technology provided in this application embodiment. This field logic control system includes a data acquisition accuracy optimization module, a data transmission synchronization optimization module, and an intelligent execution module. It adopts a processor core based on the open-source RISC-V instruction set architecture and integrates field control-specific instruction set extensions, including real-time interrupt response instructions and bus communication acceleration instructions. The processor core is connected to a local logic operation unit and a dual-bus interface module. The data acquisition accuracy optimization module is used to quantify the accuracy of data acquisition based on sensor data acquisition parameters collected in a high-temperature kitchen environment, obtaining a quantified result of the data acquisition accuracy, and making judgments based on the obtained quantified result. Whether to optimize data acquisition accuracy; the data transmission synchronization optimization module is used to quantify the synchronization of the equipment operation state according to the multi-sensor transmission synchronization parameters, and obtain the data transmission synchronization quantification result. Based on the obtained data transmission synchronization quantification result, it is determined whether to optimize data transmission synchronization; the intelligent execution module is used for solid-state relay array based on RISC-V architecture. The solid-state relay array is connected to the actuator to reduce mechanical delay; the solid-state relay has a built-in instruction parsing unit, which can identify the priority instructions marked by the standardized transmission module. After receiving the RISC-V main control execution instruction, it controls the actuator action response time from instruction reception to the actual completion of the actuator action within the preset actuator action response time, and improves the linkage response completion rate in multi-actuator linkage scenarios.

[0020] In this embodiment, the fieldbus logic controller primarily performs the following functions: receiving instructions from the management control center server; receiving collected data from downstream sensors; analyzing the data information from the control center / sensors and issuing instructions to downstream actuators; possessing field logic analysis, calculation, and field control capabilities, and capable of implementing certain field logic control in addition to executing the logic control of the management control center; connecting to the control domain gateway via RS485 bus and to sensors and actuators via CAN bus. Fieldbus sensors (Field Bus Transducer, FBT) include temperature sensors, humidity sensors, water level sensors, flame detection sensors, human body recognition sensors, and harmful gas concentration detection sensors, etc. Temperature sensors include three types: The first uses a DS18B20 temperature detection chip, which processes the read data and transmits the temperature value to the FBLC via a CAN bus. This sensor has a detection range of -55℃ to 125℃. The second uses a PT100 thermistor. First, the voltage values ​​of the two points of the PT100 thermistor relative to ground are measured using a series resistor circuit and an ADC. Then, the resistance value of the PT100 is calculated, and the measured temperature value is obtained by referring to a PT100 thermistor resistance-temperature comparison table. The third uses a DHT-11 temperature and humidity detection module, which reads the temperature and humidity values ​​of the measured environment and transmits the measured data to the FBLC via a CAN bus. Note that the DHT-11 module can also measure ambient humidity.

[0021] Furthermore, the data acquisition accuracy optimization module previously included: a composite sensor array equipped with a RISC-V architecture edge computing unit. The composite sensor array integrates an MQ-2 oil fume sensor, a PT100 temperature sensor, and a FLAME flame sensor. Each sensor has a built-in filter chip based on the RISC-V instruction set. The filter chip is equipped with an adaptive anti-electromagnetic interference algorithm, which can identify and filter out electromagnetic noise in real time. At the same time, it reduces physical interference through a nano-coating to prevent oil fume adhesion. The edge computing unit performs local preprocessing on the raw sensor data.

[0022] Furthermore, the data acquisition accuracy optimization module includes a sensor data acquisition parameter acquisition unit, a sensor data acquisition parameter threshold storage unit, a sensor data acquisition parameter correction factor storage unit, and a sensor data acquisition parameter quantization unit. The sensor data acquisition parameter acquisition unit acquires sensor data acquisition parameters during the data acquisition process, specifically including data drift rate, electromagnetic noise, and false alarm rate. The data drift rate represents the deviation rate between the actual sensor output value and the standard value. The sensor data acquisition parameter threshold storage unit retrieves data drift rate thresholds, electromagnetic noise thresholds, and false alarm rate thresholds from the control database. The sensor data acquisition parameter correction factor storage unit retrieves data drift rate correction factors, electromagnetic noise correction factors, and false alarm rate correction factors from the control database. The sensor data acquisition parameter quantization unit corrects the sensor data acquisition parameter thresholds and the ratio analysis results of the sensor data acquisition parameters using the sensor data acquisition parameter correction factors to obtain the sensor data acquisition accuracy.

[0023] In this embodiment, the specific formula for obtaining the accuracy of sensor data acquisition is as follows: ; ; A indicates the accuracy of sensor data acquisition.

[0024] B1 represents the data drift rate correction factor obtained from the control database.

[0025] C represents the data drift rate, which is the deviation rate between the actual output value of the sensor and the standard value under the same environmental conditions (such as stable oil fume concentration and temperature).

[0026] C0 represents the data drift rate threshold obtained from the control database.

[0027] B2 represents the electromagnetic noise correction factor obtained from the control database.

[0028] D0 represents the electromagnetic noise threshold obtained from the control database.

[0029] D represents electromagnetic noise.

[0030] B3 represents the false alarm rate correction factor obtained from the control database.

[0031] E0 represents the false alarm rate threshold obtained from the control database.

[0032] E represents the false alarm rate, which is the ratio of the number of false alarms triggered by the sensor under conditions without a target (such as no smoke or flame) to the total number of monitoring times.

[0033] It should be noted that the control database stores three types of correction factors corresponding to the sensor data acquisition parameters: data drift rate correction factor, electromagnetic noise correction factor, and false alarm rate correction factor. Their values ​​generally range from 0 to 1, and the sum of these three correction factors is always 1. There is a pre-defined mapping relationship between the sensor data acquisition parameters and the corresponding three types of correction factors. This relationship can be either one-to-one or many-to-one. For example, in practical applications, real-time sensor data acquisition parameters can be substituted into this mapping relationship to quickly obtain the corresponding correction factors.

[0034] The greater the electromagnetic noise, the more the performance of system components (such as the sensor's sensitive circuit and signal amplifier) ​​deteriorates, causing data deviation to gradually evolve from random fluctuations into systematic shifts. In other words, noise accumulation leads to data drift, and the greater the data drift rate, the higher the electromagnetic noise. The higher the electromagnetic noise, the greater the probability that the data exceeds the normal threshold, and the higher the false alarm rate. The greater the data drift, the more systematic the deviation, which will cause the system's normal reference to shift. If not calibrated in time, the originally reasonable threshold will become outdated, thus causing false alarms, and the higher the false alarm rate, the greater the false alarm rate.

[0035] Data drift rate is negatively correlated with sensor data acquisition accuracy. A higher data drift rate means that the systematic error grows faster per unit time, the deviation of the data from the true value is greater, and the sensor data acquisition accuracy is lower. Electromagnetic noise is also negatively correlated with sensor data acquisition accuracy. Higher electromagnetic noise means that the proportion of invalid interference components in the sensor output data is greater, the deviation of the data from the true value (random error) is more significant, and the sensor data acquisition accuracy is lower. False alarm rate is also negatively correlated with sensor data acquisition accuracy. A higher false alarm rate means that the probability of misclassifying normal data as abnormal is greater, and the sensor data acquisition accuracy is lower.

[0036] Furthermore, the specific process for determining whether to optimize data acquisition accuracy based on the obtained quantitative results of data acquisition accuracy is as follows: if the sensor data acquisition accuracy is greater than or equal to the preset sensor data acquisition accuracy threshold, then no data acquisition accuracy optimization is performed; if the sensor data acquisition accuracy is lower than the preset sensor data acquisition accuracy threshold, then data acquisition accuracy optimization is performed.

[0037] Further, the specific steps for optimizing data acquisition accuracy are as follows: Step 1, select sensors with anti-electromagnetic interference design, such as those with metal shielding shells and filter circuits, and use twisted-pair or shielded cables for signal lines; Step 2, add voltage regulator modules and decoupling capacitors, provide independent power supply or use isolated DC-DC modules, incorporate temperature sensors, establish temperature-output characteristic curves, and compensate for temperature drift errors in real time; Step 3, use moving average filtering or Kalman filtering, eliminate outliers based on statistical methods, combine multi-sensor data, reduce false alarms by controlling the logical judgment rules in the database, and dynamically adjust the alarm threshold according to the historical data distribution to avoid sensitivity imbalance caused by fixed thresholds.

[0038] In this embodiment, to optimize data acquisition accuracy, firstly, a sensor with anti-electromagnetic interference design is selected. This type of sensor, equipped with a metal shielding shell, effectively blocks external electromagnetic radiation. The built-in filtering circuit further filters out high-frequency noise. Simultaneously, twisted-pair or shielded cables are used as signal lines. Through the magnetic field cancellation effect of the twisted wires and the grounding treatment of the shielding layer, noise interference caused by electromagnetic coupling is significantly reduced, reducing signal distortion at the transmission stage and initially improving data purity. Based on this, the power supply and environmental adaptability design needs to be strengthened. A voltage regulator module is added to ensure stable power supply voltage and avoid signal drift caused by voltage fluctuations. Adding decoupling capacitors can absorb transient pulse interference in the circuit. Using an independent power supply or an isolated DC-DC module can cut off interference conduction paths between different circuits. At the same time, a built-in temperature sensor monitors the ambient temperature in real time, pre-establishing a precise temperature-output characteristic curve. An algorithm compensates for drift errors caused by temperature changes in real time, especially temperature drift errors, ensuring that the sensor maintains stable output under different temperature environments. The accuracy is significantly improved, and the data drift rate is reduced. Finally, the moving average filter can smooth the random fluctuations in the data, while the Kalman filter can dynamically estimate the optimal result by combining the system model and the measured value. The combination of the two filtering methods can effectively improve the smoothness and reliability of the data. Then, outlier points are removed from the collected data based on statistical methods to reduce the impact of extreme outliers on the overall accuracy. At the same time, cross-validation is performed on the data collected by multiple sensors. The validity of the data is verified by multiple checks through the preset logical judgment rules in the control database, which greatly reduces the risk of false alarms caused by single sensor failure or interference. Furthermore, the alarm threshold is dynamically adjusted according to the distribution characteristics of historical data to avoid the sensitivity imbalance problem caused by fixed thresholds when the data distribution changes. Finally, the whole-link optimization from hardware anti-interference and environmental error compensation to algorithm intelligent processing is achieved, which significantly improves the accuracy of data acquisition, reduces the negative impact of electromagnetic noise and data drift rate, and effectively reduces the occurrence of false alarms, ensuring that the data can truly and reliably reflect the actual state of the measured physical quantity.

[0039] Further optimization of data acquisition accuracy includes: Step 4, calling the dynamic calibration model through the RISC-V architecture edge computing unit, generating real-time calibration coefficients based on the deviation direction of the current data drift rate, linearly correcting the original output value of the sensor, and recalculating the drift rate at preset time intervals until it drops below the preset drift rate; Step 5, controlling the filter chip to adjust the adaptive anti-electromagnetic interference algorithm. If the current electromagnetic noise is greater than the preset electromagnetic noise threshold, the filter coefficient of that frequency band is increased to enhance the targeted filtering effect, while expanding the noise recognition frequency band; Step 6, reconstructing the alarm logic through the edge computing unit, setting MQ-2 to trigger an alarm by continuously detecting the oil fume concentration within a preset number of times, and setting FLAME to trigger an alarm by the flame signal lasting for a preset time or more, extending the alarm judgment cycle in scenarios without a target trigger.

[0040] In this embodiment, to further optimize the accuracy of data acquisition, it is necessary to deepen the process from three aspects: intelligent calibration, dynamic anti-interference, and alarm logic reconstruction. First, an edge computing unit based on the RISC-V architecture is deployed. With its high-efficiency computing power, it can call the dynamic calibration model in real time. This model can accurately identify the direction and magnitude of the current data drift rate deviation, and generate corresponding real-time calibration coefficients accordingly. This linearly corrects the original sensor output value and continuously tracks and recalculates the drift rate at preset time intervals. Closed-loop adjustment ensures that the drift rate quickly decreases to a preset range, significantly improving long-term data stability. Second, to address electromagnetic noise interference, a filter chip with adaptive adjustment capabilities is introduced. The edge computing unit monitors the noise intensity in real time. Once the current electromagnetic noise exceeds a preset threshold, the filter chip is immediately controlled to increase the filtering coefficient for the corresponding frequency band, enhancing the targeted filtering effect for interference in that frequency band. Simultaneously, it automatically expands the noise recognition frequency band to cover a wider range of interference types, enabling the system to maintain a high signal-to-noise ratio in complex electromagnetic environments and reducing data distortion caused by noise. At the alarm logic level, intelligent reconstruction is performed through edge computing units. For the MQ-2 fume sensor, an alarm is triggered only after a preset number of consecutive times the fume concentration exceeds the standard, avoiding false alarms caused by single instantaneous interference. For the FLAME flame sensor, the alarm mechanism is activated only after the flame signal has lasted for a preset time, effectively eliminating interference from brief flames. At the same time, for scenarios without target triggering, the alarm judgment period is extended to reduce the probability of false judgments caused by environmental fluctuations. Through the collaborative optimization of hardware and algorithms, a complete closed loop is formed from dynamic calibration, adaptive interference resistance to intelligent alarm, comprehensively improving the accuracy and reliability of data acquisition and minimizing the impact of drift, noise, and false alarms on system performance.

[0041] Furthermore, the data transmission synchronization optimization module includes a multi-sensor transmission synchronization parameter acquisition unit, a multi-sensor transmission synchronization parameter threshold storage unit, a multi-sensor transmission synchronization parameter correction factor storage unit, and a multi-sensor transmission synchronization parameter quantization unit. The multi-sensor transmission synchronization parameter acquisition unit acquires the multi-sensor transmission synchronization parameters in a multi-sensor monitoring system, specifically including data transmission delay difference and actuator response time, while also acquiring sensor data acquisition accuracy. The multi-sensor transmission synchronization parameter threshold storage unit acquires data transmission delay difference thresholds, actuator response time thresholds, and sensor data acquisition accuracy thresholds from the control database. The transmission synchronization parameter correction factor storage unit is used to obtain data transmission delay difference correction factor, actuator action response time correction factor, and sensor data acquisition accuracy correction factor from the control database. The multi-sensor transmission synchronization parameter quantization unit is used to correct the ratio analysis results of the multi-sensor transmission synchronization parameter threshold and the multi-sensor transmission synchronization parameter using the multi-sensor transmission synchronization parameter correction factor to obtain the first synchronization; it also corrects the ratio analysis results of the sensor data acquisition accuracy and the sensor data acquisition accuracy threshold using the sensor data acquisition accuracy correction factor to obtain the second synchronization; finally, the first and second synchronizations are coupled to obtain the multi-sensor transmission synchronization.

[0042] In this embodiment, the specific formula for obtaining the multi-sensor transmission synchronization is as follows: ; ; P represents the synchronization of multi-sensor transmission.

[0043] Q1 represents the accuracy correction factor for sensor data acquisition obtained from the control database.

[0044] V indicates the accuracy of sensor data acquisition.

[0045] V0 represents the threshold for the accuracy of sensor data acquisition obtained from the control database.

[0046] Q2 represents the data transmission delay difference correction factor obtained from the control database.

[0047] U0 represents the data transmission delay difference threshold obtained from the control database.

[0048] U represents the data transmission delay difference, which refers to the time difference between the arrival of data from multiple types of sensors (such as the PT100 temperature sensor and the MQ-2 oil fume sensor) triggered at the same time to the controller.

[0049] Q3 represents the actuator action response time correction factor obtained from the control database.

[0050] W0 represents the executor action response time threshold obtained from the control database.

[0051] W represents the actuator response time, which is the time difference between when the controller issues an execution command (such as starting the exhaust fan) and when the actuator actually completes the action (such as the relay contact closing or the exhaust fan starting).

[0052] It should be noted that the control database stores three types of correction factors corresponding to the multi-sensor transmission synchronization parameters: a data transmission delay difference correction factor, an actuator action response time correction factor, and a sensor data acquisition accuracy correction factor. Their values ​​generally range from 0 to 1, and the sum of these three correction factors is always 1. There is a pre-defined mapping relationship between the multi-sensor transmission synchronization parameters and their corresponding three types of correction factors. This relationship can be either one-to-one or many-to-one. For example, in practical applications, the real-time multi-sensor transmission synchronization parameters can be substituted into this mapping relationship to quickly obtain the corresponding correction factors.

[0053] The lower the accuracy of sensor data acquisition, such as the presence of drift, noise, or false alarms, the more invalid information or erroneous values ​​will be contained in the transmitted raw data. The system may need to add data verification, retransmission, or redundant transmission mechanisms, which will consume additional transmission bandwidth and time, resulting in a larger data transmission delay. A larger data transmission delay will directly increase the time for the actuator to receive the command. This delay, combined with the actuator's own mechanical / electronic response time (such as motor start-up time, valve opening and closing time), results in a longer actuator action response time. A longer actuator action response time (such as a slow valve opening speed) will cause changes in the environmental state to lag behind the sensor's expected acquisition rhythm, causing the intermediate state data collected by the sensor during the adjustment process to deviate from the actual steady state, resulting in lower sensor data acquisition accuracy.

[0054] There is a positive correlation between the accuracy of sensor data acquisition and the synchronization of multi-sensor transmission. The higher the accuracy of sensor data acquisition, the more accurately the synchronously transmitted multi-sensor data can represent the environmental state at the same point in time, and the higher the synchronization of multi-sensor transmission. There is a negative correlation between the data transmission delay difference and the synchronization of multi-sensor transmission. The greater the data transmission delay difference, the greater the difference in the time taken for data from different sensors to be acquired and transmitted to the processing unit, resulting in data arrival time misalignment and lower multi-sensor transmission synchronization. There is also a negative correlation between the actuator action response time and the synchronization of multi-sensor transmission. The longer the actuator action response time, the more its action will lag behind the sensor acquisition rhythm, and the lower the synchronization of multi-sensor transmission.

[0055] Furthermore, the specific steps for determining whether to perform data transmission synchronization optimization based on the obtained data transmission synchronization quantification results are as follows: if the multi-sensor transmission synchronization is greater than or equal to the preset multi-sensor transmission synchronization threshold, then multi-sensor transmission synchronization optimization is not performed; if the multi-sensor transmission synchronization is less than the preset multi-sensor transmission synchronization threshold, then multi-sensor transmission synchronization optimization is performed.

[0056] Further, the specific steps for optimizing data transmission synchronization are as follows: S1, uniformly adopt digital interfaces; if analog signals must be used, add an ADC sampling synchronization signal, adopt a synchronous communication protocol, and use a hardware timer to trigger multi-sensor synchronous sampling; S2, shorten the distance between the sensor and the controller, adopt a low-latency CAN bus communication method, the controller adds a high-precision timestamp to each data packet, and uses an interpolation compensation algorithm to time-align the delay difference data; S3, establish a data buffer (FIFO queue), wait for all sensor data to arrive and process them uniformly, and apply Kalman filtering to the PT100 delay data.

[0057] In this embodiment, to optimize data transmission synchronization, it is first necessary to ensure consistent time references from the sampling source, using a unified digital interface for data transmission. If analog signals must be used due to special requirements, an ADC sampling synchronization signal must be added. A synchronous communication protocol is employed, and a hardware timer precisely triggers multi-sensor synchronous sampling. This hardware-level synchronization triggering mechanism minimizes the deviation in the initial sampling time of each sensor, laying the foundation for subsequent data synchronization. Furthermore, by shortening the physical distance between the sensor and the controller, path delay differences during signal transmission are reduced. Simultaneously, a low-latency communication method, CAN bus, is used to reduce data latency in the transmission link. When receiving data, the controller adds a high-precision timestamp to each data packet and uses an interpolation compensation algorithm to perform fine-grained time alignment for data with latency differences, enabling precise matching of data from different sensors in the time dimension and significantly improving synchronization accuracy during transmission. Finally, a data buffer (such as a FIFO) is established in the data processing stage. The system queues data from all sensors before processing them uniformly, avoiding synchronization failures caused by delayed data. For sensors like the PT100 that are prone to data delays, Kalman filtering is used to smooth and predict the data, effectively compensating for data transmission delays and further correcting time deviations. Through this series of measures covering the entire process from sampling synchronization and transmission optimization to data processing compensation, the synchronization of multi-sensor data transmission can be significantly improved, ensuring high temporal consistency of data from all sensors. This provides a reliable time reference for subsequent data fusion, analysis, and decision-making, significantly improving the overall collaborative efficiency and data processing accuracy of the system.

[0058] Further optimization of data transmission synchronization includes: S4, unifying the interfaces of different types of sensors in the composite sensor array to an SPI synchronous communication interface; replacing the PT100 temperature sensor with a digital temperature sensor with an SPI interface to ensure that all sensors use the same digital communication method; simultaneously, setting a hardware synchronization trigger line between the sensor and the standardized transmission module, with the RISC-V main control module sending synchronous sampling pulses through real-time interrupt response instructions, enabling multiple types of sensors to start data acquisition at the same time; S5, integrating a high-precision real-time clock into the RISC-V main control; when sensor data is transmitted through the standardized transmission module, the bus controller adds an acquisition timestamp and a transmission timestamp based on the RTC to each data frame; after the data arrives at the controller, the transmission delay of each sensor data is calculated by comparing the timestamps, and for multiple sensor data at the same trigger time, a linear interpolation algorithm is used for time alignment, correcting the time delay difference after alignment.

[0059] In this embodiment, to further optimize data transmission synchronization, it is necessary to deepen the process from two aspects: hardware interface standardization and timestamp fine-grained management. For composite sensor arrays, the interfaces of different types of sensors are unified to SPI synchronous communication interfaces. For the PT100 temperature sensor, it is replaced with a digital temperature sensor with an SPI interface, ensuring that all sensors use the same digital communication method and eliminating the problem of inconsistent transmission delays caused by differences in interface types. At the same time, a hardware synchronization trigger line is set between the sensor and the standardized transmission module. The RISC-V main control module sends a synchronization sampling pulse through a real-time interrupt response instruction. This hardware-level triggering mechanism can ensure that multiple types of sensors accurately start data acquisition at the same time, minimizing the initial time deviation from the source of acquisition. Based on this, in RISC-V... The main control unit integrates a high-precision real-time clock (RTC). When sensor data is transmitted via the standardized transmission module, the bus controller synchronously adds an acquisition timestamp and a transmission timestamp based on the RTC to each data frame. This dual timestamp recording enables time traceability throughout the entire data lifecycle. After the data arrives at the controller, the transmission delay of each sensor's data can be accurately calculated by comparing the two timestamps. For multi-sensor data acquired at the same trigger time, a linear interpolation algorithm is used for time alignment. The time delay difference after alignment is specifically corrected, ensuring that the deviation of different sensor data in the time dimension is controlled within the microsecond range. Through four levels of optimization—interface standardization to eliminate communication differences, hardware triggering to ensure acquisition synchronization, dual timestamps to achieve delay quantization, and interpolation algorithms to complete precise alignment—the synchronization accuracy of multi-type sensor data transmission is significantly improved. This provides a highly consistent time reference for multi-source data fusion and effectively enhances the system's perception and decision-making capabilities in complex scenarios.

[0060] It is important to clarify that, in order to achieve accurate monitoring and safety control of relevant scenarios, the working settings and safety control logic of each sensor must be clearly defined: The PT100 temperature sensor detects the temperature once per second and reports data every 5 minutes under normal circumstances. If the current temperature is more than 1 degree higher than the previous one (indicating that the temperature is rising too quickly), or if the temperature exceeds 200 degrees (indicating a fire risk), the data is reported immediately; The MQ-2, MQ-5, and DHT sensors all detect the corresponding concentration values ​​once per second (MQ-2 detects oil fume concentration, MQ-5 detects gas concentration, and DHT detects temperature and humidity), and report data every 5 minutes under normal circumstances; The FLAME flame sensor is used to detect whether there is a flame on the stove to determine whether it is in use. It reports data immediately when there is a change, and reports every 5 minutes under normal circumstances; The LD2402 sensor detects once per second to determine whether there are people or not in the area. It reports information immediately when there is a change, and also reports every 5 minutes under normal circumstances. Regarding safety control, when receiving data reported by PT100, the temperature value should be displayed. If the temperature exceeds 200 degrees Celsius, an alarm should be triggered immediately. When receiving MQ-2 data, the oil fume concentration value should be displayed. When the concentration is higher than the preset concentration threshold or DO detects a high concentration, the exhaust fan should be started. The exhaust fan should be turned off when the concentration drops back to the preset concentration range. When receiving MQ-5 data, the gas concentration value should be displayed. When the concentration is higher than the preset concentration threshold or DO detects a high concentration, the exhaust fan should be started. The exhaust fan should be turned off when the concentration drops back to the preset concentration range. When receiving DHT data, the corresponding temperature and humidity values ​​should be displayed. Data received from FLAME and LD2402 should be recorded. When FLAME displays ignition status and LD2402 displays unattended status, an alarm should be triggered immediately.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0066] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A field logic control system based on RISC-V technology, characterized in that, It includes a data acquisition accuracy optimization module, a data transmission synchronization optimization module, and an intelligent execution module: The data acquisition accuracy optimization module is used to quantify the accuracy of data acquisition based on the sensor data acquisition parameters collected in the high-temperature environment of the kitchen, obtain the data acquisition accuracy quantification result, and determine whether to perform data acquisition accuracy optimization based on the obtained data acquisition accuracy quantification result. The data transmission synchronization optimization module is used to quantify the synchronization during the device operation process based on the multi-sensor transmission synchronization parameters, obtain the data transmission synchronization quantification result, and determine whether to perform data transmission synchronization optimization based on the obtained data transmission synchronization quantification result. The intelligent execution module is used for a solid-state relay array based on the RISC-V architecture. The solid-state relay array is connected to the actuator to reduce mechanical delay. The solid-state relay has a built-in instruction parsing unit that can identify priority instructions. After receiving the RISC-V master control execution instruction, it controls the actuator action response time from instruction reception to actual actuator action completion within a preset actuator action response time, and improves the linkage response completion rate in multi-actuator linkage scenarios.

2. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The data acquisition accuracy optimization module includes: The composite sensor array, equipped with a RISC-V architecture edge computing unit, integrates an MQ-2 oil fume sensor, a PT100 temperature sensor, and a FLAME flame sensor. Each sensor has a built-in filter chip based on the RISC-V instruction set. The filter chip is configured with an adaptive anti-electromagnetic interference algorithm, which can identify and filter out electromagnetic noise in real time. At the same time, it reduces physical interference through a nano-coating to prevent oil fume adhesion. The edge computing unit performs local preprocessing on the raw sensor data.

3. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The data acquisition accuracy optimization module includes a sensor data acquisition parameter acquisition unit, a sensor data acquisition parameter threshold storage unit, a sensor data acquisition parameter correction factor storage unit, and a sensor data acquisition parameter quantization unit. The sensor data acquisition parameter acquisition unit is used to acquire sensor data acquisition parameters during the sensor data acquisition process, specifically including: data drift rate, electromagnetic noise, and false alarm rate. The data drift rate represents the deviation rate between the actual output value of the sensor and the standard value. The sensor data acquisition parameter threshold storage unit is used to obtain data drift rate threshold, electromagnetic noise threshold, and false alarm rate threshold from the control database. The sensor data acquisition parameter correction factor storage unit is used to obtain data drift rate correction factor, electromagnetic noise correction factor and false alarm rate correction factor from the control database. The sensor data acquisition parameter quantization unit is used to correct the sensor data acquisition parameter threshold and the ratio analysis results of the sensor data acquisition parameters by using the sensor data acquisition parameter correction factor, so as to obtain the sensor data acquisition accuracy.

4. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The specific process for determining whether to optimize data collection accuracy based on the obtained quantitative results of data collection accuracy is as follows: If the accuracy of sensor data acquisition is greater than or equal to the preset sensor data acquisition accuracy threshold, no data acquisition accuracy optimization will be performed. If the accuracy of sensor data acquisition is lower than the preset threshold for sensor data acquisition accuracy, then the data acquisition accuracy will be optimized.

5. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The specific steps for optimizing the accuracy of data collection are as follows: Step 1: Select a sensor with electromagnetic interference resistance design, such as one with a metal shielding shell and a filter circuit; use twisted-pair or shielded cables for the signal lines. Step 2: Add a voltage regulator module and decoupling capacitor, provide independent power supply or use an isolated DC-DC module with built-in temperature sensor, establish a temperature-output characteristic curve, and compensate for temperature drift error in real time. Step 3: Using moving average filtering or Kalman filtering, outliers are removed based on statistical methods. Multi-sensor data is combined, and logical judgment rules in the control database are used to reduce false alarms. The alarm threshold is dynamically adjusted according to the distribution of historical data to avoid sensitivity imbalance caused by fixed thresholds.

6. The field logic control system based on RISC-V technology as described in claim 5, characterized in that, The optimization of data collection accuracy also includes: Step 4: The dynamic calibration model is called through the RISC-V architecture edge computing unit. Based on the deviation direction of the current data drift rate, real-time calibration coefficients are generated to linearly correct the original output value of the sensor. The drift rate is recalculated at preset time intervals until it drops to within the preset drift rate. Step 5: Control the filter chip to adjust the adaptive anti-electromagnetic interference algorithm. If the current electromagnetic noise is greater than the preset electromagnetic noise threshold, increase the filter coefficient to enhance the targeted filtering effect and expand the noise recognition frequency band. Step 6: Reconstruct the alarm logic through the edge computing unit, set MQ-2 to trigger an alarm by continuously detecting the concentration of oil fumes within a preset number of times, and set FLAME to trigger an alarm by the flame signal lasting for a preset time or longer, thereby extending the alarm judgment cycle in scenarios without a target trigger.

7. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The data transmission synchronization optimization module includes a multi-sensor transmission synchronization parameter acquisition unit, a multi-sensor transmission synchronization parameter threshold storage unit, a multi-sensor transmission synchronization parameter correction factor storage unit, and a multi-sensor transmission synchronization parameter quantization unit. The multi-sensor transmission synchronization parameter acquisition unit is used to acquire the multi-sensor transmission synchronization parameters in the monitoring composed of multiple sensors, specifically including: data transmission delay difference and actuator action response time, and at the same time, the accuracy of sensor data acquisition. The multi-sensor transmission synchronization parameter threshold storage unit is used to obtain data transmission delay difference threshold, actuator action response time threshold and sensor data acquisition accuracy threshold from the control database. The multi-sensor transmission synchronization parameter correction factor storage unit is used to obtain data transmission delay difference correction factor, actuator action response time correction factor and sensor data acquisition accuracy correction factor from the control database. The multi-sensor transmission synchronization parameter quantization unit is used to correct the analysis results of the ratio of the multi-sensor transmission synchronization parameter threshold to the multi-sensor transmission synchronization parameter through a multi-sensor transmission synchronization parameter correction factor to obtain a first synchronization; to correct the analysis results of the ratio of the sensor data acquisition accuracy to the sensor data acquisition accuracy threshold through a sensor data acquisition accuracy correction factor to obtain a second synchronization; and to couple the first synchronization and the second synchronization to obtain the multi-sensor transmission synchronization.

8. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The specific steps for determining whether to perform data transmission synchronization optimization based on the obtained data transmission synchronization quantification results are as follows: If the multi-sensor transmission synchronization is greater than or equal to the preset multi-sensor transmission synchronization threshold, then multi-sensor transmission synchronization optimization will not be performed. If the multi-sensor transmission synchronization is less than the preset multi-sensor transmission synchronization threshold, then multi-sensor transmission synchronization optimization is performed.

9. The field logic control system based on RISC-V technology as described in claim 1, characterized in that, The specific steps for optimizing data transmission synchronization are as follows: S1 uses a unified digital interface. If analog signals must be used, an ADC sampling synchronization signal is added, a synchronous communication protocol is adopted, and a hardware timer is used to trigger multi-sensor synchronous sampling. S2 shortens the distance between the sensor and the controller, adopts a low-latency CAN bus communication method, the controller adds a high-precision timestamp to each data packet, and uses an interpolation compensation algorithm to time-align the delay difference data; S3: Set up a data buffer and wait for all sensor data to arrive before processing them uniformly. Apply Kalman filtering to the PT100 delayed data.

10. The field logic control system based on RISC-V technology as described in claim 9, characterized in that, The optimization of data transmission synchronization also includes: S4 unifies the interfaces of different types of sensors in the composite sensor array to an SPI synchronous communication interface. For the PT100 temperature sensor, it is replaced with a digital temperature sensor with an SPI interface to ensure that all sensors use the same digital communication method. At the same time, a hardware synchronization trigger line is set between the sensor and the standardized transmission module. The RISC-V main control module sends a synchronization sampling pulse through a real-time interrupt response instruction, so that multiple types of sensors start data acquisition at the same time. S5 integrates a high-precision real-time clock in the RISC-V main controller. When sensor data is transmitted through the standardized transmission module, the bus controller adds an acquisition timestamp and a transmission timestamp based on RTC to each data frame. After the data arrives at the controller, the transmission delay of each sensor data is calculated by comparing the timestamps. For multiple sensor data at the same trigger time, a linear interpolation algorithm is used to align the time and correct the time delay difference after alignment.

Images