Intelligent food processing detection device and detection method thereof

Abstract

The invention discloses an intelligent food processing detection device and a detection method thereof, and the device comprises a collection module which is used for collecting real-time environment data in a food processing process. And the data processing module is used for receiving the real-time environment data transmitted by the acquisition module and analyzing and processing the data to obtain first data. And the display module is used for displaying the first data in real time. And the alarm module is used for judging abnormal operation or faults in the food processing process according to the first data and the real-time environment data, and sending an alarm signal. The production process is intelligently monitored, and abnormity is timely found and automatically adjusted or an alarm is given out. Therefore, the production efficiency can be improved, the food processing quality can be ensured, human intervention can be greatly reduced, and the automation and intelligence level of production can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection, and particularly to an intelligent food processing detection device and a detection method thereof. Background Art

[0002] With the increasingly serious food safety problems, the monitoring and detection means for various links such as the production environment, raw materials, and processing process in the traditional food processing gradually show limitations. Traditional food processing monitoring mostly relies on manual inspections and traditional instrument equipment. Manual inspections not only have low efficiency, but are also easily affected by subjective factors, with risks of omission, negligence, and delay. Traditional instrument equipment, such as thermometers, hygrometers, pH meters, etc., although can provide some basic parameter information, these devices usually can only collect data singly and do not have the ability of automatic and intelligent analysis.

[0003] Traditional food processing monitoring equipment often needs to collect data at regular intervals or manually, and the frequency and accuracy of data collection are limited. This makes abnormal situations in the food processing process often unable to be detected in time, and potential quality problems or safety hazards cannot be effectively prevented.

[0004] Existing monitoring equipment usually can only provide basic numerical reports and cannot perform multi-dimensional and comprehensive data analysis. There are many parameters involved in the food processing process, such as temperature, humidity, pressure, pH value, flow rate, etc. The changes of these parameters are often non-linear and complex. It is difficult for traditional equipment to comprehensively evaluate and adjust the complex production environment in a short time. Therefore, the response speed and control accuracy of existing equipment often cannot meet the requirements of modern food processing for safety, stability, and efficiency. Summary of the Invention

[0005] Aiming at the above defects, the technical problem solved by the present invention is to provide a detection device for intelligent food processing, which can collect multi-dimensional data (such as temperature, humidity, pressure, pH value, flow rate, etc.) in the production environment, raw materials, and processing process in real time through devices such as sensors and cameras. These data can be uploaded to the data processing module in real time at a high frequency and fed back to the operator or the cloud platform in time. Compared with the traditional manual inspection or data collection at regular intervals, the intelligent monitoring system can greatly improve the real-time and accuracy of data collection.

[0006] In the first aspect of the present invention, an intelligent food processing detection device is provided, including: a collection module for collecting real-time environmental data during food processing; a data processing module for receiving the real-time environmental data transmitted by the collection module, analyzing and processing the data to obtain first data; a display module for real-time displaying the first data; and an alarm module for judging abnormal operations or faults during food processing according to the first data and the real-time environmental data and sending an alarm signal.

[0007] According to an embodiment of the present invention, the data processing module includes: a receiving module for receiving the real-time environmental data collected by the collection module; a filtering module for filtering the real-time environmental data; a calibration module for calibrating the filtered real-time environmental data; a synchronization module for synchronizing the real-time environmental data of the calibration module in time; and an analysis module for analyzing the real-time environmental data after time synchronization.

[0008] According to an embodiment of the present invention, the collection module collects real-time environmental data during food processing through sensors.

[0009] According to an embodiment of the present invention, the calibration module includes: a duplicate removal unit for deleting duplicate data of the real-time environmental data; and a calibration unit for calibrating the error data of the sensor.

[0010] According to an embodiment of the present invention, the analysis module includes: a comparison unit for comparing the real-time environmental data after synchronization with a first threshold to obtain a comparison result; and a first output unit for obtaining first data according to the comparison result.

[0011] According to an embodiment of the present invention, the alarm module includes: a calculation unit for calculating the first data and the real-time environmental data to obtain second data; and a second output unit for outputting an alarm signal according to the second data.

[0012] According to an embodiment of the present invention, the device further includes a remote control module for remotely controlling the real-time environmental data.

[0013] In the second aspect of the present invention, an intelligent food processing detection method is provided, including: S101: Collecting real-time environmental data during food processing; S102: Receiving the real-time environmental data, analyzing and processing the data to obtain first data; S103: Real-time displaying the first data; S104: Judging abnormal operations or faults during food processing according to the first data and the real-time environmental data and sending an alarm signal.

[0014] According to an embodiment of the present invention, receiving real-time environmental data in S102, analyzing and processing the data to obtain first data, includes: receiving the collected real-time environmental data, removing the noise of the real-time environmental data, performing data filtering, correcting the real-time environmental data, performing time synchronization, and analyzing the synchronized real-time environmental data.

[0015] According to an embodiment of the present invention, in S101, real-time environmental data is collected through sensors.

[0016] According to an embodiment of the present invention, correcting the real-time environmental data and performing time synchronization includes: deleting the duplicate data of the real-time environmental data and correcting the error data of the sensor.

[0017] According to an embodiment of the present invention, analyzing the synchronized real-time environmental data includes: comparing the synchronized real-time environmental data with a first threshold to obtain a comparison result, and obtaining first data according to the comparison result.

[0018] According to an embodiment of the present invention, in S104, judging abnormal operations or faults in the food processing process according to the first data and real-time environmental data, and sending an alarm signal includes: calculating the first data and the real-time environmental data to obtain second data, and outputting an alarm signal according to the second data.

[0019] According to an embodiment of the present invention, the method further includes: S105: remotely controlling real-time environmental data.

[0020] A third aspect of the present invention provides an intelligent device, including a transmitter, a receiver, a memory, and a processor; the memory is used to store computer instructions; the processor is used to run the computer instructions stored in the memory to implement the above-mentioned detection method for intelligent food processing.

[0021] A fourth aspect of the present invention provides a storage medium, including: a readable storage medium and computer instructions, the computer instructions are stored in the readable storage medium; the computer instructions are used to implement the above-mentioned detection method for intelligent food processing.

[0022] The beneficial effects provided by the present invention: By collecting and analyzing various data in the food processing process in real time, the production process is intelligently monitored, abnormalities are discovered in a timely manner, and automatic adjustment or alarm is issued. This can not only improve production efficiency, ensure the quality of food processing, but also greatly reduce human intervention and improve the automation and intelligence level of production. At the same time, it greatly ensures the stability and safety in the food processing process. Description of the Drawings

[0023] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments in line with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0024] Figure 1 Schematic diagram of an intelligent food processing detection device disclosed in an embodiment of the present invention; Figure 2 Flowchart of an intelligent food processing detection method disclosed in an embodiment of the present invention; Figure 3 Another flowchart of an intelligent food processing detection method for a photovoltaic power station disclosed in an embodiment of the present invention.

[0025] Through the above accompanying drawings, specific embodiments of the present disclosure have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the present disclosure in any way, but to illustrate the concept of the present disclosure to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0026] Exemplary embodiments will be described in detail here, and their examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0027] An intelligent food processing detection device 10, as Figure 1 shown, includes a collection module for collecting real-time environmental data during food processing. A data processing module for receiving the real-time environmental data transmitted by the collection module and analyzing and processing the data to obtain first data. A display module for real-time displaying the first data shown. An alarm module for judging abnormal operations or faults during food processing according to the first data and real-time environmental data and sending an alarm signal.

[0028] The data processing module includes: The data processing module includes: a receiving module for receiving the real-time environmental data collected by the collection module. A filtering module for filtering the real-time environmental data. A calibration module for calibrating the filtered real-time environmental data. A synchronization module for synchronizing the real-time environmental data of the calibration module in terms of time. An analysis module for analyzing the real-time environmental data after time synchronization.

[0029] The collection module collects real-time environmental data during food processing through sensors.

[0030] Calibration module, including: a duplicate removal unit for removing duplicate data of the real-time environmental data; a calibration unit for calibrating the error data of the sensor.

[0031] The duplicate removal unit here can use timestamp to remove duplicates. Each sensor's data usually has a timestamp mark to ensure that the data is correctly arranged in chronological order. When removing duplicates, first, the data can be sorted according to the timestamp, and the data with the same timestamp can be compared. If multiple sensors collect the same data value at the same time point, it may be because the system has sent the data repeatedly. At this time, the duplicate data can be removed.

[0032] For example, assume that two temperature sensors simultaneously record the timestamp t1 and the temperature value of 25°C. If the values of these two sensors are the same and the timestamps are the same, then this set of data can be considered duplicate.

[0033] Or use threshold to remove duplicates. Sometimes, the sensor data may have duplicate values due to sensor jitter or short-term errors. For example, if a sensor reports the same data value several times in a row, and these values cannot be distinguished within the error range, then these values can be considered duplicate.

[0034] Specifically, set a "threshold", that is, the minimum value of data change. If the value of a certain sensor does not change significantly within this threshold for several consecutive times, these data are considered duplicate. If the value of 25°C reported by the temperature sensor does not change for several consecutive times, and the change amplitude is less than 0.1°C, then these data can be considered duplicate and can be removed.

[0035] If the amount of data generated by the sensor is very large, hash values can be used to remove duplicates. The features of each data record (such as timestamp, sensor ID, data value, etc.) can generate a unique hash value through a hash algorithm. By comparing the hash values, it can be judged whether there is duplicate data.

[0036] In some cases, the sensor may report similar values within a very short period of time. To remove duplicates, clustering algorithms (such as K-means or DBSCAN) can be used to cluster similar numerical values together, and only the center point of each cluster is retained as valid data.

[0037] The purpose of data calibration is to eliminate sensor errors and biases and ensure the accuracy of the data. Sensors may produce systematic biases due to manufacturing errors, environmental changes, or long-term use. Therefore, data calibration methods are used to compensate for these biases.

[0038] Under normal circumstances, the error of a sensor is fixed, that is, the sensor will produce a certain systematic error. For example, a certain temperature sensor may be 1°C higher than the actual temperature in all measurements. To solve this problem, known standard reference values (such as standard thermometers, pressure gauges, etc.) can be used for calibration. Regularly calibrate the sensor through a calibration device or standard instrument and record the deviation after calibration.

[0039] If the deviation of the sensor changes with the change of the input data, the data can be corrected through a linear regression model. Linear regression can establish a correction model based on the relationship between the output data of the sensor and the standard value, and correct the data through this model.

[0040] Specifically, collect a large amount of data of known true values and sensor measurement values, fit a correction formula through a linear regression algorithm to obtain a deviation model, and use this model to correct the output data of the sensor.

[0041] Assume the output value of the temperature sensor and the true temperature value have a certain linear relationship, and the model is: (1) where are regression coefficients estimated using the linear regression method. The above formula can be used to eliminate the deviation of the sensor.

[0042] If multiple sensors are used for the same or similar data acquisition in the system, data fusion technology can be used to correct the deviation of the sensors. This method can combine the data of multiple sensors and correct the sensor data through algorithms such as weighted average or Kalman filtering.

[0043] For example, assume that in a temperature monitoring system, two temperature sensors are used. Through the weighted average method, a more accurate temperature value can be synthesized according to the reliability and accuracy of each sensor. Assume the temperature value of sensor 1 is T1, the temperature value of sensor 2 is T2, and their weights are w1 and w2 respectively, then the synthesized temperature value is: (2) where is the temperature value of the sensor after data fusion.

[0044] Kalman filtering can effectively eliminate noise, compensate for the measurement error of the sensor, and perform real-time data correction. It can gradually correct the estimated value at the current moment according to the physical model of the system and the measurement data of the sensor.

[0045] In a food processing monitoring system, Kalman filtering can be used to smooth sensor data. Especially during the monitoring of physical quantities such as temperature and humidity, Kalman filtering can effectively remove short-term errors and predict future values.

[0046] The data processing module needs to cooperate closely with the sensors. The sensors transmit data to the data processing module through wireless communication (such as Wi-Fi, Bluetooth, LoRa, ZigBee, etc.) or wired connections (such as Ethernet, serial communication).

[0047] Common sensors include temperature and humidity sensors, pressure sensors, pH value sensors, flow sensors, etc., which collect environmental parameters during the food processing process in real time. The sensors convert analog signals into digital signals through reception (ADC converters, signal amplifiers, etc.) and transmit the data to the data processing module through a network interface.

[0048] The primary task of the data processing module is to receive real-time data from the sensor module. This is usually achieved through standard communication protocols such as the MQTT protocol, HTTP, TCP / IP, etc.

[0049] Among the data collected by multiple sensors, there may be duplicate data or sensor deviation data, and at this time, deduplication and correction are required.

[0050] If there are time delays or inconsistent acquisition frequencies in the data collected by multiple sensors, the data processing module needs to perform time synchronization to align the timestamps of different sensors.

[0051] After the data is preprocessed, it enters the intelligent algorithm analysis stage. This is the core part of the data processing module. Specifically, the following intelligent algorithms can be used to analyze and process the data.

[0052] In some scenarios, rule-based algorithms can be used, such as threshold judgment, condition triggering, etc. For example, if the data from the temperature sensor shows that the temperature exceeds the set range, an alarm signal is directly issued. Common rules include: When the temperature > 75°C, alarm or adjust the heating device. When the humidity < 30%, issue a humidification warning.

[0053] For more complex analysis tasks, machine learning algorithms can be used to perform more refined prediction, classification, or regression analysis on the data. Common machine learning algorithms include: For example, linear regression can be used to predict the change trend of a certain parameter (such as temperature, humidity, etc.) in the future for a period of time.

[0054] Decision tree / Random forest: Can be used for classification and regression to help the system judge whether production is normal and whether adjustment is needed.

[0055] Neural networks, deep neural networks (such as convolutional neural networks CNN, long short-term memory networks LSTM, etc.) can be used for complex pattern recognition and time series data analysis, such as modeling and predicting dynamic parameters in the food processing process.

[0056] Clustering analysis: Based on algorithms such as K-means and DBSCAN for data clustering, abnormal patterns or periodic changes in the food processing process can be identified.

[0057] For real-time monitoring data, anomaly detection is an important function. The data processing module can use the following methods to detect anomalies.

[0058] By analyzing statistical features such as the mean, standard deviation, and fluctuation range of the data, determine whether there are anomalies. For example, if the temperature value exceeds 3 standard deviations of the set range, it can be regarded as an anomaly.

[0059] Use clustering algorithms or classification models to determine whether the data deviates from the normal pattern and issue anomaly alerts in real time. For example, if the data collected by the sensor is inconsistent with the distribution of historical data, an autoencoder model can be used to detect anomalies.

[0060] In some complex scenarios, the data processing module can also predict and optimize the production process through intelligent algorithms. For example, use time series prediction algorithms (such as ARIMA, LSTM, etc.) to predict the temperature and humidity changes in the next period of time, and adjust the control strategy in advance to avoid potential problems in the production process.

[0061] After being analyzed and processed by the intelligent algorithm, the data processing module outputs the analysis results to the display module, which displays the processing parameters, analysis results, and anomaly alarms in real time. At this time, the display module can present the data in the form of charts, graphs, numbers, etc. according to the system design for the operator to monitor in real time.

[0062] In addition, when an anomaly is detected or a problem occurs in the processing process, the data processing module will trigger the alarm module to notify the operator by means of sound, vision, or information push, etc., so as to take repair measures in time.

[0063] The remote control module allows managers to access the monitoring data in real time through mobile phones or PC terminals, and remotely control the equipment as needed, such as adjusting parameters such as temperature and humidity, so as to avoid human errors or still be able to intervene in the food processing process when it is inconvenient to be on site.

[0064] To enhance the scalability and remote monitoring capabilities of the system, the data processing module can be connected to the cloud platform. The cloud platform can store long-term historical data, conduct in-depth analysis using big data analysis tools, optimize the production process, and improve food quality. For example, based on big data analysis, the system can identify potential problems in certain operation steps and provide improvement suggestions to the operator in advance.

[0065] The second aspect of the present invention provides an intelligent food processing detection method, as Figure 2 shown, including: S101: Collect real-time environmental data during food processing. S102: Receive the real-time environmental data, analyze and process the data to obtain the first data. S103: Display the first data in real time. S104: Determine abnormal operations or faults during food processing based on the first data and the real-time environmental data, and send an alarm signal.

[0066] In S102, receiving the real-time environmental data, analyzing and processing the data to obtain the first data includes: receiving the collected real-time environmental data, performing data filtering on the real-time environmental data, correcting the real-time environmental data, and performing time synchronization, and analyzing the synchronized real-time environmental data.

[0067] In S101, the real-time environmental data is collected through sensors.

[0068] Correcting the real-time environmental data and performing time synchronization includes: deleting duplicate data in the real-time environmental data and correcting error data of the sensor.

[0069] Analyzing the synchronized real-time environmental data includes: comparing the synchronized real-time environmental data with a first threshold to obtain a comparison result, and obtaining the first data based on the comparison result.

[0070] In S104, determining abnormal operations or faults during food processing based on the first data and the real-time environmental data, and sending an alarm signal includes: calculating the first data and the real-time environmental data to obtain a second data, and outputting an alarm signal based on the second data.

[0071] As Figure 3 shown, the method further includes: S105: Remotely control the real-time environmental data.

[0072] The third aspect of the present invention provides an intelligent device, including a transmitter, a receiver, a memory, and a processor; the memory is used to store computer instructions; the processor is used to run the computer instructions stored in the memory to implement the above intelligent food processing detection method.

[0073] A fourth aspect of the present invention provides a storage medium, comprising: a readable storage medium and computer instructions, wherein the computer instructions are stored in the readable storage medium; and the computer instructions are used to implement the above-mentioned intelligent food processing detection method.

[0074] The beneficial effects provided by the present invention are as follows: By collecting and analyzing various data in the food processing process in real time, the production process is intelligently monitored, abnormalities are detected in a timely manner, and automatic adjustment or alarms are issued. This can not only improve production efficiency, ensure the quality of food processing, but also greatly reduce human intervention and enhance the automation and intelligence level of production. At the same time, it greatly ensures the stability and safety in the food processing process.

[0075] Obviously, the above specific implementation cases are only examples for illustrating the application of the present method, rather than limitations on the implementation manners. For those of ordinary skill in the art, based on the above description, other different forms of changes and modifications can be made to study other related issues. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0076] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disks, or optical disks that can store program codes.

[0077] The above-described embodiments of electronic devices and the like are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0078] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical disks, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them; although the embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0080] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. The present invention is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0081] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. An intelligent food processing and detection device, characterized in that, The device includes: A collection module for collecting real-time environmental data during food processing; A data processing module for receiving the real-time environmental data, analyzing and processing the data to obtain first data; A display module for displaying the first data in real time; An alarm module for judging abnormal operations or faults during food processing based on the first data and the real-time environmental data, and sending an alarm signal.

2. The device according to claim 1, characterized in that, The data processing module includes: A receiving module for receiving the real-time environmental data collected by the collection module; A filtering module for filtering the real-time environmental data; A calibration module for calibrating the filtered real-time environmental data; A synchronization module for synchronizing the time of the real-time environmental data of the calibration module; An analysis module for analyzing the real-time environmental data after time synchronization.

3. The device according to claim 1, characterized in that, The collection module collects real-time environmental data during food processing through sensors.

4. The device according to claim 3, characterized in that, The calibration module includes: A duplicate removal unit for deleting duplicate data of the real-time environmental data; A calibration unit for calibrating the error data of the sensor.

5. The device according to claim 4, characterized in that, The analysis module includes: A comparison unit for comparing the real-time environmental data after synchronization with a first threshold value to obtain a comparison result; A first output unit for obtaining first data according to the comparison result.

6. The device according to claim 5, characterized in that, The alarm module includes: A calculation unit for calculating the first data and the real-time environmental data to obtain second data; A second output unit for outputting an alarm signal according to the second data.

7. The device according to claim 1, characterized in that, The device further includes a remote control module for remotely controlling the real-time environmental data.

8. An intelligent food processing detection method, characterized in that, The method includes: S101: Collect real-time environmental data during food processing; S102: Receive the real-time environmental data, analyze and process the data to obtain first data; S103: Display the first data in real time; S104: Judge abnormal operations or faults during food processing based on the first data and the real-time environmental data, and send an alarm signal.

9. The method according to claim 8, characterized in that, Receiving the real-time environmental data in S102, analyzing and processing the data to obtain first data includes: Receiving the collected real-time environmental data, filtering the real-time environmental data, calibrating the real-time environmental data, synchronizing the time, and analyzing the real-time environmental data after synchronization.

10. The method according to claim 8, characterized in that, Collecting the real-time environmental data through sensors in S101.

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