Freezing and fresh-keeping production line detection system and method based on Internet of Things

Abstract

The invention discloses a frozen fresh-keeping production line detection system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. Production line environment monitoring equipment is networked and connected in series through an Internet of Things platform; setting a monitoring window, and collecting monitoring data; performing overall planning processing on data acquired by each device through data cleaning, and constructing a curve model for the acquired data by using a curve simulation model; mapping each data curve model into the same coordinate system, and constructing a contrast curve analysis model; analyzing the trend difference degree of each time point of the contrast curve, and performing difference fusion analysis processing on each curve to obtain a production environment comprehensive curve model; performing abnormal correlation analysis based on the comprehensive curve model in combination with historical production environment data, and regulating and controlling the production line environment data according to the analysis data; and displaying the monitoring data of each environment device through a visual port, and regulating and controlling the real-time production line environment data based on the abnormal correlation analysis data of the comprehensive curve model.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a freezing and fresh-keeping production line detection system and method based on the Internet of Things. Background Art

[0002] IoT-based frozen fresh-keeping production line detection uses advanced sensors, wireless communication technology, and cloud computing platforms to achieve real-time monitoring and intelligent control of key parameters such as temperature and humidity in the production line. By transmitting data to the cloud platform, managers can remotely monitor and adjust parameters in a timely manner to ensure product quality. Under current technical conditions, people have improved the traditional manual monitoring and management mode by introducing remote monitoring equipment, and monitor and adjust the environmental temperature data of the production line in real time through the cloud platform; however, the current remote monitoring mode can only collect actual temperature data through monitoring equipment, and make alarm judgments and adjustments through preset values; however, there are two ways of regulation, one is local monitoring and regulation, when there is a local abnormality, local regulation is carried out; the other is overall regulation, monitoring and adjusting the overall temperature; both of the above modes have defects, local regulation will cause excessive local temperature differences in the production line, resulting in unbalanced overall space temperature, which is not conducive to product storage; overall regulation will cause temperature monitoring to lag, and untimely regulation will cause product damage; the above modes are not smart enough and the above temperature controls are all range temperature controls, which cannot perform intelligent and precise temperature adjustments according to actual conditions, cannot perform correlation analysis on local temperature monitoring, and cannot perform intelligent environmental temperature control. Summary of the invention

[0003] The object of the present invention is to provide a freezing and fresh-keeping production line detection system and method based on the Internet of Things to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for detecting a freezing and fresh-keeping production line based on the Internet of Things, the method comprising the following steps: S100. Network and connect the production line environment monitoring equipment through the Internet of Things platform, and record the data of each device through networking by marking the device number; set up a monitoring window to collect the monitoring data of each device; S200, retrieve data from each monitoring device through the data analysis platform, comprehensively process the data collected by each device through data cleaning, and use the curve simulation model to smooth and connect the time frame data collected by each device to build a curve model; S300, constructing a control curve analysis model by mapping the data curve models of each device to the same coordinate system; performing difference fusion analysis on each curve by analyzing the trend difference degree of the control curve at each time point, and obtaining a comprehensive curve model of the production environment; performing abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulating the production line environment data according to the analysis data; S400, display the monitoring data of each environmental equipment of the production line through the visual port, and adjust the real-time production line environmental data based on the abnormal correlation analysis data of the comprehensive curve model.

[0005] The S100 connects the production line environment monitoring equipment in series through the Internet of Things platform, records the data of each device in a network by marking the device number; sets a monitoring window, and collects the monitoring data of each device in the following specific steps: S101. Log in to the IoT management platform of the frozen fresh-keeping production line to retrieve equipment data of the production line environment monitoring equipment, and connect each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, number each equipment, and build an equipment information column to record the information data of each equipment; the monitoring equipment is a temperature sensor monitoring equipment; the equipment information includes equipment model data, location data, working hours, etc.; S102, by setting a monitoring cycle window, the environmental data of the production line is monitored in real time through various environmental monitoring devices, and the monitoring data is collected and transmitted to the data analysis platform through the monitoring network; the environmental data is the environmental temperature data of the production line.

[0006] The specific steps of S200 for retrieving data from each monitoring device through the data analysis platform, comprehensively processing the data collected by each device through data cleaning, and using the curve simulation model to smooth and concatenate the time frame data collected by each device to construct a curve model are as follows: S201, the data analysis platform retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the number of each device; performs data cleaning processing on the time series temperature set corresponding to each device, and performs overall processing on the data of each time series temperature set by screening error data, missing data and compensating data; the screening of error data, missing data and compensating data is to screen and mark the abnormal error data and missing data in each time series temperature set, and fill the data by averaging the compensating data; the overall processing is to normalize the dimension length of the time series temperature set corresponding to each device; S202. Import the processed time series temperature set data of each device into simulation through the curve simulation model, import the time series temperature set data of the corresponding time point into time frame temperature data on the time axis, and connect the data in series through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

[0007] The S300 maps the data curve models of each device to the same coordinate system to construct a control curve analysis model; analyzes the trend difference degree of each time point of the control curve, performs difference fusion analysis on each curve, and obtains a comprehensive curve model of the production environment; performs abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulates the production line environment data according to the analysis data. The specific steps are as follows: S301. Based on the ambient temperature curve fitted by the time series temperature set of each device, the corresponding ambient temperature curve of each device is mapped to the same coordinate system by mapping the coordinate system, and a comparison curve analysis model is constructed; based on the comparison curve analysis model, a trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed, and the calculation formula is Cr(t)=m(t) / n(t); wherein Cr(t) is the coincidence rate of each curve value at the corresponding time point t; m(t) is the number of coincidences of each curve value at the corresponding time point t; n(t) is the total number of each curve value at the corresponding time point t; by setting the comparison coincidence rate threshold Cr(v), when the curve coincidence rate corresponding to the time point t Cr(t)≥Cr(v), the curve value of the coincidence point of each curve at the time t is taken as the comprehensive ambient temperature data; if Cr(t)<Cr(v), the trend difference analysis is performed on the corresponding curve values ​​at the time t, and the calculation formula is ; Wherein, TDV(z,t) is the trend difference value of the corresponding numbered z curve at time t; k(z,t) is the curve derivative of the corresponding numbered z curve at time t; ave[k(n,t)] is the mean of the corresponding derivatives of each curve at time t; n is the number of curves; p(z,t) is the curve value of the corresponding numbered z curve at time t; ave[p(n,t)] is the mean of the corresponding curve values ​​of each curve at time t; where z is [1,n]; Based on the curve values ​​corresponding to each curve at each time point, the curve value correlation analysis is performed, and the calculation formula is: ; Wherein, R(n,t) is the intra-group correlation value of the curve value on each curve at the corresponding time point t; combined with the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point, the corresponding curve values ​​at each time point are screened out; if TDV(z,t)>R(n,t), the curve value of the numbered z curve at the current time point t is judged to be deviation data, and it is screened out; if TDV(z,t)≤R(n,t), the curve value of the numbered z curve at the current time point t is judged to be normal data, and it is retained; the deviation data refers to the difference between the average level of the data in the group and the correlation between the data in the group, and there is a deviation effect between the data and the rest of the data in the group; Based on the curve value screening results at each time point, the retained data at each time point is averaged and the calculated result is the comprehensive ambient temperature data at the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, a comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; S302, based on the comprehensive temperature curve of the production line environment in the current cycle, by retrieving the normal temperature fluctuation curve of the historical freezing and fresh-keeping production line corresponding to the current cycle, the temperature data of the production line in the current cycle corresponding to each time point is compared and analyzed, and the calculation formula is: ; Among them, OD(t) is the difference between the comprehensive temperature curve of the production line environment in the current cycle and the curve value corresponding to the time t on the normal temperature fluctuation curve in the corresponding historical cycle; Y(min) and Y(max) are the maximum curve value and the minimum curve value of the comprehensive temperature curve of the production line environment in the current cycle; Q(min) and Q(max) are the maximum curve value and the minimum curve value of the normal temperature fluctuation curve in the corresponding historical cycle; Y(t) and Q(t) are the curve values ​​of the comprehensive temperature curve of the production line environment in the current cycle and the normal temperature fluctuation curve in the corresponding historical cycle at the time t. By comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve of the corresponding historical cycle, if Y(t)>Q(max) or Y(t)<Q(min) exists on the comprehensive temperature curve of the production line environment in the current cycle, an abnormal alarm is issued for the current production line environment temperature; if Y(t)∈[Q(min), Q(max)], the current production line environment is regulated and analyzed by comparing the historical data; if Y(t)>Q(t), the current production line environment temperature data is regulated by comparing the curve value at the corresponding time point with the difference value. The calculation formula is Y(t) g =Y(t)-Y(t)*OD(t); if Y(t)<Q(t), the current production line environment data is adjusted, and the calculation formula is Y(t) g=Y(t)+Y(t)*OD(t); if Y(t)=Q(t), keep the current production line ambient temperature data; where Y(t) g is the production environment temperature data after regulation; when the actual production line temperature data is greater than the fluctuation range of the ambient temperature data of the corresponding historical period, it means that the current ambient temperature data is abnormal and an alarm is required; if the current ambient temperature data is within the normal range but different from the historical data, it means that the influencing factors in the current environment are different from the historical environment. If it is greater than the historical data, it means that the current environmental factors have caused the actual ambient temperature to rise; if it is less than the historical data, it means that the current environmental factors have caused the actual ambient temperature to drop; the temperature is regulated by comparing the difference values; for the time point where the temperature regulation behavior occurs, a time observation interval is set, and no secondary temperature regulation is performed within the interval. It is used to observe the changing trend of the ambient temperature of the production line after regulation; when the observation interval is exceeded, the above comparative regulation analysis is repeated.

[0008] The S400 displays the monitoring data of each environmental device of the production line through the visual port, and based on the abnormal correlation analysis data of the comprehensive curve model, the specific steps of regulating the real-time production line environmental data are as follows: S401, using the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; S402, recording the curve analysis model data of each device, and outputting the abnormal environment alarm status and temperature control data of the current production line.

[0009] A freezing and fresh-keeping production line detection system based on the Internet of Things, the system includes an equipment networking module, a data curve processing module, a comprehensive analysis and control module, and a data feedback output module; The equipment networking module networks and connects the production line environment monitoring equipment through the Internet of Things platform, and records the data of each device by marking the equipment number; sets a monitoring window to collect the monitoring data of each device; the data curve processing module retrieves data from each monitoring device through the data analysis platform, comprehensively processes the collected data of each device through data cleaning, and uses the curve simulation model to smoothly connect the time frame data of the data collected by each device to construct a curve model; the comprehensive analysis and control module constructs a control curve analysis model by mapping the data curve model of each device to the same coordinate system; by analyzing the trend difference degree of the control curve at each time point, each curve is subjected to difference fusion analysis and processing to obtain a comprehensive curve model of the production environment; based on the comprehensive curve model and the historical production environment data, an abnormal correlation analysis is performed, and the production line environment data is regulated according to the analysis data; the feedback output module displays the monitoring data of each environmental device of the production line through the visual port, and regulates the real-time production line environment data based on the abnormal correlation analysis data of the comprehensive curve model.

[0010] The device networking module includes a device networking construction unit and a periodic data acquisition unit; The equipment networking construction unit retrieves equipment data of the production line environment monitoring equipment by logging into the Internet of Things management platform of the frozen fresh-keeping production line, and connects each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, each equipment is numbered and labeled, and an equipment information column is built to record the information data of each equipment; the monitoring equipment is a temperature sensing monitoring equipment; The periodic data acquisition unit sets a monitoring period window, monitors the production line environmental data in real time through various environmental monitoring devices, collects the monitoring data and transmits it to the data analysis platform through the monitoring network; the environmental data is the production line environmental temperature data.

[0011] The data curve processing module includes a data overall processing unit and a data curve fitting unit; The data analysis platform of the data coordination processing unit retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the number of each device; performs data cleaning processing on the time series temperature set corresponding to each device, and coordinates the data of each time series temperature set by screening out erroneous data, missing data and making up for data processing; The data curve fitting unit imports the time series temperature set data processed by each device into simulation through the curve simulation model, imports the time frame temperature data of the time series temperature set data of the corresponding time point on the time axis, and concatenates the data through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

[0012] The comprehensive analysis and control module includes a comprehensive environment temperature curve analysis unit and a control unit for control of environment temperature data; The comprehensive ambient temperature curve analysis unit is based on the ambient temperature curve fitted by the time series temperature set of each device, and the corresponding ambient temperature curve of each device is mapped to the same coordinate system by mapping the coordinate system to construct a comparison curve analysis model; based on the comparison curve analysis model, the trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the corresponding same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed; by setting a comparison coincidence rate threshold, the coincidence rate of the values ​​at the same time point is judged; based on the judgment result, the comprehensive ambient temperature data of each time point is analyzed; if the curve at the corresponding time point is greater than or equal to the value coincidence rate threshold, the corresponding The curve value of the overlapping point of the curve at the time point is the comprehensive ambient temperature data; if it is less than the value overlap rate threshold, the trend difference analysis is performed on each curve value; the curve value correlation analysis is performed based on the curve value of each curve at each time point; the curve value corresponding to each time point is screened out by combining the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point; based on the curve value screening result at each time point, the data retained at each time point is averaged, and the calculation result is the comprehensive ambient temperature data of the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, the comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; The control environment temperature data control unit is based on the comprehensive temperature curve of the production line environment in the current cycle, and by calling up the normal temperature fluctuation curve of the historical freezing and preservation production line corresponding to the current cycle, compares the difference value analysis of the temperature data of the production line in the current cycle corresponding to each time point; by comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve in the corresponding historical cycle, an alarm is issued for abnormal environment temperature data based on the comparison result; for normal environment temperature data, by comparing the environment temperature data at the corresponding historical moment, the environment temperature data of the current production line is controlled and analyzed based on the comparison result.

[0013] The data feedback output module includes a visualization output unit and a control alarm instruction output unit; The visual output unit uses the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; The control alarm instruction output unit records the curve analysis model data of each device and outputs the abnormal environment alarm situation and temperature control data of the current production line.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines the Internet of Things platform to carry out serial networking of the environmental monitoring equipment of the freezing and fresh-keeping production line, and comprehensively processes the series of temperature data collected by the equipment, performs curve fitting, comprehensive curve fusion analysis, and compares historical data for regulation and analysis, thereby realizing intelligent detection and regulation of the environmental data of the production line; the present invention performs curve fitting on the local temperature data of the production line and performs overlap rate and trend difference analysis by analyzing the curve values ​​at each time point, thereby realizing overall fusion analysis of the local temperature data of the production line; compares the historical temperature data curve with the current production line temperature data curve for comparison and difference analysis, and realizes real-time and accurate temperature regulation of the current production line environmental data in combination with abnormal judgment; the present invention combines the local temperatures of the production line for correlation analysis, obtains the comprehensive environmental temperature data of the production line through fusion analysis, improves the traditional local temperature mean calculation overall temperature method, and improves the accuracy; accurately controls the temperature by comparing historical data and combining with the comparison difference, thereby improving the rough method of traditional temperature range control, and can more intelligently improve the temperature control accuracy of the production line under different environmental factor scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a schematic structural diagram of a freezing and fresh-keeping production line detection system based on the Internet of Things. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example: Figure 1 As shown, the present invention provides a technical solution: A method for detecting a freezing and fresh-keeping production line based on the Internet of Things, the method comprising the following steps: S100. Network and connect the production line environment monitoring equipment through the Internet of Things platform, and record the data of each device through networking by marking the device number; set up a monitoring window to collect the monitoring data of each device; S200, retrieve data from each monitoring device through the data analysis platform, comprehensively process the data collected by each device through data cleaning, and use the curve simulation model to smooth and connect the time frame data collected by each device to build a curve model; S300, constructing a control curve analysis model by mapping the data curve models of each device to the same coordinate system; performing difference fusion analysis on each curve by analyzing the trend difference degree of the control curve at each time point, and obtaining a comprehensive curve model of the production environment; performing abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulating the production line environment data according to the analysis data; S400, display the monitoring data of each environmental equipment of the production line through the visual port, and adjust the real-time production line environmental data based on the abnormal correlation analysis data of the comprehensive curve model.

[0018] The S100 connects the production line environment monitoring equipment in series through the Internet of Things platform, records the data of each device in a network by marking the device number; sets a monitoring window, and collects the monitoring data of each device in the following specific steps: S101. Log in to the IoT management platform of the frozen fresh-keeping production line to retrieve equipment data of the production line environment monitoring equipment, and connect each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, number each equipment, and build an equipment information column to record the information data of each equipment; the monitoring equipment is a temperature sensor monitoring equipment; S102, by setting a monitoring cycle window, the environmental data of the production line is monitored in real time through various environmental monitoring devices, and the monitoring data is collected and transmitted to the data analysis platform through the monitoring network; the environmental data is the environmental temperature data of the production line.

[0019] The specific steps of S200 for retrieving data from each monitoring device through the data analysis platform, comprehensively processing the data collected by each device through data cleaning, and using the curve simulation model to smooth and concatenate the time frame data collected by each device to construct a curve model are as follows: S201, the data analysis platform retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the equipment number; performs data cleaning processing on the time series temperature set corresponding to each equipment, and performs overall processing on the data of each time series temperature set by screening out erroneous data, missing data and supplementing data processing; S202. Import the processed time series temperature set data of each device into simulation through the curve simulation model, import the time series temperature set data of the corresponding time point into time frame temperature data on the time axis, and connect the data in series through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

[0020] The S300 maps the data curve models of each device to the same coordinate system to construct a control curve analysis model; analyzes the trend difference degree of each time point of the control curve, performs difference fusion analysis on each curve, and obtains a comprehensive curve model of the production environment; performs abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulates the production line environment data according to the analysis data. The specific steps are as follows: S301. Based on the ambient temperature curve fitted by the time series temperature set of each device, the corresponding ambient temperature curve of each device is mapped to the same coordinate system by mapping the coordinate system, and a comparison curve analysis model is constructed; based on the comparison curve analysis model, a trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed, and the calculation formula is Cr(t)=m(t) / n(t); wherein Cr(t) is the coincidence rate of each curve value at the corresponding time point t; m(t) is the number of coincidences of each curve value at the corresponding time point t; n(t) is the total number of each curve value at the corresponding time point t; by setting the comparison coincidence rate threshold Cr(v), when the curve coincidence rate corresponding to the time point t Cr(t)≥Cr(v), the curve value of the coincidence point of each curve at the time t is taken as the comprehensive ambient temperature data; if Cr(t)<Cr(v), the trend difference analysis is performed on the corresponding curve values ​​at the time t, and the calculation formula is ; Among them, TDV(z,t) is the trend difference value of the corresponding numbered curve z at time t; k(z,t) is the curve derivative of the corresponding numbered curve z at time t; ave[k(n,t)] is the mean of the corresponding derivatives of each curve at time t; n is the number of curves; p(z,t) is the curve value of the corresponding numbered curve z at time t; ave[p(n,t)] is the mean of the corresponding curve values ​​of each curve at time t; Based on the curve values ​​corresponding to each curve at each time point, the curve value correlation analysis is performed, and the calculation formula is: ; Among them, R(n,t) is the intra-group correlation value of the curve value on each curve at the corresponding time point t; combined with the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point, the corresponding curve values ​​at each time point are screened out; if TDV(z,t)>R(n,t), the curve value of the numbered z curve at the current time point t is judged to be deviation data, and it is screened out; if TDV(z,t)≤R(n,t), the curve value of the numbered z curve at the current time point t is judged to be normal data and is retained; Based on the curve value screening results at each time point, the retained data at each time point is averaged and the calculated result is the comprehensive ambient temperature data at the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, a comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; S302, based on the comprehensive temperature curve of the production line environment in the current cycle, by retrieving the normal temperature fluctuation curve of the historical freezing and fresh-keeping production line corresponding to the current cycle, the temperature data of the production line in the current cycle corresponding to each time point is compared and analyzed, and the calculation formula is: ; Among them, OD(t) is the difference between the comprehensive temperature curve of the production line environment in the current cycle and the curve value corresponding to the time t on the normal temperature fluctuation curve in the corresponding historical cycle; Y(min) and Y(max) are the maximum curve value and the minimum curve value of the comprehensive temperature curve of the production line environment in the current cycle; Q(min) and Q(max) are the maximum curve value and the minimum curve value of the normal temperature fluctuation curve in the corresponding historical cycle; Y(t) and Q(t) are the curve values ​​of the comprehensive temperature curve of the production line environment in the current cycle and the normal temperature fluctuation curve in the corresponding historical cycle at the time t. By comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve of the corresponding historical cycle, if Y(t)>Q(max) or Y(t)<Q(min) exists on the comprehensive temperature curve of the production line environment in the current cycle, an abnormal alarm is issued for the current production line environment temperature; if Y(t)∈[Q(min), Q(max)], the current production line environment is regulated and analyzed by comparing the historical data; if Y(t)>Q(t), the current production line environment temperature data is regulated by comparing the curve value at the corresponding time point with the difference value. The calculation formula is Y(t) g =Y(t)-Y(t)*OD(t); if Y(t)<Q(t), the current production line environment data is adjusted, and the calculation formula is Y(t) g =Y(t)+Y(t)*OD(t); if Y(t)=Q(t), keep the current production line ambient temperature data; where Y(t) g It is the temperature data of the production environment after regulation.

[0021] The S400 displays the monitoring data of each environmental device of the production line through the visual port, and based on the abnormal correlation analysis data of the comprehensive curve model, the specific steps of regulating the real-time production line environmental data are as follows: S401, using the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; S402, recording the curve analysis model data of each device, and outputting the abnormal environment alarm status and temperature control data of the current production line.

[0022] A freezing and fresh-keeping production line detection system based on the Internet of Things, the system includes an equipment networking module, a data curve processing module, a comprehensive analysis and control module, and a data feedback output module; The equipment networking module networks and connects the production line environment monitoring equipment through the Internet of Things platform, and records the data of each device by marking the equipment number; sets a monitoring window to collect the monitoring data of each device; the data curve processing module retrieves data from each monitoring device through the data analysis platform, comprehensively processes the collected data of each device through data cleaning, and uses the curve simulation model to smoothly connect the time frame data of the data collected by each device to construct a curve model; the comprehensive analysis and control module constructs a control curve analysis model by mapping the data curve model of each device to the same coordinate system; by analyzing the trend difference degree of the control curve at each time point, each curve is subjected to difference fusion analysis and processing to obtain a comprehensive curve model of the production environment; based on the comprehensive curve model and the historical production environment data, an abnormal correlation analysis is performed, and the production line environment data is regulated according to the analysis data; the feedback output module displays the monitoring data of each environmental device of the production line through the visual port, and regulates the real-time production line environment data based on the abnormal correlation analysis data of the comprehensive curve model.

[0023] The device networking module includes a device networking construction unit and a periodic data acquisition unit; The equipment networking construction unit retrieves equipment data of the production line environment monitoring equipment by logging into the Internet of Things management platform of the frozen fresh-keeping production line, and connects each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, each equipment is numbered and labeled, and an equipment information column is built to record the information data of each equipment; the monitoring equipment is a temperature sensing monitoring equipment; The periodic data acquisition unit sets a monitoring period window, monitors the production line environmental data in real time through various environmental monitoring devices, collects the monitoring data and transmits it to the data analysis platform through the monitoring network; the environmental data is the production line environmental temperature data.

[0024] The data curve processing module includes a data overall processing unit and a data curve fitting unit; The data analysis platform of the data coordination processing unit retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the number of each device; performs data cleaning processing on the time series temperature set corresponding to each device, and coordinates the data of each time series temperature set by screening out erroneous data, missing data and making up for data processing; The data curve fitting unit imports the time series temperature set data processed by each device into simulation through the curve simulation model, imports the time frame temperature data of the time series temperature set data of the corresponding time point on the time axis, and concatenates the data through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

[0025] The comprehensive analysis and control module includes a comprehensive environment temperature curve analysis unit and a control unit for control of environment temperature data; The comprehensive ambient temperature curve analysis unit is based on the ambient temperature curve fitted by the time series temperature set of each device, and the corresponding ambient temperature curve of each device is mapped to the same coordinate system by mapping the coordinate system to construct a comparison curve analysis model; based on the comparison curve analysis model, the trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the corresponding same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed; by setting a comparison coincidence rate threshold, the coincidence rate of the values ​​at the same time point is judged; based on the judgment result, the comprehensive ambient temperature data of each time point is analyzed; if the curve at the corresponding time point is greater than or equal to the value coincidence rate threshold, the corresponding The curve value of the overlapping point of the curve at the time point is the comprehensive ambient temperature data; if it is less than the value overlap rate threshold, the trend difference analysis is performed on each curve value; the curve value correlation analysis is performed based on the curve value of each curve at each time point; the curve value corresponding to each time point is screened out by combining the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point; based on the curve value screening result at each time point, the data retained at each time point is averaged, and the calculation result is the comprehensive ambient temperature data of the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, the comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; The control environment temperature data control unit is based on the comprehensive temperature curve of the production line environment in the current cycle, and by calling up the normal temperature fluctuation curve of the historical freezing and preservation production line corresponding to the current cycle, compares the difference value analysis of the temperature data of the production line in the current cycle corresponding to each time point; by comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve in the corresponding historical cycle, an alarm is issued for abnormal environment temperature data based on the comparison result; for normal environment temperature data, by comparing the environment temperature data at the corresponding historical moment, the environment temperature data of the current production line is controlled and analyzed based on the comparison result.

[0026] The data feedback output module includes a visualization output unit and a control alarm instruction output unit; The visual output unit uses the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; The control alarm instruction output unit records the curve analysis model data of each device and outputs the abnormal environment alarm situation and temperature control data of the current production line; In the example: At present, a frozen product manufacturing enterprise adopts the Internet of Things-based frozen preservation production line detection system of the present invention to carry out environmental monitoring and adjustment of its frozen preservation production line; by logging into the Internet of Things management platform of the frozen preservation production line, the equipment data of the production line environmental monitoring equipment is retrieved, and each monitoring equipment is connected in series to the same supervision network by building an equipment association network; in the supervision network, each equipment is numbered and marked, and an equipment information column is built to record the information data of each equipment, wherein the monitoring equipment is a temperature sensing monitoring equipment; by setting a monitoring cycle window, the production line environmental data is monitored in real time by each environmental monitoring equipment, and the monitoring data is collected and transmitted to the data analysis platform through the monitoring network, wherein the environmental data is the production line environmental temperature data; The data analysis platform retrieves the cycle ambient temperature data collected from the production line and builds a time series temperature set based on the equipment number. It cleans the corresponding time series temperature set of each equipment and processes the data of each time series temperature set in a coordinated manner by screening out erroneous data, missing data and making up for data. It imports the processed time series temperature set data of each equipment into the simulation through the curve simulation model, imports the time frame temperature data of the time series temperature set data of the corresponding time point on the time axis, and connects the data in series through a smooth curve to fit the monitoring ambient temperature curve within the corresponding equipment cycle. Based on the ambient temperature curve fitted by the time series temperature set of each device, the corresponding ambient temperature curve of each device is mapped to the same coordinate system through the mapping coordinate system to construct a comparison curve analysis model; based on the comparison curve analysis model, the trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the corresponding same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed, and the calculation formula is Cr(t)=m(t) / n(t); by setting the comparison coincidence rate threshold Cr(v), when the curve coincidence rate corresponding to the time point t Cr(t)≥Cr(v), the curve value of the coincidence point of each curve at time t is used as the comprehensive ambient temperature data; if Cr(t)<Cr(v), the trend difference analysis is performed on the corresponding curve values ​​at time t, and the calculation formula is ; Based on the curve values ​​corresponding to each curve at each time point, the curve value correlation analysis is performed, and the calculation formula is: ; Combined with the trend difference analysis data of the curve values ​​of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point, the corresponding curve values ​​at each time point are screened out; if TDV(z,t)>R(n,t), the curve value of the numbered z curve at the current time point t is judged to be deviation data, and it is screened out; if TDV(z,t)≤R(n,t), the curve value of the numbered z curve at the current time point t is judged to be normal data and is retained; based on the curve value screening results at each time point, the retained data at each time point is averaged, and the calculation result is the comprehensive ambient temperature data of the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, the corresponding production line environment comprehensive temperature curve in the current cycle is constructed; Based on the comprehensive temperature curve of the production line environment in the current cycle, the normal temperature fluctuation curve of the historical frozen preservation production line corresponding to the current cycle is retrieved, and the temperature data of the production line in the current cycle corresponding to each time point is compared and analyzed. The calculation formula is: ; By comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve of the corresponding historical cycle, if Y(t)>Q(max) or Y(t)<Q(min) exists on the comprehensive temperature curve of the production line environment in the current cycle, an abnormal alarm is issued for the current production line environment temperature; if Y(t)∈[Q(min), Q(max)], the current production line environment is regulated and analyzed by comparing the historical data; if Y(t)>Q(t), the current production line environment temperature data is regulated by comparing the curve value at the corresponding time point with the difference value. The calculation formula is Y(t) g =Y(t)-Y(t)*OD(t); if Y(t)<Q(t), the current production line environment data is adjusted, and the calculation formula is Y(t) g =Y(t)+Y(t)*OD(t); if Y(t)=Q(t), keep the current production line ambient temperature data; use the IoT visualization window to display the ambient temperature monitoring data of each monitoring point on the frozen preservation production line in real time; record the curve analysis model data of each device, and output the abnormal environmental alarm status and temperature control data of the current production line.

[0027] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for detecting a freezing and fresh-keeping production line based on the Internet of Things, characterized in that: The method comprises the following steps: S100. Network and connect the production line environment monitoring equipment through the Internet of Things platform, and record the data of each device through networking by marking the device number; set up a monitoring window to collect the monitoring data of each device; S200, retrieve data from each monitoring device through the data analysis platform, comprehensively process the data collected by each device through data cleaning, and use the curve simulation model to smooth and connect the time frame data collected by each device to build a curve model; S300, constructing a control curve analysis model by mapping the data curve models of each device to the same coordinate system; performing difference fusion analysis on each curve by analyzing the trend difference degree of the control curve at each time point, and obtaining a comprehensive curve model of the production environment; performing abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulating the production line environment data according to the analysis data; S400, display the monitoring data of each environmental equipment of the production line through the visual port, and adjust the real-time production line environmental data based on the abnormal correlation analysis data of the comprehensive curve model.

2. According to claim 1, a method for detecting a freezing and fresh-keeping production line based on the Internet of Things is characterized in that: The S100 connects the production line environment monitoring equipment in series through the Internet of Things platform, records the data of each device in a network by marking the device number; sets a monitoring window, and collects the monitoring data of each device in the following specific steps: S101. Log in to the IoT management platform of the frozen fresh-keeping production line to retrieve equipment data of the production line environment monitoring equipment, and connect each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, number each equipment, and build an equipment information column to record the information data of each equipment; the monitoring equipment is a temperature sensor monitoring equipment; S102, by setting a monitoring cycle window, the environmental data of the production line is monitored in real time through various environmental monitoring devices, and the monitoring data is collected and transmitted to the data analysis platform through the monitoring network; the environmental data is the environmental temperature data of the production line.

3. The method for detecting a freezing and fresh-keeping production line based on the Internet of Things according to claim 2, characterized in that: The specific steps of S200 for retrieving data from each monitoring device through the data analysis platform, comprehensively processing the data collected by each device through data cleaning, and using the curve simulation model to smooth and concatenate the time frame data collected by each device to construct a curve model are as follows: S201, the data analysis platform retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the equipment number; performs data cleaning processing on the time series temperature set corresponding to each equipment, and performs overall processing on the data of each time series temperature set by screening out erroneous data, missing data and supplementing data processing; S202. Import the processed time series temperature set data of each device into simulation through the curve simulation model, import the time series temperature set data of the corresponding time point into time frame temperature data on the time axis, and connect the data in series through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

4. The method for detecting a freezing and fresh-keeping production line based on the Internet of Things according to claim 3, characterized in that: The S300 maps the data curve models of each device to the same coordinate system to construct a control curve analysis model; analyzes the trend difference degree of each time point of the control curve, performs difference fusion analysis on each curve, and obtains a comprehensive curve model of the production environment; performs abnormal correlation analysis based on the comprehensive curve model combined with historical production environment data, and regulates the production line environment data according to the analysis data. The specific steps are as follows: S301. Based on the ambient temperature curve fitted by the time series temperature set of each device, the corresponding ambient temperature curve of each device is mapped to the same coordinate system by mapping the coordinate system, and a comparison curve analysis model is constructed; based on the comparison curve analysis model, a trend difference analysis is performed based on the ambient temperature curve values ​​of each device at each time point, and the values ​​of each curve at the same time point are marked, and the coincidence rate of the values ​​of each curve at the same time point is analyzed, and the calculation formula is Cr(t)=m(t) / n(t); wherein Cr(t) is the coincidence rate of each curve value at the corresponding time point t; m(t) is the number of coincidences of each curve value at the corresponding time point t; n(t) is the total number of each curve value at the corresponding time point t; by setting the comparison coincidence rate threshold Cr(v), when the curve coincidence rate corresponding to the time point t Cr(t)≥Cr(v), the curve value of the coincidence point of each curve at the time t is taken as the comprehensive ambient temperature data; if Cr(t)<Cr(v), the trend difference analysis is performed on the corresponding curve values ​​at the time t, and the calculation formula is ； Among them, TDV(z,t) is the trend difference value of the corresponding numbered curve z at time t; k(z,t) is the curve derivative of the corresponding numbered curve z at time t; ave[k(n,t)] is the mean of the corresponding derivatives of each curve at time t; n is the number of curves; p(z,t) is the curve value of the corresponding numbered curve z at time t; ave[p(n,t)] is the mean of the corresponding curve values ​​of each curve at time t; Based on the curve values ​​corresponding to each curve at each time point, the curve value correlation analysis is performed, and the calculation formula is: ； Among them, R(n,t) is the intra-group correlation value of the curve value on each curve at the corresponding time point t; combined with the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group correlation data of each curve value at the corresponding time point, the corresponding curve values ​​at each time point are screened out; if TDV(z,t)>R(n,t), the curve value of the numbered z curve at the current time point t is judged to be deviation data, and it is screened out; if TDV(z,t)≤R(n,t), the curve value of the numbered z curve at the current time point t is judged to be normal data and is retained; Based on the curve value screening results at each time point, the retained data at each time point is averaged and the calculated result is the comprehensive ambient temperature data at the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, a comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; S302, based on the comprehensive temperature curve of the production line environment in the current cycle, by retrieving the normal temperature fluctuation curve of the historical freezing and fresh-keeping production line corresponding to the current cycle, the temperature data of the production line in the current cycle corresponding to each time point is compared and analyzed, and the calculation formula is: ； Among them, OD(t) is the difference between the comprehensive temperature curve of the production line environment in the current cycle and the curve value corresponding to the time t on the normal temperature fluctuation curve in the corresponding historical cycle; Y(min) and Y(max) are the maximum curve value and the minimum curve value of the comprehensive temperature curve of the production line environment in the current cycle; Q(min) and Q(max) are the maximum curve value and the minimum curve value of the normal temperature fluctuation curve in the corresponding historical cycle; Y(t) and Q(t) are the curve values ​​of the comprehensive temperature curve of the production line environment in the current cycle and the normal temperature fluctuation curve in the corresponding historical cycle at the time t. By comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve of the corresponding historical cycle, if Y(t)>Q(max) or Y(t)<Q(min) exists on the comprehensive temperature curve of the production line environment in the current cycle, an abnormal alarm is issued for the current production line environment temperature; if Y(t)∈[Q(min), Q(max)], the current production line environment is regulated and analyzed by comparing the historical data; if Y(t)>Q(t), the current production line environment temperature data is regulated by comparing the curve value at the corresponding time point with the difference value. The calculation formula is Y(t) g =Y(t)-Y(t)*OD(t); if Y(t)<Q(t), the current production line environment data is adjusted, and the calculation formula is Y(t) g =Y(t)+Y(t)*OD(t); if Y(t)=Q(t), keep the current production line ambient temperature data; where Y(t) g It is the temperature data of the production environment after regulation.

5. The method for detecting a freezing and fresh-keeping production line based on the Internet of Things according to claim 4, characterized in that: The S400 displays the monitoring data of each environmental device of the production line through the visual port, and based on the abnormal correlation analysis data of the comprehensive curve model, the specific steps of regulating the real-time production line environmental data are as follows: S401, using the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; S402, recording the curve analysis model data of each device, and outputting the abnormal environment alarm status and temperature control data of the current production line.

6. A freezing and fresh-keeping production line detection system based on the Internet of Things, characterized by: The system includes an equipment networking module, a data curve processing module, a comprehensive analysis and control module, and a data feedback output module; The equipment networking module networks and connects the production line environment monitoring equipment through the Internet of Things platform, and records the data of each device by marking the equipment number; sets a monitoring window to collect the monitoring data of each device; the data curve processing module retrieves data from each monitoring device through the data analysis platform, comprehensively processes the collected data of each device through data cleaning, and uses the curve simulation model to smoothly connect the time frame data of the data collected by each device to construct a curve model; the comprehensive analysis and control module constructs a control curve analysis model by mapping the data curve model of each device to the same coordinate system; by analyzing the trend difference degree of the control curve at each time point, each curve is subjected to difference fusion analysis and processing to obtain a comprehensive curve model of the production environment; based on the comprehensive curve model and the historical production environment data, an abnormal correlation analysis is performed, and the production line environment data is regulated according to the analysis data; the feedback output module displays the monitoring data of each environmental device of the production line through the visual port, and regulates the real-time production line environment data based on the abnormal correlation analysis data of the comprehensive curve model.

7. The detection system for a freezing and fresh-keeping production line based on the Internet of Things according to claim 6 is characterized in that: The device networking module includes a device networking construction unit and a periodic data acquisition unit; The equipment networking construction unit retrieves equipment data of the production line environment monitoring equipment by logging into the Internet of Things management platform of the frozen fresh-keeping production line, and connects each monitoring equipment in series to the same supervision network by building an equipment association network; in the supervision network, each equipment is numbered and labeled, and an equipment information column is built to record the information data of each equipment; the monitoring equipment is a temperature sensing monitoring equipment; The periodic data acquisition unit sets a monitoring period window, monitors the production line environmental data in real time through various environmental monitoring devices, collects the monitoring data and transmits it to the data analysis platform through the monitoring network; the environmental data is the production line environmental temperature data.

8. The detection system for a freezing and fresh-keeping production line based on the Internet of Things according to claim 7 is characterized in that: The data curve processing module includes a data overall processing unit and a data curve fitting unit; The data analysis platform of the data coordination processing unit retrieves the cycle ambient temperature collection data of the production line and constructs a time series temperature set based on the number of each device; performs data cleaning processing on the time series temperature set corresponding to each device, and coordinates the data of each time series temperature set by screening out erroneous data, missing data and making up for data processing; The data curve fitting unit imports the time series temperature set data processed by each device into simulation through the curve simulation model, imports the time frame temperature data of the time series temperature set data of the corresponding time point on the time axis, and concatenates the data through a smooth curve to fit the monitoring environment temperature curve within the corresponding device cycle.

9. The detection system for a freezing and fresh-keeping production line based on the Internet of Things according to claim 8, characterized in that: The comprehensive analysis and control module includes a comprehensive environment temperature curve analysis unit and a control unit for control of environment temperature data; The comprehensive ambient temperature curve analysis unit is based on the ambient temperature curve fitted by the time series temperature set of each device, and the corresponding ambient temperature curve of each device is plane-mapped to the same coordinate system through the mapping coordinate system to construct a control curve analysis model; based on the control curve analysis model, the trend difference analysis is performed based on the ambient temperature curve value corresponding to each device at each time point, and the value of each curve at the corresponding same time point is marked, and the value overlap rate of each curve at the same time point is analyzed; by setting a comparison overlap rate threshold, the value overlap rate at the same time point is judged; based on the judgment result, the comprehensive ambient temperature data of each time point is analyzed; if the curve at the corresponding time point is greater than or equal to the value overlap rate threshold, the curve value of the overlap point of the curve at the corresponding time point is used as the comprehensive ambient temperature data; if it is less than the value overlap rate threshold, the trend difference analysis is performed on each curve value; based on the curve value corresponding to each curve at each time point, the curve value association analysis is performed; combined with the trend difference analysis data of the curve value of each curve at the corresponding time point and the intra-group association data of each curve value at the corresponding time point, the corresponding curve value at each time point is screened out; Based on the curve value screening results at each time point, the retained data at each time point is averaged and the calculated result is the comprehensive ambient temperature data at the current corresponding time point; by smoothing the comprehensive ambient temperature data at each time point, a comprehensive temperature curve of the production line environment corresponding to the current cycle is constructed; The control environment temperature data control unit is based on the comprehensive temperature curve of the production line environment in the current cycle, and by calling up the normal temperature fluctuation curve of the historical freezing and preservation production line corresponding to the current cycle, compares the difference value analysis of the temperature data of the production line in the current cycle corresponding to each time point; by comparing the comprehensive temperature curve of the production line environment in the current cycle with the normal temperature fluctuation curve in the corresponding historical cycle, an alarm is issued for abnormal environment temperature data based on the comparison result; for normal environment temperature data, by comparing the environment temperature data at the corresponding historical moment, the environment temperature data of the current production line is controlled and analyzed based on the comparison result.

10. The detection system for a freezing and fresh-keeping production line based on the Internet of Things according to claim 9, characterized in that: The data feedback output module includes a visualization output unit and a control alarm instruction output unit; The visual output unit uses the Internet of Things visualization window to display the environmental temperature monitoring data of each monitoring point on the freezing and fresh-keeping production line in real time; The control alarm instruction output unit records the curve analysis model data of each device and outputs the abnormal environment alarm situation and temperature control data of the current production line.

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