Food production safety monitoring system

Abstract

The invention relates to the technical field of production data processing, in particular to a food production safety monitoring system, which comprises a production data acquisition module used for acquiring image information, equipment operation information and workshop environment information of a food production workshop in real time; the image analysis module is used for dividing equipment areas and analyzing the safety degree of the image areas; the environment analysis module is used for analyzing the workshop environment parameters according to the workshop environment information; the equipment analysis module is used for analyzing the equipment operation parameters and the associated influence parameters; the safety analysis module is used for analyzing the production safety degree according to the workshop environment information, the image area safety degree, the workshop environment parameters, the equipment operation parameters and the associated influence parameters; and the safety monitoring module is used for analyzing the production safety state according to the production safety degree and outputting the production safety state. According to the invention, accurate monitoring of food production safety is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of production data processing, and in particular to a food production safety monitoring system. Background Art

[0002] With the expansion of food production and circulation, and the increase in consumer attention to food safety, food safety issues have become increasingly complex. At present, new technologies such as big data, artificial intelligence, and the Internet of Things are gradually being applied to the field of food safety monitoring, improving the accuracy and efficiency of monitoring through real-time monitoring, data analysis, and early warning. These technologies not only ensure the safety of the entire food chain from raw materials to finished products, but also promote the healthy development of the food industry.

[0003] Chinese Patent Publication No.: CN104881728A discloses a food production traceability monitoring system service platform, including 10 subsystems: central monitoring system, mixing room control system, mixing room feeding / return frame system, hoist control system, storage control management system, storage frame scheduling management system, workshop feeding / return frame control system, dumping management control system, feeding trolley automatic control system, chicken bouillon production control system; also includes a system for centralized real-time production process data collection, wherein the data collection includes production output, equipment status, fault records, fault handling countermeasures, user authority management, equipment operation authority management, equipment operation parameter storage, report automatic / manual printing, product whole box and small package serial number automatic scanning record database and a central control room for realizing production process visualization. This invention only realizes the safety analysis of equipment operation data in food production, and does not realize the comprehensive analysis of image data, equipment operation data, environmental data and cleaning data in the food production workshop. There are problems of low efficiency in analyzing food production safety data and inaccurate food production safety monitoring. Summary of the invention

[0004] An object of the present invention is to provide a food production safety monitoring system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A food production safety monitoring system, comprising:

[0007] Production data acquisition module, used to obtain real-time workshop monitoring images, workshop UV images, equipment operation information and workshop environment information of food production workshops;

[0008] An image analysis module is used to divide the equipment area according to the workshop monitoring image of the food production workshop, and is also used to count the number of equipment abnormal points in the equipment area according to the workshop ultraviolet image, and is also used to analyze the safety of the image area according to the equipment area and the number of equipment abnormal points;

[0009] Environmental analysis module, used to analyze workshop environmental parameters based on workshop environmental information;

[0010] The equipment analysis module is used to analyze the equipment operation parameters according to the equipment operation information, and also to analyze the equipment change status according to the equipment operation parameters, and to analyze the associated influencing parameters according to the equipment change status and workshop environment information;

[0011] Safety analysis module, used to analyze production safety based on workshop environment information, image area safety, workshop environment parameters, equipment operation parameters and related influencing parameters;

[0012] The safety monitoring module is used to analyze the production safety status according to the production safety degree and output the production safety status.

[0013] Furthermore, the image analysis module is provided with a grayscale normalization unit, which is used to normalize the grayscale values ​​of the pixels in the workshop monitoring image through a normalization formula according to the grayscale value of each pixel in the workshop monitoring image.

[0014] Furthermore, the image analysis module is also provided with an abnormality statistics unit, which is used to compare the gray value L(x2, y2) of the pixel point in the equipment area of ​​the workshop ultraviolet image with the ultraviolet gray threshold α, and judge whether the pixel point in the ultraviolet image of the current analysis workshop is an abnormal pixel point according to the comparison result, and the abnormality statistics unit counts the number of abnormal pixel points in the ultraviolet image as the number of equipment abnormal points;

[0015] The image analysis module is also provided with a regional analysis unit, which is used to calculate the image area safety H(i, t) through a regional safety analysis formula according to the equipment area, the grayscale value of the pixels in the normalized workshop monitoring image and the number of equipment abnormal points.

[0016] Furthermore, the environmental analysis module calculates the workshop environmental parameter M(t) according to the workshop temperature and workshop humidity through the environmental analysis formula, and the environmental analysis module is provided with the following environmental analysis formula:

[0017]

[0018] Wherein, W1(t) represents the workshop temperature, W2(t) represents the workshop humidity, v represents the range parameter, v∈N and v≤V, and V represents the statistical range.

[0019] Furthermore, the equipment analysis module is provided with an operation analysis unit, which is used to calculate the equipment operation parameter A(i, t) according to the equipment operation information through the operation analysis formula. The operation analysis unit is provided with the following operation analysis formula:

[0020] A(i,t)=P(i,t)×lg[|Q(i,t)|+1] / p(i,t);

[0021] Among them, A(i,t) represents the equipment operating parameters, Q(i,t) represents the operating temperature, and p(i,t) represents the rated power.

[0022] Furthermore, the equipment analysis module is further provided with a variation analysis unit, which is used to compare the operation fluctuation parameter a(i, t) with the fluctuation comparison threshold β, and determine the equipment variation state according to the comparison result, and the equipment variation state includes: fluctuation and stability;

[0023] The equipment analysis module is also provided with a correlation analysis unit, which is used to compare the workshop environment parameter M(t) with the operation fluctuation parameter a(i, t) when the equipment change state is fluctuating, and to analyze the correlation influence parameter B(i, t) when the workshop environment parameter is greater than the operation fluctuation parameter, and set B(i, t) = M(t) / a(i, t).

[0024] Furthermore, the safety analysis module is provided with a safety analysis unit, which is used to calculate the production safety degree through a safety degree analysis formula according to the image area safety degree, workshop environmental parameters and equipment operation parameters. The safety analysis unit is provided with a safety degree analysis formula as follows:

[0025] S(i,t)=[A(i,t)×M(t)] H(i,t) ;

[0026] Among them, S(i,t) represents the production safety.

[0027] Furthermore, the safety analysis module is also provided with a cleaning analysis unit, which is used to adjust the analysis process of production safety according to the cleaning time of the workshop, the air quality of the workshop and the water quality of the workshop. If F < f and the air quality of the workshop is excellent or good and the water quality of the workshop is excellent or good, the cleaning analysis unit does not adjust the analysis process of production safety; if F < f and the air quality of the workshop is poor or the water quality of the workshop is poor, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i, t). If F ≥ f and the air quality of the workshop is good or bad, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i, t). If the air quality in the workshop is excellent and the water quality in the workshop is excellent, the cleaning analysis unit does not adjust the analysis process of the production safety. If F≥f and the air quality in the workshop is good and the water quality in the workshop is good, the cleaning analysis unit adjusts the analysis process of the production safety, and the adjusted production safety is S1(i, t). If F≥f and the air quality in the workshop is poor or the water quality in the workshop is poor, the cleaning analysis unit adjusts the analysis process of the production safety, and the adjusted production safety is S1(i, t); wherein F represents the cleaning time of the workshop, and f represents the cleaning time threshold.

[0028] Furthermore, the safety analysis module is also provided with an impact analysis unit, which is used to compare the associated impact parameter M(t) with the impact comparison threshold m, and optimize the adjustment process of the production safety degree according to the comparison result. When the associated impact parameter does not meet the threshold, the adjustment process of the production safety degree is optimized, and the optimized production safety degree is S2(i, t).

[0029] Furthermore, the safety monitoring module compares the production safety level with the safety level threshold, and determines the production safety status according to the comparison result, and the production safety status includes: high risk, medium risk and low risk.

[0030] The beneficial effects of the present invention are as follows: through the acquisition of image information, equipment operation information and workshop environment information of the food production workshop by the production data acquisition module, and the comprehensive analysis of the acquired data by other modules, comprehensive monitoring and analysis of equipment operation data, environmental data and image data in the food production process can be realized, thereby analyzing the production safety and food safety of the food production workshop, realizing the analysis of production safety, and then improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 Schematic diagram of the structure of the food production safety monitoring system of this embodiment.

[0033] Figure 2 Schematic diagram of the structure of the image analysis module of this embodiment.

[0034] Figure 3 Schematic diagram of the structure of the equipment analysis module in this embodiment.

[0035] Figure 4 This is a schematic diagram of the structure of the security analysis module of this embodiment. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0037] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0038] See also Figure 1 As shown, it is a food production safety monitoring system of this embodiment, including:

[0039] The production data acquisition module is used to obtain the image information, equipment operation information and workshop environment information of the food production workshop in real time. The image information includes the workshop monitoring image and the workshop ultraviolet image. The workshop monitoring image is the image of the food production workshop taken by the monitoring camera in the food production workshop. The workshop ultraviolet image is the ultraviolet image of the food production workshop taken by the ultraviolet camera in the food production workshop. The shooting angles of the workshop monitoring image and the workshop ultraviolet image should be kept consistent so that the pixel points corresponding to the positions of each area in the image are consistent. The equipment operation information includes equipment power, rated power and operating temperature. The workshop environment information includes workshop cleaning time, workshop temperature, workshop humidity, workshop air quality and workshop water quality. The workshop cleaning time is the cleaning time after the workshop cleaning work is carried out. When the workshop cleaning work is carried out, the workshop air quality and the workshop water quality are measured. During the workshop cleaning work, the workshop cleaning time is set to 0, and the unit of the workshop cleaning time is hours. The workshop air quality includes poor, good and excellent, and the workshop water quality includes poor, good and excellent. The workshop air quality can be evaluated based on the data such as the particle size, particle concentration and gas concentration in the workshop air, and the air quality can also be determined by installing an air sampler to collect and analyze microorganisms in the air. The workshop water quality can be determined by comparing the light transmittance, pH value and ion content of the water measured in the food production workshop with the water quality data stipulated by the state. The equipment operation information is obtained by importing and obtaining through the equipment operation data management platform in the food production workshop, and the workshop environment information is obtained by uploading through sensors or by user interaction;

[0040] An image analysis module, used to divide the equipment area according to the workshop monitoring image of the food production workshop, and also used to count the number of equipment abnormal points in the equipment area according to the workshop ultraviolet image, and also used to analyze the safety of the image area according to the equipment area and the number of equipment abnormal points, the image analysis module is connected to the production data acquisition module;

[0041] Specifically, when analyzing image information, the image analysis module described in this embodiment takes the lower left corner pixel point of each image as the coordinate origin, and takes the two sides adjacent to the coordinate origin as the x-axis and y-axis respectively. The x-axis increases from left to right, and the y-axis increases from bottom to top. The unit length is 1 pixel. A plane rectangular coordinate system is established, and the coordinate points are used to represent the position of each pixel in the image.

[0042] See also Figure 2 As shown, the image analysis module includes:

[0043] The area division unit is used to mark the equipment area in the workshop monitoring image, realize the recognition and extraction of the target area in the monitoring image, and determine the area that needs to be monitored;

[0044] Specifically, the area division unit described in this embodiment compares the preset equipment contour with each pixel point in the workshop monitoring image to determine the equipment position in the workshop monitoring image, and takes the area covered by the equipment position in the workshop monitoring image as the equipment area; the equipment area in the workshop monitoring image is marked by the area division unit to extract the pixel point area corresponding to the target equipment in the monitoring image, thereby ensuring the accuracy of the system analysis of equipment samples, improving the efficiency of the system analysis of food production safety data, and improving the accuracy of food production safety monitoring; it can be understood that the present embodiment does not make specific limitations on the division method of the equipment area, and technical personnel in this field can freely set it, such as it can also be set to manually mark the equipment area in the workshop monitoring image or identify the equipment area by inputting the workshop monitoring image into the target detection algorithm.

[0045] Please see Continue Reading Figure 2 As shown, the image analysis module also includes:

[0046] The grayscale normalization unit is used to perform a normalization operation on the grayscale values ​​of pixels in the workshop monitoring image, and the grayscale normalization unit is connected to the area division unit.

[0047] Specifically, the grayscale normalization unit in this embodiment performs a normalization operation on the grayscale value of each pixel in the workshop monitoring image through a normalization formula according to the grayscale value of each pixel in the workshop monitoring image. The grayscale normalization unit is provided with a normalization formula as follows:

[0048] R(x1,y1)=[L(x1,y1)-L(x1,y1) min ] / [L(x1,y1) max -L(x1,y1) min ];

[0049] Among them, R(x1,y1) represents the grayscale value of the pixel in the normalized workshop monitoring image, (x1,y1) represents the coordinates of the pixel in the workshop monitoring image, L(x1,y1) represents the grayscale value of the pixel in the workshop monitoring image, and L(x1,y1) min Indicates the minimum grayscale value of the pixel in the workshop monitoring image, L(x1,y1) max Represents the maximum value of the grayscale value of the pixel in the workshop monitoring image; the grayscale normalization unit normalizes the grayscale value of the pixel in the workshop monitoring image to narrow the data fluctuation range and reduce the impact of fluctuations in various image parameters on image data analysis, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring.

[0050] Please see Continue Reading Figure 2 As shown, the image analysis module also includes:

[0051] The abnormality statistics unit is used to count the number of abnormal points of equipment in the ultraviolet image of the workshop, and the number of abnormal points of equipment is used to represent the number of abnormal pixels in the ultraviolet image, so as to monitor the operation of the equipment according to the ultraviolet image. The abnormality statistics unit is connected to the area division unit.

[0052] Specifically, the abnormality statistics unit described in this embodiment compares the grayscale value of the pixel point in the equipment area of ​​the workshop ultraviolet image with the ultraviolet grayscale threshold, and analyzes the number of equipment abnormal points according to the comparison result. If L(x2,y2)>α and (x2,y2)∈U(i), the abnormality statistics unit determines that the pixel point in the ultraviolet image of the workshop currently being analyzed is an abnormal pixel point. Otherwise, the abnormality statistics unit determines that the pixel point in the ultraviolet image of the workshop currently being analyzed is a normal pixel point. The abnormality statistics unit counts the number of abnormal pixels in the ultraviolet image as the number of equipment abnormal points, wherein L(x2,y2) represents the grayscale value of the pixel point in the ultraviolet image of the workshop, (x2,y2) represents the coordinates of the pixel point in the ultraviolet image of the workshop, α represents the ultraviolet grayscale threshold, 210≤α≤240, U(i) represents the set of pixel point coordinates in the equipment area, i represents the equipment number, i∈N + . The device number is defined as a number used to distinguish different devices in each image. The abnormal statistical unit compares and analyzes the grayscale of the pixels in the device area in the ultraviolet image to analyze the abnormal pixels, thereby improving the efficiency of the system in analyzing food production safety data and improving the accuracy of food production safety monitoring; the number of device abnormal points is the number of abnormal pixels in each device area. It can be understood that in this embodiment, the value of the ultraviolet grayscale threshold is not specifically limited, and technicians in this field can set it freely as long as the statistics of the number of abnormal pixels are satisfied. The optimal value of the ultraviolet grayscale threshold is: α=220.

[0053] Please see Continue Reading Figure 2 As shown, the image analysis module also includes:

[0054] The area analysis unit is used to analyze the safety of the image area, and the image area safety degree is used to represent the safety of the area where the device is located based on the image data. The area analysis unit is connected to the grayscale normalization unit and the abnormality statistics unit.

[0055] Specifically, the regional analysis unit in this embodiment calculates the image regional safety degree according to the equipment area, the gray value of the pixel points in the normalized workshop monitoring image and the number of equipment abnormal points through the regional safety analysis formula. The regional analysis unit is provided with a regional safety analysis formula as follows:

[0056]

[0057] Where H(i,t) represents the security of the image area, t represents the analysis cycle number, and t∈N + , NU(i) represents the number of pixels in the equipment area, and NL(i,t) represents the number of equipment abnormal points. The analysis cycle number is defined as the number that distinguishes the time of data collection. In this embodiment, the collection time interval is 1 second; the regional analysis unit analyzes the equipment area, the grayscale value of the pixels in the normalized workshop monitoring image, and the number of equipment abnormal points to analyze the regional safety degree, and realizes the comprehensive analysis of the monitoring image and the ultraviolet image, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring.

[0058] Please continue reading Figure 1 As shown, the food production safety monitoring system also includes:

[0059] The environment analysis module is used to analyze the workshop environment parameters according to the workshop environment information, and the environment analysis module is connected to the production data acquisition module.

[0060] Specifically, the environmental analysis module in this embodiment calculates the workshop environmental parameters according to the workshop temperature and workshop humidity through the environmental analysis formula. The environmental analysis module is provided with the following environmental analysis formula:

[0061]

[0062] Wherein, M(t) represents the workshop environment parameter, W1(t) represents the workshop temperature, W2(t) represents the workshop humidity, v represents the range parameter, v∈N and v≤V, V represents the statistical range, 10≤V≤30. Specifically, in this embodiment, the environment analysis module analyzes the workshop environment information to analyze the workshop environment parameters, and uses the workshop environment parameters to represent the characteristics of workshop environment data changes in a short period of time, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring. It can be understood that in this embodiment, the value of the statistical range is not specifically limited, and it only needs to meet the analysis of the workshop environment parameters. The optimal value of the statistical range is: V=15.

[0063] Please continue reading Figure 1 As shown, the food production safety monitoring system also includes:

[0064] An equipment analysis module, used to analyze equipment operation parameters according to equipment operation information, and also used to analyze equipment change status according to equipment operation parameters, and to analyze associated influencing parameters according to equipment change status and workshop environment information, the equipment analysis module being connected to the environment analysis module;

[0065] See also Figure 3 As shown, the device analysis module includes:

[0066] An operation analysis unit is used to analyze the equipment operation parameters, and use the equipment operation parameters to represent the characteristic relationship between the equipment operation data, so as to realize the unified analysis of the equipment operation data;

[0067] Specifically, the operation analysis unit in this embodiment calculates the equipment operation parameters according to the equipment operation information through the operation analysis formula. The operation analysis unit is provided with the following operation analysis formula:

[0068] A(i,t)=P(i,t)×lg[|Q(i,t)|+1] / p(i,t);

[0069] Wherein, A(i, t) represents the equipment operating parameters, Q(i, t) represents the operating temperature, and p(i, t) represents the rated power. The operation analysis unit analyzes the equipment operating information to analyze the equipment operating parameters, and analyzes the characteristic relationship between the equipment operating data and the standard rated operating data. The analysis magnitude of the data is determined according to the operating temperature of the equipment, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring.

[0070] See also Figure 3 As shown, the device analysis module also includes:

[0071] A change analysis unit is used to analyze the change status of the equipment, classify the equipment into two categories according to the change status of the data, and determine the fluctuation status of the equipment data in a short period of time. The change analysis unit is connected to the operation analysis unit;

[0072] Specifically, the change analysis unit in this embodiment analyzes the equipment change state according to the equipment operation parameters. If a(i, t) ≥ β, the change analysis unit determines that the equipment change state in the current analysis period is fluctuating; if a(i, t) < β, the change analysis unit determines that the equipment change state in the current analysis period is stable; wherein a(i, t) represents the operation fluctuation parameter, and setting β represents the fluctuation comparison threshold, 0.05≤β≤0.1. It can be understood that the value of the fluctuation comparison threshold is not specifically limited in this embodiment, and can be freely set by those skilled in the art, as long as it satisfies the analysis of the equipment change state. The optimal value of the fluctuation comparison threshold is: β=0.05.

[0073] See also Figure 3 As shown, the device analysis module also includes:

[0074] A correlation analysis unit, used for analyzing correlation influence parameters, using correlation influence parameters to represent the change characteristic relationship between the equipment operation data and the environmental data, and the correlation analysis unit is connected to the change analysis unit;

[0075] Specifically, when the equipment change state is fluctuating, the correlation analysis unit in this embodiment analyzes the correlation influence parameters according to the workshop environmental parameters. If M(t)>a(i,t), the correlation analysis unit sets the correlation influence parameter to B(i,t), and sets B(i,t)=M(t) / a(i,t); if M(t)≤a(i,t), the correlation analysis unit does not analyze the correlation influence parameter. The correlation analysis unit analyzes the equipment change state and the workshop environmental parameters to analyze the correlation influence parameters, and realizes a comprehensive analysis of the environmental data and the equipment operation data, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring.

[0076] Please continue reading Figure 1 As shown, the food production safety monitoring system also includes:

[0077] A safety analysis module, used to analyze the production safety according to the workshop environment information, the image area safety, the workshop environment parameters, the equipment operation parameters and the associated influencing parameters, the safety analysis module is connected with the image analysis module and the equipment analysis module;

[0078] See also Figure 4 As shown, the security analysis module includes:

[0079] Safety analysis unit, used to analyze production safety according to image area safety, workshop environment parameters and equipment operation parameters, and use production safety to represent the safety status of equipment operation in each time period in the food production workshop;

[0080] Specifically, the safety analysis unit in this embodiment calculates the production safety degree through a safety degree analysis formula according to the image area safety degree, workshop environment parameters and equipment operation parameters. The safety analysis unit is provided with a safety degree analysis formula as follows:

[0081] S(i,t)=[A(i,t)×M(t)] H(i,t) ;

[0082] Among them, S(i,t) represents the production safety degree; the safety analysis unit analyzes the safety degree of the image area, the workshop environmental parameters and the equipment operation parameters to analyze the production safety degree, and realizes the analysis of the equipment production safety, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring.

[0083] See also Figure 4 As shown, the security analysis module also includes:

[0084] The cleaning analysis unit is used to adjust the analysis process of the production safety degree so that the adjusted production safety degree is related to the cleaning data of the food production workshop. The cleaning analysis unit is connected to the safety analysis unit.

[0085] Specifically, the cleaning analysis unit in this embodiment adjusts the analysis process of production safety according to the cleaning time of the workshop, the air quality of the workshop and the water quality of the workshop. If F < f and the air quality of the workshop is excellent or good and the water quality of the workshop is excellent or good, the cleaning analysis unit does not adjust the analysis process of production safety; if F < f and the air quality of the workshop is poor or the water quality of the workshop is poor, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i, t), and S1(i, t) = S(i, t) × (1-G) is set F If F≥f and the air quality of the workshop is excellent and the water quality of the workshop is excellent, the clean analysis unit does not adjust the analysis process of the production safety degree. If F≥f and the air quality of the workshop is good and the water quality of the workshop is good, the clean analysis unit adjusts the analysis process of the production safety degree. The adjusted production safety degree is S1(i,t). Set S1(i,t)=S(i,t)×(1+G) F If F≥f and the workshop air quality is poor or the workshop water quality is poor, the clean analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i,t), setting S1(i,t)=S(i,t)×(1-2×G) F ; Wherein, F represents the cleaning time of the workshop, f represents the cleaning time threshold, 3≤f≤6, G represents the cleaning parameter, 0.02≤G≤0.05; the cleaning analysis unit analyzes the cleaning time of the workshop, the air quality of the workshop and the water quality of the workshop to adjust the analysis process of the production safety, and realizes the comprehensive analysis of the equipment operation and environmental cleaning data on production safety, thereby improving the system's analysis efficiency of food production safety data and improving the accuracy of food production safety monitoring; it can be understood that in this embodiment, the values ​​of the cleaning time threshold and the cleaning parameters are not specifically limited, and technical personnel in this field can set them freely, as long as the adjustment of the production safety is satisfied. The optimal values ​​of the cleaning time threshold and the cleaning parameters are: f=4, G=0.03.

[0086] See also Figure 4 As shown, the security analysis module also includes:

[0087] The impact analysis unit is used to optimize the adjustment process of the production safety degree so that the optimized production safety degree is related to the environmental data changes in a short period of time. The impact analysis unit is connected to the cleaning analysis unit.

[0088] Specifically, the impact analysis unit in this embodiment optimizes the adjustment process of the production safety degree according to the associated impact parameters. If M(t)≤m, the impact analysis unit determines that the associated impact parameters meet the threshold and does not optimize the adjustment process of the production safety degree. If M(t)>m, the impact analysis unit determines that the associated impact parameters do not meet the threshold and optimizes the adjustment process of the production safety degree. The optimized production safety degree is S2(i,t), and S2(i,t)=S1(i,t)×M(t)×B(i,t) / m is set; wherein, m represents the impact comparison threshold, 0.08≤m≤0.12; it can be understood that in this embodiment, the value of the impact comparison threshold is not specifically limited, and technical personnel in this field can set it freely, as long as the optimization of the production safety adjustment process is satisfied. The optimal value of the impact comparison threshold is: m=0.1.

[0089] Please continue reading Figure 1 As shown, the food production safety monitoring system also includes:

[0090] The safety monitoring module is used to analyze the production safety status according to the production safety degree and output the production safety status. The safety monitoring module is connected to the safety analysis module.

[0091] Specifically, the safety monitoring module described in this embodiment compares the production safety with the safety threshold, and analyzes the production safety status according to the comparison result. If S(i, t)≤s1, the safety monitoring module determines that the production safety status is high risk, if s1<S(i, t)<s2, the safety monitoring module determines that the production safety status is medium risk, if S(i, t)≥s2, the safety monitoring module determines that the production safety status is low risk; wherein, s1 represents the first safety threshold, 0.5≤s1≤0.7, s2 represents the second safety threshold, 1.2≤s2≤1.4. It can be understood that the value of the safety threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the production safety status. The optimal value of the safety threshold is: s1=0.6, s2=1.3.

[0092] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A food production safety monitoring system, characterized in that: include: Production data acquisition module, used to obtain real-time workshop monitoring images, workshop UV images, equipment operation information and workshop environment information of food production workshops; An image analysis module is used to divide the equipment area according to the workshop monitoring image of the food production workshop, and is also used to count the number of equipment abnormal points in the equipment area according to the workshop ultraviolet image, and is also used to analyze the safety of the image area according to the equipment area and the number of equipment abnormal points; Environmental analysis module, used to analyze workshop environmental parameters based on workshop environmental information; The equipment analysis module is used to analyze the equipment operation parameters according to the equipment operation information, and also to analyze the equipment change status according to the equipment operation parameters, and to analyze the associated influencing parameters according to the equipment change status and workshop environment information; Safety analysis module, used to analyze production safety based on workshop environment information, image area safety, workshop environment parameters, equipment operation parameters and related influencing parameters; The safety monitoring module is used to analyze the production safety status according to the production safety degree and output the production safety status.

2. The food production safety monitoring system according to claim 1, characterized in that: The image analysis module is provided with a grayscale normalization unit, which is used to normalize the grayscale values ​​of the pixels in the workshop monitoring image through a normalization formula according to the grayscale value of each pixel in the workshop monitoring image.

3. The food production safety monitoring system according to claim 2, characterized in that: The image analysis module is also provided with an abnormality statistics unit, which is used to compare the gray value L(x2, y2) of the pixel point in the equipment area of ​​the workshop ultraviolet image with the ultraviolet gray threshold α, and judge whether the pixel point in the ultraviolet image of the current analysis workshop is an abnormal pixel point according to the comparison result, and the abnormality statistics unit counts the number of abnormal pixel points in the ultraviolet image as the number of equipment abnormal points; The image analysis module is also provided with a regional analysis unit, which is used to calculate the image area safety H(i, t) through a regional safety analysis formula according to the equipment area, the grayscale value of the pixels in the normalized workshop monitoring image and the number of equipment abnormal points.

4. The food production safety monitoring system according to claim 3, characterized in that: The environmental analysis module calculates the workshop environmental parameter M(t) according to the workshop temperature and workshop humidity through the environmental analysis formula. The environmental analysis module is provided with the following environmental analysis formula: Wherein, W1(t) represents the workshop temperature, W2(t) represents the workshop humidity, v represents the range parameter, v∈N and v≤V, and V represents the statistical range.

5. The food production safety monitoring system according to claim 4, characterized in that: The equipment analysis module is provided with an operation analysis unit, which is used to calculate the equipment operation parameter A(i, t) according to the equipment operation information through the operation analysis formula. The operation analysis unit is provided with the operation analysis formula as follows: A(i,t)=P(i,t)×lg[|Q(i,t)|+1] / p(i,t); Among them, A(i,t) represents the equipment operating parameters, Q(i,t) represents the operating temperature, and p(i,t) represents the rated power.

6. The food production safety monitoring system according to claim 5, characterized in that: The equipment analysis module is further provided with a variation analysis unit, which is used to compare the operation fluctuation parameter a(i, t) with the fluctuation comparison threshold β, and determine the equipment variation state according to the comparison result, and the equipment variation state includes: fluctuation and stability; The equipment analysis module is also provided with a correlation analysis unit, which is used to compare the workshop environment parameter M(t) with the operation fluctuation parameter a(i, t) when the equipment change state is fluctuating, and to analyze the correlation influence parameter B(i, t) when the workshop environment parameter is greater than the operation fluctuation parameter, and set B(i, t) = M(t) / a(i, t).

7. The food production safety monitoring system according to claim 6, characterized in that: The safety analysis module is provided with a safety analysis unit, which is used to calculate the production safety degree through a safety degree analysis formula according to the image area safety degree, workshop environment parameters and equipment operation parameters. The safety analysis unit is provided with a safety degree analysis formula as follows: S(i,t)=[A(i,t)×M(t)] H(i,t) ; Among them, S(i,t) represents the production safety.

8. The food production safety monitoring system according to claim 7, characterized in that: The safety analysis module is also provided with a cleaning analysis unit, which is used to adjust the analysis process of production safety according to the cleaning time of the workshop, the air quality of the workshop and the water quality of the workshop. If F < f and the air quality of the workshop is excellent or good and the water quality of the workshop is excellent or good, the cleaning analysis unit does not adjust the analysis process of production safety; if F < f and the air quality of the workshop is poor or the water quality of the workshop is poor, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1 (i, t). If F ≥ f and the air quality of the workshop is If F≥f and the air quality of the workshop is good and the water quality of the workshop is good, the cleaning analysis unit does not adjust the analysis process of production safety. If F≥f and the air quality of the workshop is poor or the water quality of the workshop is poor, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i, t); if F≥f and the air quality of the workshop is poor or the water quality of the workshop is poor, the cleaning analysis unit adjusts the analysis process of production safety, and the adjusted production safety is S1(i, t); wherein F represents the cleaning time of the workshop, and f represents the cleaning time threshold.

9. The food production safety monitoring system according to claim 8, characterized in that: The safety analysis module is also provided with an impact analysis unit, which is used to compare the associated impact parameter M(t) with the impact comparison threshold m, and optimize the adjustment process of the production safety degree according to the comparison result. When the associated impact parameter does not meet the threshold, the adjustment process of the production safety degree is optimized, and the optimized production safety degree is S2(i,t).

10. The food production safety monitoring system according to claim 9, characterized in that: The safety monitoring module compares the production safety level with the safety level threshold, and determines the production safety status according to the comparison result. The production safety status includes: high risk, medium risk and low risk.

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