An enterprise safety production management method

Abstract

The present invention discloses an enterprise safety production management method, which relates to the technical field of safety production, and includes: Step 1, obtaining food processing information, obtaining the processing information of the food processing production line; Step 2, processing the food processing information, according to the processing information of the food processing production line, processing to obtain the pollution degree judgment value and the microorganism growth judgment value of the food processing production line, and judging whether the food processing production line needs to be cleaned and disinfected; Step 3, generating a cleaning and disinfection plan, if the food processing production line needs to be cleaned, determining the cleaning plan of the food processing production line, if the food processing production line needs to be disinfected, determining the disinfection plan of the food processing production line; Step 4, display processing, displaying the cleaning and disinfection plan to the management personnel of the food processing production line, so that the cleaning and disinfection work is more in line with the actual pollution situation of the production line, and improving the cleaning and disinfection effect of the food processing production line.

Description

Technical Field

[0001] The present invention relates to the technical field of work safety, and particularly relates to a work safety management method for enterprises. Background Art

[0002] In today's society, the food industry is booming, and the efficient operation of food processing production lines provides people with a rich variety of food choices. However, with the increasing attention paid to food safety issues, the production safety management of food processing production lines has become increasingly prominent, which is crucial for consumers' health, the survival of enterprises, and social stability. Therefore, it is extremely necessary to manage the production of food processing production lines.

[0003] The prior art, such as an invention patent application with publication number CN116453292B, discloses a method for classifying and warning production safety risks in a chemical enterprise, which specifically includes: obtaining a personnel risk coefficient based on the proportion of staff who do not correctly use safety tools. Obtaining a monitoring risk coefficient for different risk sources based on the environmental monitoring data of the risk source area, and obtaining a risk source risk coefficient based on the proportion of risk sources whose monitoring risk coefficient is greater than the monitoring risk design value. Obtaining an operation risk coefficient based on the operation data of chemical production equipment, and obtaining an equipment risk coefficient based on the operation risk coefficient and the proportion of fire hydrants with abnormal water pressure. Based on the personnel risk coefficient, equipment risk coefficient, and risk source risk coefficient, an evaluation model is constructed to obtain a safety risk coefficient, and the production safety risk level is classified based on the safety risk coefficient, and whether to give a warning is determined according to the production safety risk level, thereby further improving the safety of production.

[0004] The prior art, such as an invention patent application with publication number CN111695743B, discloses a method, device, equipment, and storage medium for monitoring the production safety of factory-made food. The method includes: starting a central monitoring model and a raw material source control model to monitor the procurement of production raw material sources. Before processing production, determining the quality level of the obtained food raw materials to be processed and predicting the quality level of the food products. Judging whether the predicted quality level of the food products meets a preset threshold. If it meets, processing production is carried out. During the entire process of processing production, obtaining the scoring index value when each step of the process flow is completed. Comprehensively evaluating the completion quality of the food products, classifying the food products into different grades, and sending the classification results to the central monitoring model to complete the monitoring of food production safety. This application can effectively monitor the production of food from the overall production process and improve the qualification rate of food quality.

[0005] Combining the above solutions, it can be found that there are still deficiencies in the prior art, which are specifically reflected in the following aspects: The cleaning or disinfection of traditional food processing production lines mostly relies on fixed time or empirical arrangements, limited to fixed cleaning and disinfection cycles or empirical operations, lacking in-depth excavation of historical processing information and using it as a basis for judgment and evaluation, making it difficult for the cleaning and disinfection work to fit the actual pollution situation and microbial growth risk of the production line. At the same time, when evaluating the pollution and microbial growth of food processing production lines, multiple factors such as the type of processed food, processing volume, processing time interval, and temperature and humidity of the processing environment are not considered, making it difficult to ensure the comprehensiveness of the analysis of the pollution and microbial growth of food processing production lines, thus reducing the effectiveness of subsequent cleaning and disinfection of food processing production lines. Summary of the Invention

[0006] The purpose of the present invention is to provide an enterprise safety production management method, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an enterprise safety production management method, including: Step 1, obtaining food processing information, obtaining the processing information of the food processing production line.

[0008] Step 2, processing food processing information, processing the obtained processing information of the food processing production line to obtain the pollution degree judgment value and microbial growth judgment value of the food processing production line, and judging whether the food processing production line needs to be cleaned and disinfected.

[0009] Step 3, generating a cleaning and disinfection plan. If the food processing production line needs to be cleaned, determine the cleaning plan for the food processing production line; if the food processing production line needs to be disinfected, determine the disinfection plan for the food processing production line.

[0010] Step 4, display processing, displaying the cleaning and disinfection plan to the management personnel of the food processing production line.

[0011] The beneficial effects of the present invention are as follows: (1) The present invention deeply excavates historical processing information and uses it as a basis for judgment and evaluation, making the cleaning and disinfection work more in line with the actual pollution situation and microbial growth risk of the production line, making up for the deficiencies in the prior art, no longer limited to fixed cleaning and disinfection cycles or empirical operations, and realizing the transformation from "timing" to "on-demand".

[0012] (2) When the present invention evaluates the pollution and microbial growth of the food processing production line, it considers multiple factors such as the type of processed food, processing volume, processing time interval, and temperature and humidity of the processing environment, ensuring the comprehensiveness of the analysis of the pollution and microbial growth of the food processing production line, thereby improving the effectiveness of subsequent cleaning and disinfection of the food processing production line. Brief Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a flowchart of the method of the present invention. Specific embodiments

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0016] Refer to Figure 1 As shown, the present invention provides an enterprise safety production management method, including: Step 1, food processing information acquisition, obtaining the processing information of the food processing production line from the monitoring center of the food processing production line.

[0017] In a specific embodiment of the present invention, the processing information includes a historical processing data set, an operator behavior data set, and an environment data set.

[0018] The historical processing data set includes the types and quantities of foods processed at each time node.

[0019] The operator behavior data set includes the output times and average risk distances of each risk action type at each time node.

[0020] The environment data set includes the characteristic parameters of each environment data at each time node.

[0021] It should be noted that the monitoring center of the food production line integrates a variety of sensors for environmental monitoring to realize the monitoring of environmental data, including but not limited to temperature, humidity, wind speed, etc.

[0022] Step 2, food processing information processing, processing the processing information of the food processing production line to obtain a pollution degree judgment value and a microbial growth judgment value of the food processing production line, and judging whether the food processing production line needs to be cleaned and disinfected.

[0023] In a specific embodiment of the present invention, the method for determining the pollution degree value of the food processing production line is as follows: extract the historical processing data set, the operator behavior data set, and the environmental data set from the processing information of the food processing production line, and respectively determine the first pollution degree R1, the second pollution degree R2, and the third pollution degree R3 of the food processing production line.

[0024] If (R1 = 1) ∨ (R2 = 1) ∨ (R3 = 1), then record the pollution degree value of the food processing production line as 1, indicating that the food processing production line needs to be cleaned. ∨ represents the logical symbol "or".

[0025] If (R1 = -1) ∧ (R2 = -1) ∧ (R3 = -1), then record the pollution degree value of the food processing production line as -1, indicating that the food processing production line does not need to be cleaned. ∧ represents the logical symbol "and".

[0026] In a specific embodiment of the present invention, the method for determining the first pollution degree of the food processing production line is as follows: extract the food type and quantity processed at each time node from the historical processing data set of the food processing production line, and match the pollution characteristic parameter SE of the food type processed at each time node of the food processing production line from the pollution characteristic parameters corresponding to each food type stored in the web data warehouse. The pollution characteristic parameters corresponding to each food type include values from 0 to 1.

[0027] It should be noted that the pollution characteristic parameters corresponding to each food type specifically reflect the probability of each food type causing pollution during processing. For example, for fried foods, the oil has strong viscosity, adhesion, and poor fluidity, and is extremely likely to adhere to the surfaces of equipment and the inner walls of pipelines in the food processing production line, forming oil stains. For dairy products, proteins are prone to denaturation and coagulation under conditions such as heating, acid, and alkali, and adhere to the equipment surface to form stains. For confectionery foods, the sugar has good solubility and conditional viscosity, and will form relatively stubborn sticky stains only under specific conditions such as a very high sugar concentration and a low environmental humidity. Therefore, the pollution characteristic parameter of fried foods is greater than that of dairy products, and the pollution characteristic parameter of dairy products is greater than that of confectionery foods. For example, the pollution characteristic parameters of fried foods, dairy products, and confectionery foods are set to 0.9, 0.7, and 0.5 respectively.

[0028] According to the time nodes, in the plane rectangular coordinate system, draw the SE - t trend curve A and the SL - t trend curve B respectively, where t represents the time node number.

[0029] Perform correlation analysis on curves A and B. If curves A and B respectively meet two conditions, record the first pollution degree of the food processing production line as 1; otherwise, record it as -1. Condition 1 is that the number of vertices of curve A is greater than or equal to b, where b is a preset threshold. Condition 2 is that the overall trend of curve B is rising and the total processing quantity is greater than u, where u is the appropriate total processing quantity of the food processing production line stored in the web data warehouse.

[0030] For Condition 1, when the number of vertices of curve A is greater than or equal to b, assuming b = 0, it means that the types of foods processed by the processing production line fluctuate greatly. This situation implies a relatively high risk of food cross - contamination in the processing production line. Therefore, when the number of vertices of curve A is greater than or equal to b, it indicates a relatively high pollution risk of the food processing production line.

[0031] For Condition 2, when the overall trend of curve B is rising, it indicates that the processing volume of the food processing production line is increasing. At the same time, the total processing quantity is greater than u, indicating that the total processing quantity exceeds the appropriate total processing quantity of the food processing production line, and the pollution risk is relatively high.

[0032] It should be noted that before the food processing production line is put into use, pollutant tests are usually carried out. For example, different types of foods are continuously input for processing, and the volume of pollutants is continuously collected until it is necessary to perform cleaning. Record the total processing quantity of continuous input, and record the minimum total processing quantity of continuous input as the appropriate total processing quantity of the food processing production line.

[0033] In a specific embodiment of the present invention, the method for determining the second pollution degree of the food processing production line is as follows: Extract the output times and average risk distances of each risk action type at each time node from the operation behavior dataset of the food processing production line.

[0034] It should be noted that taking the center point of the occupied area of the food processing production line as the center point, a three - dimensional space coordinate system is established. The risk action types of the operator are captured by monitoring equipment, and the shortest distance between the locator worn by the operator and the food processing production line is recorded as the risk distance, so as to obtain the risk distances of each occurrence of each risk action type of the operator, and perform mean processing on them to obtain the average risk distance.

[0035] Import the output times and average risk distances of each risk action type at each time node of the food processing production line into the second pollution degree evaluation model to output the second pollution degree of the food processing production line.

[0036] In the formula respectively represent the existential quantifier and the universal quantifier, β _iis the action risk index at the i-th time node of the food processing production line, H′ _m 、JI′ _m 、β′ are respectively the allowed output times, the safe average distance, and the action risk index threshold of the m-th risk action type stored in the web data warehouse. i is the number of each time node, i = 1, 2,..., n, where n is an integer greater than 2, and m is the number of each risk action type, m = 1, 2,..., l, where l is an integer greater than 2.

[0037] In a specific embodiment of the present invention, the method for determining the third pollution degree of the food processing production line is as follows: Extract the characteristic parameters of each environmental data at each time node from the environmental data set of the food processing production line, and then combine the pollution-related environmental data stored in the web data warehouse to extract the characteristic parameters of each pollution-related environmental data at each time node of the food processing production line, and compare them with the safe characteristic parameter interval of each pollution-related environmental data stored in the web data warehouse. If the characteristic parameter of a certain pollution-related environmental data is not within the safe characteristic parameter interval, then mark this pollution-related environmental data as risk environmental data, and summarize to obtain each risk environmental data.

[0038] It should be noted that the pollution-related environmental data are specifically the environmental data related to pollution in the food processing production line, such as temperature, humidity, wind speed, air quality, floor dirt area, wall dirt area, etc.

[0039] It should be noted again that the characteristic parameter of each environmental data, for example, the characteristic parameter of temperature specifically refers to the average value of temperature.

[0040] Count the number of risk environmental data and the number of pollution-related environmental data at each time node of the food processing production line, and divide them to obtain the environmental risk ratio Ht at each time node of the food processing production line. In a rectangular coordinate system, fit the Ht - t trend line P and obtain the slope of the line P. When the slope of the line P is greater than or equal to the preset growth slope, mark the third pollution degree of the food processing production line as 1, otherwise, mark it as -1.

[0041] In a specific embodiment of the present invention, the method for determining the microbial growth judgment value of the food processing production line is as follows: Extract the historical processing data set, the operator behavior data set, and the environmental data set from the processing information of the food processing production line, and respectively determine the first microbial growth value E1, the second microbial growth value E2, and the third microbial growth value E3 of the food processing production line.

[0042] If (E1 = 1) ∨ (E2 = 1) ∨ (E3 = 1), then record the microbial growth judgment value of the food processing production line as 1, indicating that the food processing production line needs to be disinfected.

[0043] If (E1 = -1) ∧ (E2 = -1) ∧ (E3 = -1), then record the microbial growth judgment value of the food processing production line as -1, indicating that the food processing production line does not need to be disinfected.

[0044] It should be noted that the method for determining the first microbial growth value E1, the second microbial growth value E2, and the third microbial growth value E3 of the food processing production line is the same as the method for confirming the first contamination degree, the second contamination degree, and the third contamination degree.

[0045] When evaluating the contamination and microbial growth of the food processing production line in the present invention, considering multiple factors such as the type of processed food, the processing volume, the processing time interval, and the temperature and humidity of the processing environment ensures the comprehensiveness of the analysis of the contamination and microbial growth of the food processing production line, thereby improving the subsequent cleaning and disinfection effects of the food processing production line.

[0046] Step 3: Generate a cleaning and disinfection plan. If the food processing production line needs to be cleaned, determine the cleaning plan for the food processing production line. If the food processing production line needs to be disinfected, determine the disinfection plan for the food processing production line.

[0047] In a specific embodiment of the present invention, the method for specifically determining the cleaning plan for the food processing production line is as follows: According to the first contamination degree, the second contamination degree, and the third contamination degree of the food processing production line, when the first contamination degree, the second contamination degree, and the third contamination degree are all 1, record the cleaning rule code of the food processing production line as 111. When two of the first contamination degree, the second contamination degree, and the third contamination degree are 1, record the cleaning rule code of the food processing production line as 110. When one of the first contamination degree, the second contamination degree, and the third contamination degree is 1, record the cleaning rule code of the food processing production line as 100.

[0048] According to the cleaning rule code of the food processing production line, combined with the cleaning plans corresponding to each cleaning rule code stored in the web data warehouse, match to obtain the cleaning plan for the food processing production line. The cleaning plan includes the cleaning solution concentration, the cleaning duration, and the cleaning power.

[0049] It should be noted that the cleaning solutions corresponding to the respective cleaning rule codes are specifically obtained by training based on prior training data, where the prior training data includes the cleaning rule codes, cleaning solutions, and cleaning effect characterization values of each historical cleaning. The cleaning effect characterization values of each historical cleaning are normalized to obtain the processed cleaning effect characterization values of each historical cleaning, which are compared with the cleaning effect characterization value thresholds preset for the respective cleaning rule codes. If the cleaning effect characterization value of a certain historical cleaning is greater than or equal to the cleaning effect characterization value threshold, then this historical cleaning is recorded as a qualified historical cleaning, and the qualified historical cleanings of each time are summarized.

[0050] Based on the cleaning rule codes and cleaning solutions of each qualified cleaning, the qualified cleanings and cleaning solutions of each cleaning rule code are mapped, and the cleaning solutions of each qualified cleaning are averaged to obtain the cleaning solutions of each cleaning rule code.

[0051] The cleaning effect characterization value is specifically the pollutant parameters of the food processing production line identified by image recognition technology, where the pollutant parameters include the volumes of various pollutants. The total volume of pollutants is summarized, and the reciprocal is taken after adding it to the compensation value to obtain the cleaning effect characterization value.

[0052] In a specific embodiment of the present invention, the method for specifically determining the disinfection plan for the food processing production line is as follows: Based on the first microbial growth value, the second microbial growth value, and the third microbial growth value of the food processing production line, when the first microbial growth value, the second microbial growth value, and the third microbial growth value are all 1, the disinfection rule code of the food processing production line is recorded as 111; when two of the first microbial growth value, the second microbial growth value, and the third microbial growth value are 1, the disinfection rule code of the food processing production line is recorded as 110; when one of the first microbial growth value, the second microbial growth value, and the third microbial growth value is 1, the disinfection rule code of the food processing production line is recorded as 100.

[0053] Based on the historical processing dataset, the operator behavior dataset, and the environmental dataset of the food processing production line, the dominant microbial species of the food processing production line are determined.

[0054] Based on the dominant microbial species of the food processing production line, combined with the database of the ultraviolet sensitivity of the dominant microbial species stored in the web data warehouse, the ultraviolet sensitive wavelengths corresponding to the dominant microbial species of the food processing production line are screened out and used as the ultraviolet wavelengths.

[0055] According to the disinfection rule code and the dominant microbial species of the food processing production line, combined with the disinfection duration corresponding to each disinfection rule code for each dominant microbial species stored in the web data warehouse, the disinfection duration of the food processing production line is matched.

[0056] Generate a disinfection plan for the food processing production line based on the ultraviolet wavelength and disinfection duration of the food processing production line.

[0057] In a specific embodiment of the present invention, the method for determining the dominant microorganisms in the food processing production line is as follows: extract the dominant microorganism species, historical processing data sets, operator behavior data sets, and environmental data sets of each historical production from the prior data in the web data warehouse, and compare the historical processing data sets, operator behavior data sets, and environmental data sets of the food processing production line with them to obtain the processing similarity, operator behavior similarity, and environmental similarity between the food processing production line and each historical production. The overall similarity between the food processing production line and each historical production is obtained through mean processing.

[0058] It should be noted that for the two numerical data sets of the historical processing data set and the environmental data set, the similarity can be calculated by calculating the mean square error and then the root mean square error. The similarity is inversely proportional to the root mean square error. It can also be calculated by the mean absolute error, and the similarity is inversely proportional to the mean absolute error. Both are existing technologies and will not be elaborated here. For the data set of operator behavior as a set of keywords, the similarity of the operator behavior data set can be calculated through the Jaccard similarity coefficient, which is also an existing technology and will not be elaborated here.

[0059] If the overall similarity between the food processing production line and a certain historical production is the largest, then the dominant microorganism species of this historical production is used as the dominant microorganism species of the food processing production line.

[0060] The in-depth excavation of historical processing information in the present invention, taking it as the basis for judgment and evaluation, makes the cleaning and disinfection work more in line with the actual pollution situation and microbial breeding risk of the production line, making up for the deficiencies in the existing technology. It is no longer limited to a fixed cleaning and disinfection cycle or empirical operation, and realizes the transformation from "timing" to "on-demand".

[0061] Step Four: Display processing, display the cleaning and disinfection plan to the management personnel of the food processing production line.

[0062] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of the present technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for enterprise safety production management, characterized in that, Including: Step 1, obtaining food processing information, obtaining the processing information of the food processing production line; The processing information includes a historical processing data set, an operator behavior data set, and an environment data set; The historical processing data set includes the food types and quantities processed at each time node; The operator behavior data set includes the output times and average risk distances of each risk action type at each time node; The environment data set includes the characteristic parameters of each environmental data at each time node; Step 2, processing food processing information, processing the processing information of the food processing production line to obtain a pollution degree judgment value and a microbial growth judgment value of the food processing production line, and judging whether the food processing production line needs to be cleaned and disinfected; The method for specifically determining the pollution degree judgment value of the food processing production line is as follows: Extract the historical processing data set, the operator behavior data set, and the environment data set from the processing information of the food processing production line, and respectively determine the first pollution degree R1, the second pollution degree R2, and the third pollution degree R3 of the food processing production line; If (R1 = 1) ∨ (R2 = 1) ∨ (R3 = 1), then record the pollution degree judgment value of the food processing production line as 1, indicating that the food processing production line needs to be cleaned, and ∨ represents the logical symbol or; If (R1 = -1) ∧ (R2 = -1) ∧ (R3 = -1), then record the pollution degree judgment value of the food processing production line as -1, indicating that the food processing production line does not need to be cleaned, and ∧ represents the logical symbol and; The specific method for determining the first pollution degree of the food processing production line is as follows: Extract the food types and quantities processed at each time node from the historical processing data set of the food processing production line, and match the pollution characteristic parameters SE of the food types processed at each time node of the food processing production line from the pollution characteristic parameters corresponding to each food type stored in the web data warehouse. The pollution characteristic parameters corresponding to each food type include values between 0 and 1; According to the time node, in the plane rectangular coordinate system, draw the SE-t trend curve A and the SL-t trend curve B respectively, where t represents the time node number; Conduct correlation analysis on curves A and B. If curves A and B respectively meet two conditions, then record the first pollution degree of the food processing production line as 1, otherwise, record it as -1. Condition 1 is that the number of vertices of curve A is greater than or equal to b, where b is a preset threshold, and Condition 2 is that the overall trend of curve B rises and the total processing quantity is greater than u, where u is the appropriate total processing quantity of the food processing production line stored in the web data warehouse; The specific method for determining the second pollution degree of the food processing production line is as follows: Extract the output times and average risk distances of each risk action type at each time node from the operator behavior data set of the food processing production line; Import the output times and average risk distances of each risk action type at each time node of the food processing production line into the second pollution degree evaluation model to output the second pollution degree of the food processing production line; In the formula are respectively expressed as the existential quantifier and the universal quantifier,[[]] β _i is the action risk index at the i-th time node of the food processing production line, H′ _m , JI′ _m , β′ are respectively the allowable output times, the safety average distance, and the action risk index threshold of the m-th risk action type stored in the web data warehouse. i is the number of each time node, i = 1, 2,..., n, n is an integer greater than 2, m is the number of each risk action type, m = 1, 2,..., l, and l is an integer greater than 2; The specific method for determining the third pollution degree of the food processing production line is as follows: Extract the characteristic parameters of each environmental data at each time node from the environmental dataset of the food processing production line, and then combine with the pollution-related environmental data stored in the web data warehouse to extract the characteristic parameters of each pollution-related environmental data at each time node of the food processing production line, and compare them with the safety characteristic parameter intervals of the pollution-related environmental data stored in the web data warehouse. If the characteristic parameter of a pollution-related environmental data is not within the safety characteristic parameter interval, then mark this pollution-related environmental data as risk environmental data, and summarize to obtain each risk environmental data; Statistically obtain the quantity of risk environmental data and the quantity of pollution-related environmental data at each time node of the food processing production line, and perform division to obtain the environmental risk ratio Ht at each time node of the food processing production line. In the rectangular coordinate system, fit the Ht-t trend line P and obtain the slope of the line P. When the slope of the line P is greater than or equal to the preset growth slope, mark the third pollution degree of the food processing production line as 1, otherwise, mark it as -1; The determination method of the microbial growth judgment value of the food processing production line is as follows: Extract the historical processing dataset, the operator behavior dataset, and the environmental dataset from the processing information of the food processing production line, and respectively determine the first microbial growth value E1, the second microbial growth value E2, and the third microbial growth value E3 of the food processing production line; If (E1 = 1) ∨ (E2 = 1) ∨ (E3 = 1), then mark the microbial growth judgment value of the food processing production line as 1, indicating that the food processing production line needs to be disinfected; If (E1 = -1) ∧ (E2 = -1) ∧ (E3 = -1), then mark the microbial growth judgment value of the food processing production line as -1, indicating that the food processing production line does not need to be disinfected; Step 3: Generate a cleaning and disinfection plan. If the food processing production line needs to be cleaned, determine the cleaning plan of the food processing production line. If the food processing production line needs to be disinfected, determine the disinfection plan of the food processing production line; Step 4: Display processing. Display the cleaning and disinfection plan to the management personnel of the food processing production line.

2. The enterprise safety production management method according to claim 1, characterized in that The determination method of the cleaning plan of the food processing production line is as follows: According to the first pollution degree, the second pollution degree, and the third pollution degree of the food processing production line, when the first pollution degree, the second pollution degree, and the third pollution degree are all 1, then mark the cleaning rule code of the food processing production line as 111. When two of the first pollution degree, the second pollution degree, and the third pollution degree are 1, then mark the cleaning rule code of the food processing production line as 110. When one of the first pollution degree, the second pollution degree, and the third pollution degree is 1, then mark the cleaning rule code of the food processing production line as 100; According to the cleaning rule code of the food processing production line, combine with the cleaning plans corresponding to each cleaning rule code stored in the web data warehouse to match and obtain the cleaning plan of the food processing production line. The cleaning plan includes the cleaning liquid concentration, the cleaning duration, and the cleaning power.

3. The enterprise safety production management method according to claim 1, characterized in that The determination method of the disinfection plan of the food processing production line is as follows: Based on the first microbial growth value, the second microbial growth value, and the third microbial growth value of the food processing production line, when the first microbial growth value, the second microbial growth value, and the third microbial growth value are all 1, the disinfection rule code of the food processing production line is recorded as 111. When two of the first microbial growth value, the second microbial growth value, and the third microbial growth value are 1, the disinfection rule code of the food processing production line is recorded as 110. When one of the first microbial growth value, the second microbial growth value, and the third microbial growth value is 1, the disinfection rule code of the food processing production line is recorded as 100; Based on the historical processing data set, the operator behavior data set, and the environmental data set of the food processing production line, determine the dominant microbial species of the food processing production line; Based on the dominant microbial species of the food processing production line, combined with the ultraviolet sensitivity database of the dominant microbial species stored in the web data warehouse, screen out the ultraviolet sensitive wavelength corresponding to the dominant microbial species of the food processing production line, and use this as the ultraviolet wavelength; According to the disinfection rule code and the dominant microbial species of the food processing production line, combined with the disinfection duration corresponding to each disinfection rule code of each dominant microbial species stored in the web data warehouse, match and obtain the disinfection duration of the food processing production line; Generate a disinfection plan for the food processing production line according to the ultraviolet wavelength and the disinfection duration of the food processing production line.

4. The enterprise safety production management method according to claim 3, wherein, The specific determination method for determining the dominant microorganisms of the food processing production line is as follows: Extract the dominant microbial species, historical processing data set, operator behavior data set, and environmental data set of each historical production from the prior data in the web data warehouse, and compare the historical processing data set, operator behavior data set, and environmental data set of the food processing production line with them to obtain the processing similarity, operator behavior similarity, and environmental similarity between the food processing production line and each historical production. The average value is processed to obtain the overall similarity between the food processing production line and each historical production; If the overall similarity between the food processing production line and a certain historical production is the largest, then the dominant microbial species of this historical production is used as the dominant microbial species of the food processing production line.

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