Raw material inventory data processing method and system based on Internet of Things, and storage medium

Abstract

The invention relates to the technical field of raw material inventory management, and discloses a raw material inventory data processing method and system based on the Internet of Things and a storage medium, and the method comprises the steps: S1, collecting flow velocity data in a liquid raw material feeding process and real-time monitoring data of an environment where the liquid raw material is located; s2, performing state analysis on the liquid raw materials according to the flow velocity data and the types of the liquid raw materials, and performing early warning monitoring on different types of liquid raw materials according to a state analysis result and real-time monitoring data; and S3, adjusting and controlling the liquid raw material storage environment parameters according to the early warning monitoring result. By calculating the flow velocity data of different liquid raw materials, the state of the liquid raw materials can be judged, meanwhile, the liquid raw materials of different types and states can be comprehensively and deeply analyzed in combination with the obtained real-time monitoring data, and then the quality of the liquid raw materials in the storage process is guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of raw material inventory management, and in particular to a method, system and storage medium for raw material inventory data processing based on the Internet of Things. Background Technology

[0002] In the feed preparation industry, the inventory management of bulk liquids (such as liquid methionine, lysine, choline, antifungal agents, emulsifiers, oils, molasses, etc.) is rapidly developing from traditional manual operation to automation and intelligence. Through the deep integration of automated hardware, IoT sensors and digital management systems, real-time monitoring, precise control and data traceability of inventory can be achieved.

[0003] In existing technologies, IoT technology is used to acquire relevant environmental parameters of liquid raw materials during feed preparation. These parameters are then used to control the storage environment of the liquid raw materials. When environmental parameters exceed the set standards for the storage environment, timely warning messages are issued to remind warehouse management personnel to make adjustments and controls to ensure the quality of the liquid raw materials. However, different liquid raw materials have different physical properties, and the combined influence of different environmental parameters can also adversely affect the quality of liquid raw materials, leading to problems such as product failure, decreased purity, and sedimentation / stratification. Therefore, how to analyze the storage of different liquid raw materials based on IoT-collected data is the fundamental problem that this invention aims to solve. Summary of the Invention

[0004] To achieve comprehensive and in-depth analysis of the storage of different liquid raw materials, this application provides a raw material inventory data processing method based on the Internet of Things.

[0005] Firstly, this application provides a method for processing raw material inventory data based on the Internet of Things, employing the following technical solution: IoT-based raw material inventory data processing methods include: S1. Collect flow rate data and real-time monitoring data of the surrounding environment during the liquid raw material feeding process; S2. Perform state analysis on the liquid raw materials based on the flow rate data and the type of liquid raw materials, and perform early warning monitoring on different types of liquid raw materials based on the state analysis results and real-time monitoring data. S3. Adjust and control the environmental parameters of liquid raw material storage based on the early warning monitoring results.

[0006] Optionally, the process of performing state analysis on liquid feedstock includes: The flow rate data of the liquid raw material is compared with the test flow rate data under the same feed port specification to obtain the ratio of the flow rate data to the test flow rate data. Based on the test data, a ratio-viscosity mapping relationship is established for different types of liquid raw materials. The obtained ratio is substituted into the ratio-viscosity mapping relationship to obtain the viscosity of the liquid raw material of that type. The viscosity is used as the state analysis result of the liquid raw material of that type.

[0007] Optionally, the real-time monitoring data includes storage pressure, ambient humidity, and ambient temperature; The process of early warning monitoring of liquid raw materials includes: The temperature sensitivity coefficient and humidity influence coefficient of the liquid raw material are determined according to the type of liquid raw material. The first risk value of liquid raw materials is determined based on their viscosity, storage pressure, ambient humidity, ambient temperature, and humidity influence coefficient. The second risk value of liquid raw materials is determined based on ambient temperature and temperature sensitivity coefficient; Early warning monitoring results are obtained based on the first and second risk values ​​of deterioration.

[0008] Optionally, the calculation process for the first risk value of degradation includes: The ratios of ambient humidity, ambient temperature, and storage pressure to their corresponding standard values ​​were obtained respectively. , , Obtain according to the preset ratio coefficient , , The weighted sum Sum, f(x) is a defined function, f(x)=0 when x≤1, otherwise f(x)=x; Obtain the ratio of the viscosity of the liquid raw material to the standard viscosity. ,according to The range in which the liquid raw material is located determines its hygroscopic coefficient. , ∈[0,1); The weighted sum Sum, (1+ The cumulative product of the humidity influence coefficient and the humidity effect coefficient is used as the first deterioration risk value.

[0009] Optionally, the calculation process for the second risk value of degradation includes: The real-time ambient temperature is obtained by linear fitting of the acquired ambient temperature. The maximum change in ambient temperature, ΔT{t-Δt~t}, is obtained in real-time ambient temperature using Δt as the time window, where Δt is a preset fixed duration and t is the current time point. Obtain the maximum real-time ambient temperature Tmax{t-△t~t} during the time period t-△t~t; The real-time ambient temperature Ti, i∈[1,n], at n time points within Δt(t) is obtained according to a fixed time interval. This is then analyzed using a model. Obtain the environmental temperature stability coefficient s during the time period t-Δt~t; The temperature risk factor is determined based on ΔT{t-Δt~t}, Tmax{t-Δt~t} and s. The temperature risk factor is positively correlated with ΔT{t-Δt~t}, Tmax{t-Δt~t} and s, respectively. The product of the temperature risk factor and the temperature sensitivity coefficient is used as the second risk value for deterioration.

[0010] Optionally, the process of obtaining early warning monitoring results based on the first and second risk values ​​of geological degradation includes: The first and second risk values ​​of deterioration are compared with their corresponding thresholds: When the first risk value of deterioration is greater than or equal to the corresponding threshold, a humidity risk warning command is issued; When the second risk value of deterioration is greater than or equal to the corresponding threshold, a temperature risk warning command is issued. When both the first and second risk values ​​of deterioration are less than the corresponding thresholds, a risk coefficient of deterioration is obtained based on the first and second risk values ​​of deterioration. The risk coefficient of deterioration is compared with the risk threshold. When the risk coefficient of deterioration is greater than or equal to the risk threshold, a potential risk warning instruction is issued.

[0011] Optionally, the adjustment control process includes: When one or more of the humidity risk warning instructions or potential risk warning instructions are received, all real-time monitoring data are analyzed and judged, and real-time monitoring data with abnormalities in the analysis are adjusted. When a temperature risk warning is received, the ambient temperature in the real-time monitoring data is adjusted.

[0012] Secondly, this application provides a raw material inventory data processing system based on the Internet of Things, which adopts the following technical solution: An IoT-based raw material inventory data processing system, wherein the system employs any one of the above-described IoT-based raw material inventory data processing methods, including: A flow sensor is used to collect real-time flow during the raw material feeding process and obtain flow velocity information based on the real-time flow. An environmental parameter sensor cluster is used to acquire real-time monitoring data of the environment in which the liquid raw materials are located; The early warning and analysis center is used to perform state analysis on liquid raw materials based on flow rate data and the type of liquid raw materials, and to perform early warning monitoring on different types of liquid raw materials based on the state analysis results and real-time monitoring data. The adjustment control module is used to adjust and control the environmental parameters of liquid raw material storage based on the early warning monitoring results.

[0013] Thirdly, this application provides a storage medium, which adopts the following technical solution: A storage medium storing the raw material inventory data processing method based on the Internet of Things as described in any one of the above.

[0014] In summary, this application includes at least one of the following beneficial technical effects: This invention calculates the flow rate data of different liquid raw materials, thereby determining the state of the liquid raw materials. Combined with the acquired real-time monitoring data, it can conduct a comprehensive and in-depth analysis of different types and states of liquid raw materials, thereby ensuring the quality of liquid raw materials during storage. Attached Figure Description

[0015] Figure 1 This is a flowchart of the raw material inventory data processing method based on the Internet of Things in this invention.

[0016] Figure 2 This is a logical schematic diagram of the raw material inventory data processing system based on the Internet of Things in this invention. Detailed Implementation

[0017] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0018] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0019] This application discloses a method and system for processing raw material inventory data based on the Internet of Things (IoT), referring to... Figure 1 The method includes steps S1-S3, wherein S1 involves collecting flow rate data during the liquid raw material feeding process and real-time monitoring data of the surrounding environment; referring to Figure 2This process mainly employs a cluster of flow sensors and environmental parameter sensors. The flow sensors collect real-time flow data during raw material feeding and calculate flow velocity information using the formula: flow velocity = flow rate ÷ cross-sectional area. The environmental parameter sensors integrate temperature, humidity, and pressure sensors, enabling accurate monitoring of the storage environment parameters of the liquid raw materials. S2: Based on the flow velocity data and the type of liquid raw material, the state of the liquid raw material is analyzed. Based on the state analysis results and real-time monitoring data, early warning monitoring is performed for different types of liquid raw materials. S3: Based on the early warning monitoring results, the storage environment parameters of the liquid raw materials are adjusted and controlled. In the above process, by calculating the flow velocity data of different liquid raw materials, the state of the liquid raw materials can be judged. Simultaneously, combined with the acquired real-time monitoring data, a comprehensive and in-depth analysis can be performed on different types and states of liquid raw materials, thereby ensuring the quality of the liquid raw materials during storage.

[0020] The process of performing state analysis on liquid raw materials includes: firstly, comparing the flow rate data of the liquid raw material with the test flow rate data under the same specification inlet. The test flow rate data is obtained from the test data of liquid raw materials with different viscosities under the same specification and size inlet. The ratio of the flow rate data to the test flow rate data is obtained. Based on the test data, a ratio-viscosity mapping relationship is established for different types of liquid raw materials. The obtained ratio is substituted into the ratio-viscosity mapping relationship, thereby enabling the comparison to obtain the viscosity of that type of liquid raw material. The viscosity is used as the state analysis result of that type of liquid raw material. In subsequent early warning monitoring, the obtained viscosity of the liquid raw material can provide an accurate reference, improve the accuracy and sensitivity of monitoring, and thus ensure a comprehensive and in-depth analysis of the storage conditions of liquid raw materials.

[0021] In one embodiment, the real-time monitoring data includes storage pressure, ambient humidity, and ambient temperature; The process of early warning monitoring of liquid raw materials includes: determining the temperature sensitivity coefficient and humidity influence coefficient of the liquid raw material based on its type; the temperature sensitivity coefficient and humidity influence coefficient of the liquid raw material are set according to the physical characteristics of different types of liquid raw materials. For example, raw materials that are more sensitive to temperature (such as emulsions) have a relatively high temperature sensitivity coefficient, while raw materials that easily absorb moisture from the air (such as certain alcohols) are more sensitive to humidity, and therefore have a relatively high humidity sensitivity coefficient. By determining the temperature sensitivity coefficient and humidity influence coefficient of the liquid raw material, targeted analysis can be carried out on the characteristics of different liquid raw materials, thereby improving the sensitivity of the monitoring process.

[0022] In the early warning detection process, the first step is to determine the initial deterioration risk value of the liquid raw material based on its viscosity, storage pressure, ambient humidity, ambient temperature, and humidity influence coefficient. Clearly, the initial deterioration risk value reflects the degree of risk the liquid raw material is affected by humidity. However, the combination of moisture in the air with the liquid raw material is affected by temperature, pressure, and the viscosity of the liquid itself. Therefore, in addition to ambient humidity, calculating the initial deterioration risk value using storage pressure, ambient temperature, and the viscosity of the liquid raw material allows for a more accurate assessment of the degree of humidity influence. The calculation process for the initial deterioration risk value includes obtaining the ratios of ambient humidity, ambient temperature, and storage pressure to their corresponding standard values. , , The corresponding standard values ​​for ambient humidity, ambient temperature, and storage pressure are set according to the storage environment standards for the liquid raw material, and then obtained according to the preset proportional coefficients. , , The weighted sum Sum, where f(x) is a defined function, f(x) = 0 when x ≤ 1, otherwise f(x) = x; by defining the function... , Adjustments were made to disregard the impact of ambient temperature and storage pressure being below the corresponding standard values. Additionally, , , The corresponding weighted values ​​are obtained by integrating test data of the liquid raw material under different environmental parameters. This process is achieved through the controlled variable method. The degree of deterioration of the liquid raw material is measured when different environmental parameters exceed the storage environment standard (while other environmental parameters meet the storage environment standard). By measuring the degree of deterioration of the liquid raw material under different environmental parameters, the influence of that environmental parameter on the liquid raw material is determined. The higher the degree of deterioration, the higher the corresponding weight. Therefore, by comparing the degree of deterioration, the weighting ratio corresponding to different environmental parameters is determined. Thus, the weighted sum Sum can reflect the environmental state. In addition, the ratio of the viscosity of the liquid raw material to the standard viscosity is obtained. The standard viscosity is set according to the relevant standards for liquid raw materials. The range in which the liquid raw material is located determines its hygroscopic coefficient. , ∈[0,1); this process is based on different ratios The correspondence between the test data of the liquid raw materials in the interval was achieved by setting different test groups, each corresponding to a different ratio. Based on the degree of moisture absorption from the air by different test groups of liquid raw materials (which can be determined by weight), the moisture absorption capacity of the liquid raw materials in different test groups is judged. A moisture absorption coefficient of the liquid raw materials is then set based on the moisture absorption capacity, thereby obtaining different ratios. The mapping relationship between the hygroscopic coefficient of the liquid raw material and the actual hygroscopic coefficient of the liquid raw material, and based on the mapping relationship, the following can be achieved: The range in which the liquid raw material is located determines its hygroscopic coefficient. The process allows for the determination of the hygroscopic coefficient of the liquid raw material based on the obtained viscosity. Then the weighted sum Sum, (1+ The cumulative result of the product of the humidity influence coefficient and the humidity effect coefficient is used as the first deterioration risk value. Therefore, the first deterioration risk value can be dynamically adjusted according to the environmental conditions, the hygroscopic coefficient of the liquid raw material and the magnitude of the humidity influence coefficient, so as to achieve an accurate judgment on the degree of influence of humidity on the liquid raw material.

[0023] In one embodiment, the second deterioration risk value of the liquid raw material is determined based on the ambient temperature and temperature sensitivity coefficient. The calculation process includes: linearly fitting the real-time acquired ambient temperature to obtain the real-time ambient temperature; firstly, using Δt as a time window, the maximum ambient temperature change ΔT{t-Δt~t} is obtained, where Δt is a preset fixed duration set based on empirical data, and t is the current time point; therefore, the maximum ambient temperature change ΔT{t-Δt~t} can reflect the temperature change state over a past period; simultaneously, the maximum real-time ambient temperature Tmax{t-Δt~t} is obtained during the t-Δt~t period, and the maximum value Tmax{t-Δt~t} can reflect the extreme temperature state over a past period; at the same time, the real-time ambient temperatures Ti, i∈[1,n], at n time points within Δt(t) are obtained according to a fixed time interval, and the model is used to determine the optimal temperature for the process. Obtain the environmental temperature stability coefficient s during the time period t-Δt~t. When the temperature fluctuation during the time period t-Δt~t is large, the adverse impact on the liquid raw material is higher, and the value of the environmental temperature stability coefficient s is larger. Therefore, the temperature risk factor is determined based on ΔT{t-Δt~t}, Tmax{t-Δt~t}, and s. The temperature risk factor is positively correlated with ΔT{t-Δt~t}, Tmax{t-Δt~t}, and s, respectively. Thus, the risk of the current environmental temperature can be judged through the temperature risk factor. The specific calculation model of the temperature risk factor is obtained by fitting multiple sets of test data. The product of the temperature risk factor and the temperature sensitivity coefficient is used as the second deterioration risk value. Therefore, the risk of temperature deterioration of the liquid raw material can be judged by the risk of the current environmental temperature and the temperature sensitivity coefficient of the liquid raw material.

[0024] Subsequently, early warning monitoring results are obtained based on the first and second air quality risk values. This process includes: comparing the first and second air quality risk values ​​with their corresponding thresholds, which are set based on test simulation data. Therefore, when the first air quality risk value is greater than or equal to the corresponding threshold, a humidity risk warning is issued; when the second air quality risk value is greater than or equal to the corresponding threshold, a temperature risk warning is issued; when both the first and second air quality risk values ​​are less than their corresponding thresholds, a comprehensive judgment of the first and second air quality risk values ​​is also required. The common risk of the risk value is determined by using the weighted sum of the first and second deterioration risk values ​​as the deterioration risk coefficient. The weighting of the first and second deterioration risk values ​​is set according to the test data. The deterioration risk coefficient is compared with the risk threshold, which is set according to the test simulation. When the deterioration risk coefficient is greater than or equal to the risk threshold, a potential risk warning instruction is issued. Through the above judgment process, different warning monitoring results can be obtained based on the first and second deterioration risk values, which facilitates the subsequent adjustment and control process and ensures the comprehensiveness and depth of monitoring of the liquid raw material storage environment.

[0025] In one embodiment, the adjustment control process includes: when one or more of a humidity risk warning command or a potential risk warning command are obtained, performing judgment and analysis on all real-time monitoring data, and adjusting the real-time monitoring data that shows abnormalities in the judgment and analysis; when a temperature risk warning command is obtained, adjusting the ambient temperature in the real-time monitoring data. The specific adjustment process can be achieved through manual analysis and adjustment or by adjusting stepwise by reducing the unit quantity. When adjusting stepwise by reducing the unit quantity, the corresponding adjustment amount is reduced by the unit quantity and then real-time monitoring is performed. The monitoring stops when the warning monitoring result meets the requirements. The above methods can also be combined when adjusting specific parameters, and there are no restrictions here.

[0026] This application also discloses a storage medium storing the raw material inventory data processing method based on the Internet of Things as described in any one of the above embodiments.

[0027] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for processing raw material inventory data based on the Internet of Things, characterized in that, include: S1. Collect flow rate data and real-time monitoring data of the surrounding environment during the liquid raw material feeding process; S2. Perform state analysis on the liquid raw materials based on the flow rate data and the type of liquid raw materials, and perform early warning monitoring on different types of liquid raw materials based on the state analysis results and real-time monitoring data. S3. Adjust and control the environmental parameters of liquid raw material storage based on the early warning monitoring results.

2. The method for processing raw material inventory data based on the Internet of Things according to claim 1, characterized in that, The process of performing state analysis on liquid feedstocks includes: The flow rate data of the liquid raw material is compared with the test flow rate data under the same feed port specification to obtain the ratio of the flow rate data to the test flow rate data. Based on the test data, a ratio-viscosity mapping relationship is established for different types of liquid raw materials. The obtained ratio is substituted into the ratio-viscosity mapping relationship to obtain the viscosity of the liquid raw material of that type. The viscosity is used as the state analysis result of the liquid raw material of that type.

3. The method for processing raw material inventory data based on the Internet of Things according to claim 2, characterized in that, The real-time monitoring data includes storage pressure, ambient humidity, and ambient temperature. The process of early warning monitoring of liquid raw materials includes: The temperature sensitivity coefficient and humidity influence coefficient of the liquid raw material are determined according to the type of liquid raw material. The first risk value of liquid raw materials is determined based on their viscosity, storage pressure, ambient humidity, ambient temperature, and humidity influence coefficient. The second risk value of liquid raw materials is determined based on ambient temperature and temperature sensitivity coefficient; Early warning monitoring results are obtained based on the first and second risk values ​​of deterioration.

4. The method for processing raw material inventory data based on the Internet of Things according to claim 3, characterized in that, The calculation process for the first risk value of deterioration includes: The ratios of ambient humidity, ambient temperature, and storage pressure to their corresponding standard values ​​were obtained respectively. , , Obtain according to the preset ratio coefficient , , The weighted sum Sum, f(x) is a defined function, f(x)=0 when x≤1, otherwise f(x)=x; Obtain the ratio of the viscosity of the liquid raw material to the standard viscosity. ,according to The range in which the liquid raw material is located determines its hygroscopic coefficient. , ∈[0,1); The weighted sum Sum, (1+ The cumulative product of the humidity influence coefficient and the humidity effect coefficient is used as the first deterioration risk value.

5. The method for processing raw material inventory data based on the Internet of Things according to claim 3, characterized in that, The calculation process for the second risk value of deterioration includes: The real-time ambient temperature is obtained by linear fitting of the acquired ambient temperature. The maximum change in ambient temperature, ΔT{t-Δt~t}, is obtained in real-time ambient temperature using Δt as the time window, where Δt is a preset fixed duration and t is the current time point. Obtain the maximum real-time ambient temperature Tmax{t-△t~t} during the time period t-△t~t; The real-time ambient temperature Ti, i∈[1,n], at n time points within Δt(t) is obtained according to a fixed time interval. This is then analyzed using a model. Obtain the environmental temperature stability coefficient s during the time period t-Δt~t; The temperature risk factor is determined based on ΔT{t-Δt~t}, Tmax{t-Δt~t} and s. The temperature risk factor is positively correlated with ΔT{t-Δt~t}, Tmax{t-Δt~t} and s, respectively. The product of the temperature risk factor and the temperature sensitivity coefficient is used as the second risk value for deterioration.

6. The method for processing raw material inventory data based on the Internet of Things according to claim 3, characterized in that, The process of obtaining early warning monitoring results based on the first and second risk values ​​of geological degradation includes: The first and second risk values ​​of deterioration are compared with their corresponding thresholds: When the first risk value of deterioration is greater than or equal to the corresponding threshold, a humidity risk warning command is issued; When the second risk value of deterioration is greater than or equal to the corresponding threshold, a temperature risk warning command is issued. When both the first and second risk values ​​of deterioration are less than the corresponding thresholds, a risk coefficient of deterioration is obtained based on the first and second risk values ​​of deterioration. The risk coefficient of deterioration is compared with the risk threshold. When the risk coefficient of deterioration is greater than or equal to the risk threshold, a potential risk warning instruction is issued.

7. The method for processing raw material inventory data based on the Internet of Things according to claim 6, characterized in that, The adjustment and control process includes: When one or more of the humidity risk warning instructions or potential risk warning instructions are received, all real-time monitoring data are analyzed and judged, and real-time monitoring data with abnormalities in the analysis are adjusted. When a temperature risk warning is received, the ambient temperature in the real-time monitoring data is adjusted.

8. A raw material inventory data processing system based on the Internet of Things, characterized in that, The system employs the IoT-based raw material inventory data processing method as described in any one of claims 1-7, including: A flow sensor is used to collect real-time flow during the raw material feeding process and obtain flow velocity information based on the real-time flow. An environmental parameter sensor cluster is used to acquire real-time monitoring data of the environment in which the liquid raw materials are located; The early warning and analysis center is used to perform state analysis on liquid raw materials based on flow rate data and the type of liquid raw materials, and to perform early warning monitoring on different types of liquid raw materials based on the state analysis results and real-time monitoring data. The adjustment control module is used to adjust and control the environmental parameters of liquid raw material storage based on the early warning monitoring results.

9. A storage medium, characterized in that, The system stores the raw material inventory data processing method based on the Internet of Things as described in any one of claims 1-7.

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