Method and system for collecting environmental data of medicinal liquor production workshop

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a medicinal liquor production workshop environment data acquisition method and system, and the method comprises the steps: obtaining environment data of different dimensions in the current batch production process of a medicinal liquor production workshop; determining the instability of the environmental data of each dimension at each moment in the current batch production process, and determining a plurality of target time periods in the current batch production process based on the stability; and in the next batch production process, increasing the acquisition frequency of the environmental data in the same time period corresponding to the target time period, and decreasing the acquisition frequency of the environmental data in the same time period corresponding to the non-target time period. According to the invention, while the collection of redundant data in a normal time period is reduced, the data collection quality in an important time period is ensured, and the stability of the medicinal liquor production quality is effectively ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and particularly relates to a method and system for collecting environmental data in a medicinal liquor production workshop. Background Art

[0002] In the fermentation, extraction, aging and other links of the medicinal liquor production process, the environmental factors have a great influence. Therefore, it is necessary to monitor the environmental data in real time during the production process of the medicinal liquor to ensure that the environmental data changes in the production workshop of the medicinal liquor of the same type and different batches are consistent, so as to ensure the stability and consistency of the quality of the medicinal liquor in different batches.

[0003] In the process of monitoring the environmental data during the production process of different batches of the same type of medicinal liquor in the same workshop, since the entire production process is a long-term process, for a fixed collection frequency, when the collection frequency of the environmental data in the medicinal liquor production workshop is too high, a large amount of redundant data will be generated, and when the frequency is too low, it is easy to miss important data collection. Therefore, how to determine the appropriate collection frequency of the environmental data during the production process of the medicinal liquor, while reducing the redundant data collection during normal periods, and ensuring the quality of the data collection during important periods, so as to ensure the consistent production quality of the medicinal liquor in different batches is crucial. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for collecting environmental data in a medicinal liquor production workshop, and the specific technical solutions adopted are as follows: In the first aspect, the present invention provides a method for collecting environmental data in a medicinal liquor production workshop, including the following steps: Obtain the environmental data of different dimensions during the production process of the current batch in the medicinal liquor production workshop; Determine the instability of the environmental data of each dimension at each moment during the production process of the current batch; Determine the dimension with instability greater than the instability threshold as the target dimension at each moment during the production process of the current batch; Regard the moment when the proportion of the number of target dimensions in all dimensions is greater than the set ratio as the target moment, and consecutive adjacent target moments form a target period, and several target periods are obtained; In the production process of the next batch, increase the collection frequency of the environmental data in the same period corresponding to the target period, and decrease the collection frequency of the environmental data in the same period corresponding to the non-target period.

[0005] Combined with the above first aspect, in some possible implementation manners, determining the instability of the environmental data of each dimension at each moment during the production process of the current batch includes: Determine the time window at each moment during the production process of the current batch; Determine the dimension data sequence corresponding to the environmental data of each dimension in the time window; Determine the instability of the environmental data of each dimension at each moment during the current batch production process according to the fluctuation of the dimension data sequence.

[0006] Combined with the first aspect above, in some possible implementation manners, determining the instability of the environmental data of each dimension at each moment during the current batch production process includes: Determine the maximum value sequence composed of all maximum values in the dimension data sequence; Determine the maximum value and the minimum value in the maximum value sequence, the extreme difference formed by the difference between the maximum value and the minimum value, and the time interval between the maximum value and the minimum value; Determine the maximum value between the maximum value and the minimum value in the maximum value sequence as the reference maximum value; Determine the trough sequence formed by the dimension data of each dimension between every two adjacent reference maximum values in the dimension data sequence; Determine the fluctuation law factor according to the change trend difference between any two trough sequences; Determine the instability of the environmental data of each dimension at each moment during the current batch production process according to the number of reference maximum values, the extreme difference, the time interval, and the fluctuation law factor.

[0007] Combined with the first aspect above, in some possible implementation manners, increasing the acquisition frequency of the environmental data in the same time period corresponding to the target time period includes: Determine the importance of each target time period according to the distribution of the target dimension at each moment in each target time period; Determine the environmental stability factor of each target time period according to the time interval between adjacent target time periods and the distribution similarity of the target dimension at each moment between adjacent target time periods; Determine the retention factor of each target time period according to the importance and the environmental stability factor; In the next batch production process, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period according to the retention factor.

[0008] Combined with the first aspect above, in some possible implementation manners, determining the importance of each target time period includes: Determine the intersection of the target dimensions at every two adjacent moments in each target time period to obtain the dimension intersection; Take the continuously adjacent same dimension intersections as a new intersection to obtain several new intersections, and determine the attention factor of the new intersections; Determine the target dimension intersection of each of the target time periods according to the several new intersections; Determine the maximum value of the number of target dimensions at all moments in each of the target time periods; Determine the ratio of the number of target dimensions included in the target dimension intersection to the maximum value of the number as the dimension data consistency of each of the target time periods; Determine the importance of each of the target time periods according to the duration and dimension data consistency of each of the target time periods, as well as the attention factors of all the new intersections and the number of target dimensions included in the new intersections in each of the target time periods.

[0009] Combined with the above first aspect, in some possible implementation manners, determining the attention factor of the new intersection includes: Determine the average value of the instability of the environmental data of all target dimensions included in the new intersections at all moments in each of the target time periods to obtain the instability mean value; Determine the product of the ratio of the number of target dimensions included in the new intersection to the number of all dimensions, the time length of the time period spanned by the new intersection and the instability mean value as the attention factor of the new intersection.

[0010] Combined with the above first aspect, in some possible implementation manners, determining the importance of each of the target time periods includes: Determine the second product of the attention factor of each new intersection and the number of target dimensions included in each new intersection in each of the target time periods; Determine the third product of the accumulated value of all the second products and the duration of each of the target time periods; Determine the ratio of the third product to the dimension data consistency as the importance of each of the target time periods.

[0011] Combined with the above first aspect, in some possible implementation manners, determining the environmental stability factor of each of the target time periods includes: Determine the same dimensions in the target dimension intersections of each of the target time periods and the target time periods before and after it as the key dimensions; Determine the sub-environmental stability factor between each of the target time periods and the target time periods before and after it according to the number of dimensions of the key dimensions and the time interval between each of the target time periods and the target time periods before and after it; Determine the average value of the fourth product of the durations of the target time periods before and after each of the target time periods and the corresponding sub-environmental stability factors as the environmental stability factor of each of the target time periods.

[0012] Combined with the above first aspect, in some possible implementation manners, adjusting the acquisition frequency of the environmental data in the same time period corresponding to the target time period according to the retention factor includes: Determining a frequency adjustment amount according to the acquisition frequency of the environmental data in the target time period and the retention factor; Determining the sum of the acquisition frequency of the environmental data in the target time period and the frequency adjustment amount, and using the sum as the increased acquisition frequency of the environmental data in the same time period corresponding to the target time period in the next batch production process.

[0013] In a second aspect, the present invention further provides an environmental data acquisition system for a medicinal liquor production workshop, including a memory and a processor. The memory is used to store executable program codes, and the processor is used to call and run the executable program codes from the memory, so that the device executes the method in the above first aspect or any one of the possible implementation manners of the first aspect.

[0014] In a third aspect, the present invention further provides a computer program product, which includes: computer program codes, when the computer program codes run on a computer, enabling the computer to execute the method in the above first aspect or any one of the possible implementation manners of the first aspect.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores computer program codes, and when the computer program codes run on a computer, enabling the computer to execute the method in the above first aspect or any one of the possible implementation manners of the first aspect.

[0016] The present invention has the following beneficial effects: The present invention obtains environmental data in different dimensions during the current batch production process of a medicinal liquor production workshop; determines the instability of the environmental data in each dimension at each moment during the current batch production process, and based on the stability, determines the target moments during the current batch production process. The continuously adjacent target moments form a target time period, so as to determine several target time periods during the current batch production process; in the next batch production process, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period, and decrease the acquisition frequency of the environmental data in the same time period corresponding to the non-target time period. The present invention reduces the acquisition frequency of redundant data in normal time periods while increasing the data acquisition frequency of the target time period, thereby ensuring the quality of data acquisition in important abnormal time periods and effectively ensuring the stability of the medicinal liquor production quality. Description of the Drawings

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for 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 be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the steps of a method for collecting environmental data in a medicinal liquor production workshop according to an embodiment of the present invention; Figure 2 It is a flowchart of the steps of determining the instability of environmental data in each dimension at each moment during the production process of the current batch according to an embodiment of the present invention; Figure 3 It is a flowchart of the steps of increasing the collection frequency of environmental data in the same time period corresponding to the target time period according to an embodiment of the present invention; Figure 4 It is a schematic diagram of determining the intersection of target dimensions in each target time period according to an embodiment of the present invention; Figure 5 It is a schematic structural diagram of a system for collecting environmental data in a medicinal liquor production workshop according to an embodiment of the present invention. Detailed Embodiments

[0019] To clearly illustrate the technical features of this solution, the present invention will be described in detail below through specific embodiments in combination with the drawings.

[0020] The embodiments of the present invention will be described in more detail below with reference to the drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0021] It should be understood that the steps recorded in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0022] The term "including" and its variations used herein are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0023] It should be noted that concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0024] In the embodiments of the present invention, although operations or steps are described in a specific order in the drawings, it should not be understood that these operations or steps are required to be performed in the specific order shown or in a serial order, or that all the operations or steps shown are required to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps can be performed serially; they can also be performed in parallel; or a part of these operations or steps can be performed.

[0025] At the same time, it can be understood that the data involved in the technical solution of the present invention (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws, regulations and related provisions. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs, and all parameters or indicators in the formulas involved in the present invention are numerical values after normalization to eliminate the influence of dimensions.

[0026] First, the application scenario of the present invention will be described. In the production process of medicinal liquor, it is necessary to collect and monitor environmental factors in multiple dimensions of the medicinal liquor production workshop environment in real time, such as temperature, humidity, gas concentration (such as carbon dioxide), air circulation speed, etc. These environmental factors will affect key processes such as the soaking, fermentation, and extraction of medicinal materials components of the medicinal liquor. When the collection frequency of the environmental data of the medicinal liquor production workshop is too high, a large amount of redundant data will be generated; while when the collection frequency is too low, if the changes in the workshop environment are too frequent, it will cause the failure to collect effective data of these changes in time, which may lead to inconsistent production quality of the medicinal liquor in different batches.

[0027] For the above application scenario, the embodiments of the present invention provide a method and system for collecting environmental data in a medicinal liquor production workshop. By analyzing the environmental data in different dimensions during the production process of the current batch, the collection frequency of the environmental data at different times during the production process of the next batch is determined, while reducing the collection of redundant data during normal times and ensuring the quality of data collection during important abnormal times, thereby ensuring the stability and consistency of the production quality of the medicinal liquor.

[0028] Next, a method and system for collecting environmental data in a medicinal liquor production workshop provided by the embodiments of the present invention will be introduced in detail with reference to the drawings.

[0029] Figure 1 shows a schematic diagram of the basic process of a method for collecting environmental data in a medicinal liquor production workshop provided by an embodiment of the present invention, asFigure 1 As shown in the figure, the method specifically includes the following steps: Step S100: Obtain environmental data of different dimensions during the production process of the current batch in the medicinal liquor production workshop.

[0030] Specifically, the main components of the medicinal liquor are obtained through the extraction process of the medicinal materials. Environmental fluctuations in the medicinal liquor production workshop will affect the stability and consistency of the production quality of the medicinal liquor. For example, changes in temperature and humidity will directly affect the dissolution rate of the active ingredients in the medicinal materials. If the temperature is too high or too low, it may lead to incomplete extraction of the medicinal materials, thereby affecting the concentration of the active ingredients in the medicinal liquor and potentially making the efficacy of the medicinal liquor unstable.

[0031] Therefore, during the production process of the medicinal liquor, it is necessary to collect and monitor various data of the production workshop environment in real time, including environmental data such as temperature, humidity, gas concentration, and air circulation speed. These environmental data help to ensure the stability of the medicinal liquor production process and provide effective production quality assurance.

[0032] In this embodiment, multi-dimensional environmental data during the production process of the current production batch in the medicinal liquor production workshop is collected. The specific collection methods are as follows: 1. Use a temperature sensor to collect the temperature of the air in the production workshop to obtain temperature time-series data, and use a humidity sensor to collect the humidity of the air in the production workshop to obtain humidity time-series data. Among them, the types of temperature sensors and humidity sensors can be reasonably selected according to needs. For example, for the humidity sensor, since the capacitive humidity sensor has a fast response and is suitable for real-time monitoring, the humidity sensor can specifically adopt a capacitive humidity sensor.

[0033] 2. Use a gas sensor to collect the concentration of the gas in the production workshop to obtain gas concentration time-series data. Among them, the type of gas sensor can be reasonably selected according to needs. For example, for carbon dioxide in the production workshop, the concentration can be collected through a non-dispersive infrared (NDIR) sensor.

[0034] 3. Use an anemometer or a flow meter to collect the air circulation speed in the production workshop to obtain air circulation speed time-series data. Among them, the types of anemometers or flow meters can be reasonably selected according to needs. Commonly used anemometers include hot film anemometers, vane anemometers, etc., and commonly used flow meters such as vortex flow meters, ultrasonic flow meters, etc. can all be used for measuring the air circulation speed.

[0035] In this embodiment, multi-dimensional environmental data during the current production batch in the medicinal liquor production workshop is synchronously collected. The collection frequency can be reasonably set according to experience. For example, the collection frequency is set to once every 30 seconds. The time-series data corresponding to the environmental data of all dimensions is standardized to make them have similar scales and distributions for better comparison and analysis. Among them, the method of standardization can be reasonably selected according to needs. In this embodiment, the decimal scaling standardization method can be used for the standardization of time-series data.

[0036] Thus, the environmental data of different dimensions during the current batch production process in the medicinal liquor production workshop can be obtained. The environmental data of each dimension is a time-series data after being standardized, and the elements with the same serial number in the time-series data corresponding to the environmental data of different dimensions correspond to the same moment during the current batch production process.

[0037] Step S200: Determine the instability of the environmental data of each dimension at each moment during the current batch production process.

[0038] Specifically, the production of medicinal liquor is a dynamic process. During the fermentation process of medicinal liquor production, the change of environmental data is crucial for ensuring fermentation quality and product safety. If the environmental data of a certain dimension is unstable during the production of medicinal liquor, it may affect the production quality of medicinal liquor, and this part of environmental data needs to be analyzed keyly. Therefore, the integrity of this part of the collected data needs to be ensured.

[0039] Step S300: Determine the target dimension at each moment during the current batch production process for the dimension whose instability is greater than the instability threshold.

[0040] Specifically, an instability threshold is preset. The specific value of this instability threshold can be reasonably set according to needs. In this embodiment, the value of this instability threshold is set to 0.6. For any th moment during the current batch production process, compare the instability of the environmental data of each dimension at this th moment with this instability threshold, and determine the dimension whose instability is greater than the instability threshold as the target dimension at this th moment, so that the target dimensions at this th moment can be obtained.

[0041] Thus, by comparing the instability of the environmental data of each dimension at each moment during the current batch production process with the instability threshold, the target dimension at each moment during the current batch production process can be determined. The target dimension refers to the dimension where the environmental data fluctuates greatly at the corresponding moment.

[0042] Step S400: Take the moments when the quantity ratio of the target dimension in all dimensions is greater than a set ratio as target moments. Successive adjacent target moments form a target period, and several target periods are obtained.

[0043] Specifically, a set ratio is preset. The specific value of this set ratio can be reasonably set according to needs. In this embodiment, the value of this set ratio is set to 0.5. For any th moment in the current batch production process, determine the quantity ratio of the target dimension of this th moment in all dimensions, compare this quantity ratio with the set ratio, and take the moment when the quantity ratio is greater than the set ratio as the target moment. Thus, several target moments in the current batch production process can be determined. Successive adjacent target moments form a target period, and thus several target periods in the current batch production process are obtained. It should be understood that due to the persistence of data instability, for a single isolated target moment, it is very likely to be an interference moment and cannot form a target period alone. Therefore, a target period should be composed of at least two successive adjacent target moments.

[0044] So far, several target periods in the current batch production process have been obtained. A target period refers to an abnormal period when the environmental data in different dimensions is relatively unstable. For the other periods formed by the moments outside all the target periods in the current batch production process, they are called non-target periods.

[0045] Step S500: In the next batch production process, increase the acquisition frequency of the environmental data in the same period corresponding to the target period, and decrease the acquisition frequency of the environmental data in the same period corresponding to the non-target period.

[0046] Specifically, as a consumer product, if the medicinal liquor in each production batch has differences in medicinal liquor flavor, taste, color, aroma, etc. due to different environmental conditions, it will affect the consumer experience. Through the above Step S400, several target periods in the current batch production process are determined, and the environmental data in different dimensions is relatively unstable within the target period. Since the production processes of the medicinal liquor of the same type in different batches are the same, for the abnormal periods when the environmental data is relatively unstable in the production process of the previous batch of medicinal liquor, key attention is required in the production process of the next batch of medicinal liquor.

[0047] Therefore, during the production process of the next batch, increase the acquisition frequency of the environmental data corresponding to the same time period in the target time period, and at the same time decrease the acquisition frequency of the environmental data corresponding to the same time period in the non-target time period. For example, adjust the acquisition frequency of the environmental data corresponding to the same time period in the target time period to twice the acquisition frequency of the environmental data in the target time period during the production process of the current batch, and adjust the acquisition frequency of the environmental data corresponding to the same time period in the non-target time period to half of the acquisition frequency of the environmental data in the non-target time period during the production process of the current batch, so as to avoid excessive or insufficient data acquisition during the production process of the next batch, thereby achieving precise control of the environmental conditions for medicinal liquor production and ultimately ensuring the consistency of the quality of medicinal liquor.

[0048] In a method for collecting environmental data in a medicinal liquor production workshop provided in this embodiment, by analyzing the instability of the environmental data of each dimension at each moment during the production process of the current batch, several target time periods with relatively unstable environmental data of different dimensions during the production process of the current batch are determined. Therefore, during the production process of the next batch, increase the acquisition frequency of the environmental data corresponding to the same time period in each target time period, and decrease the acquisition frequency of the environmental data corresponding to the same time period in the non-target time period. While reducing the acquisition of redundant data during normal time periods, the quality of data acquisition during important abnormal time periods is ensured, thereby facilitating the precise control of the environmental conditions for medicinal liquor production and ultimately ensuring the consistency of the quality of medicinal liquor.

[0049] Further, in some possible implementation manners, as Figure 2 shown, determining the instability of the environmental data of each dimension at each moment during the production process of the current batch includes: Step S201: Determine the time window at each moment during the production process of the current batch; Step S202: Determine the dimension data sequence corresponding to the environmental data of each dimension in the time window; Step S203: Determine the instability of the environmental data of each dimension at each moment during the production process of the current batch according to the fluctuation of the dimension data sequence.

[0050] Specifically for the above steps, according to the environmental data of each dimension during the production process of the current batch, the production process of the current batch is divided into multiple production stages. For example, determine the tangent slope of each data point in the environmental data of each dimension, use the K-means clustering algorithm to cluster all the tangent slopes, and cluster several consecutive tangent slopes that are close to each other into one category, thereby obtaining multiple tangent slope clusters. All the moments corresponding to the tangent slopes in each tangent slope cluster constitute a production stage. Among them, the value of K in the K-means clustering algorithm can be reasonably set as needed. In this embodiment, K = 3 is set.

[0051] For any th moment during the production process of the current batch, for the environmental data corresponding to each dimension, with this th moment as the central moment of the window, determine a time window with a set window size. For example, the set window size can be set to 31, that is, the window area formed by this th moment and 15 moments on each of its left and right sides is used as the time window for this th moment. Among them, this th moment and 15 moments on each of its left and right sides belong to the same production stage. It should be understood that for the first few moments and the last few moments of each production stage, when the number of moments on the left or right side of this th moment is less than 15, the adjacent moments on the right or left side of this th moment can be correspondingly supplemented into the window area, so as to finally obtain a time window with a set window size. Furthermore, determine the dimension data sequence formed by the environmental data of each dimension in this time window. This dimension data sequence is the time series data segment corresponding to the time series data of the environmental data of the corresponding dimension within this time window. Analyze the fluctuation situation of this dimension data sequence, so as to determine the instability of the environmental data of the corresponding dimension at this th moment. When the fluctuation degree of this dimension data sequence is relatively high, the instability of the environmental data of the corresponding dimension is higher.

[0052] In this embodiment, by determining the time window for each moment during the production process of the current batch and determining the dimension data sequence corresponding to the environmental data of each dimension in this time window, and by analyzing the fluctuation situation of this dimension data sequence, the instability of the environmental data of each dimension at each moment can be accurately determined.

[0053] Furthermore, in some possible implementation manners, determining the instability of the environmental data of each dimension at each moment during the production process of the current batch includes: Determine the maximum value sequence formed by all the maximum values in the dimension data sequence; Determine the maximum value and the minimum value in the maximum value sequence, the extreme difference formed by the difference between the maximum value and the minimum value, and the time interval between the maximum value and the minimum value; Determine the maximum value between the maximum value and the minimum value in the maximum value sequence as the reference maximum value; Determine the trough sequence formed by the dimension data of each dimension between every two adjacent reference maximum values in the dimension data sequence; Determine the fluctuation law factor according to the difference in the change trends of any two trough sequences; Determine the instability of the environmental data of each dimension at each moment during the production process of the current batch according to the number of reference maxima, the extreme difference value, the time interval, and the fluctuation law factor.

[0054] Specifically for the above steps, for any th moment and any th dimension data sequence during the production process of the current batch, obtain all the maxima in this dimension data sequence to form a maximum value sequence. Take the absolute value of the difference between the maximum value and the minimum value in this maximum value sequence, and denote it as the fluctuation range of the th dimension data at the th moment. The larger the fluctuation range, the higher the instability of the environmental data of the th dimension within the time window of the th moment.

[0055] During the production process of medicinal liquor, the environmental data of each dimension will fluctuate within a certain range. Under normal circumstances, the fluctuations of the environmental data of each dimension will have a certain pattern, that is, in most stable production environments, the distribution of troughs is relatively regular. If the external environment changes greatly, the regularity between troughs may be affected, and the regularity of fluctuations will become unstable.

[0056] Therefore, obtain several maxima between the maximum value and the minimum value in the maximum value sequence as reference maxima. Take the time series sequence formed by the dimension data of each dimension between every two adjacent reference maxima as the trough sequence. Denote the reciprocal of the sum of the mean of the DTW distances between all arbitrary two trough sequences and a fixed parameter as the fluctuation law factor of the th dimension data at the th moment. The larger the DTW distance between any two trough sequences, the greater the shape difference between the two trough sequences, the lower the similarity, the more irregular the fluctuations, and the smaller the value of the corresponding fluctuation law factor . Among them, the fixed parameter is an infinitesimal value greater than 0, which is used to prevent the denominator from being zero and can be reasonably valued according to needs. In this embodiment, this fixed parameter is set.

[0057] According to the number of reference maxima between the and the minimum value in the maximum value sequence , and the fluctuation range , to obtain the th moment of the th dimension of the fluctuation frequency of the dimension data . Among them, The larger it is, it indicates that within the time window of the th moment, there are more maximum values in the dimension data of the th dimension distributed between the maximum value and the minimum value . The frequent change of the data will cause the instability of the data, which will further affect the quality of the medicinal liquor.

[0058] Determine the time interval between the maximum value and the minimum value in the maximum value sequence. When the time interval is smaller, it indicates that within the time window of the th moment, the dimension data of the th dimension can reach the minimum value from the maximum value or reach the maximum value from the minimum value in a shorter time, and the stability of the data is worse.

[0059] Furthermore, according to the fluctuation law factor , the fluctuation frequency , and the time interval between the maximum value and the minimum value in the maximum value sequence, determine the instability of the environmental data of any th dimension at any th moment during the production process of the current batch through the following formula: ; In the formula: represents the standard normalization function, which is used to normalize the numerical value.

[0060] In this embodiment, for the dimension data sequence corresponding to the environmental data of each dimension in the time window of each moment during the production process of the current batch, by analyzing the maximum value sequence composed of all the maximum values in the dimension data sequence, the instability of the environmental data of each dimension at each moment during the production process of the current batch can be accurately determined.

[0061] Furthermore, in some possible implementation manners, as Figure 3 shown, increasing the acquisition frequency of the environmental data in the same time period corresponding to the target time period includes: Step S501: Determine the importance of each target time period according to the distribution of the target dimension at each moment in each target time period. Step S502: Determine the environmental stability factor of each target time period according to the time interval between adjacent target time periods and the distribution similarity of the target dimension at each moment between adjacent target time periods. Step S503: Determine the retention factor of each target time period according to the importance and the environmental stability factor. Step S504: In the next batch production process, according to the retention factor, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period.

[0062] Specifically for the above steps, in the production of medicinal liquor, the complex changes in the production workshop environment will bring more uncertainties and risks to the production of medicinal liquor. Different environmental factors have different degrees of influence on each link of the production of medicinal liquor. The simultaneous change of multiple factors without obvious rules will make the production process difficult to control, which may have a comprehensive impact on the fermentation process of the medicinal liquor, resulting in the quality of the medicinal liquor being difficult to guarantee, and even serious problems such as deterioration may occur. Therefore, this complex environmental change needs to be given key attention.

[0063] For each moment in the production process of the current batch, there may be multiple target dimensions or there may be a single target dimension. When there is a single target dimension at a certain moment, it means that the environmental data of a single dimension is unstable at this moment. At this time, there may be a problem with a certain system in the production workshop. For example, unstable humidity may be due to the unreasonable ventilation system in the fermentation workshop, poor air circulation, resulting in excessive accumulation or rapid loss of moisture; and when there are multiple target dimensions at a certain moment, it means that the environmental data of multiple dimensions are unstable at the same moment. At this time, it means that there is a problem with the overall performance of the workshop. For example, the control system fails during the fermentation process, which may simultaneously affect the control of temperature, humidity and carbon dioxide concentration. Therefore, the instability of the environmental data of multiple dimensions will have a serious comprehensive impact on the fermentation of the medicinal liquor, resulting in a serious decline in the quality of the produced medicinal liquor.

[0064] Therefore, according to the distribution of the target dimension at each moment in each target time period in the production process of the current batch, determine the importance of each target time period. This importance reflects the impact of the instability of the environmental data of multiple dimensions in each target time period on the quality of the production of medicinal liquor. When the impact on the quality of the production of medicinal liquor is small, the importance of this target time period is low.

[0065] Meanwhile, during the production process of medicinal liquor, the fermentation environment is dynamically changing. If only the instability of environmental data in a single target period is considered, the change trends and patterns in previous periods may be overlooked. Taking the m-th target period as an example, if problems occurred in the temperature and humidity dimensions in the (m - 1)-th period, and only the temperature dimension problem worsened while the humidity returned to normal in the m-th period, it indicates that the equipment's humidity regulation functioned. On the contrary, if more unstable environmental dimension data appear in the m-th target period, for example, problems occur not only in the temperature and humidity dimensions but also in the carbon dioxide concentration dimension in the m-th period, it shows that the fermentation environment has become more complex. To more accurately capture these fluctuations and take timely adjustment measures, it is necessary to increase the acquisition frequency of environmental data in the m-th period during the production process of the next batch of medicinal liquor, thereby reducing the impact of environmental fluctuations on the quality of medicinal liquor.

[0066] Therefore, analyze the time interval between adjacent target periods and the distribution similarity of the target dimensions at each moment between adjacent target periods to determine the environmental stability factor for each target period. This environmental stability factor reflects the degree of distribution similarity of the target dimensions of each target period and its adjacent target periods. When the degree of distribution similarity is lower, it indicates that the environmental changes in the medicinal liquor production workshop are more complex and the stability is worse, and the value of the corresponding environmental stability factor is smaller.

[0067] Furthermore, based on the importance of each target period and the environmental stability factor, determine the retention factor for each target period. When the value of the importance of a target period is larger and the environmental stability factor is smaller, the value of the corresponding retention factor is larger. For a target period with a larger retention factor, it is more likely to contain abnormal, fluctuating, or other key environmental data that may affect the quality of medicinal liquor. For example, in the initial stage, although the temperature, humidity, and carbon dioxide concentration are gradually increasing, if the retention factor of a certain period is large, it may mean that abnormal situations occurred during this heating process, such as the temperature rising too fast or too slow, which may affect the growth and metabolism of microorganisms, and then lead to different qualities of medicinal liquor of the same type in different batches, reducing the consistency of the quality of medicinal liquor products.

[0068] In this embodiment, based on the importance and the environmental stability factor, the retention factor of any -th target period during the production process of the current batch is determined through the following formula : ; In the formula: represents the importance of any -th target period during the production process of the current batch; represents the environmental stability factor of any -th target period during the production process of the current batch; Represents a standard normalization function used to normalize numerical values; Represents a correction parameter, is an infinitesimal value greater than 0 used to prevent the denominator from being zero and can be reasonably valued as needed. In this embodiment, it is set .

[0069] In the next production batch, determine the same time periods corresponding to each target time period, and adjust the acquisition frequency of the environmental data for the same time periods corresponding to each target time period according to the retention factor of each target time period. When the retention factor is higher, the value of the acquisition frequency of the environmental data for the adjusted same time period is higher.

[0070] In this embodiment, by determining the retention factor for each target time period in the current production batch and adjusting the acquisition frequency of the environmental data for the same time periods corresponding to the target time periods in the next production batch according to this retention factor, the acquisition frequency of the environmental data in the next production batch can be adjusted more accurately, thus ensuring the consistency of the quality of the medicinal liquor products.

[0071] Furthermore, in some possible implementation manners, determining the importance of each target time period includes: Determine the intersection of the target dimensions for every two adjacent moments in each of the target time periods to obtain a dimension intersection; Take consecutive adjacent identical dimension intersections as a new intersection to obtain several new intersections, and determine the attention factor of the new intersections; According to the several new intersections, determine the target dimension intersection for each of the target time periods; Determine the maximum value of the number of target dimensions for all moments in each of the target time periods; Determine the ratio of the number of target dimensions included in the target dimension intersection to the maximum value as the dimension data consistency for each of the target time periods; According to the duration and dimension data consistency of each of the target time periods, as well as the attention factors of all the new intersections and the number of target dimensions included in the new intersections for each of the target time periods, determine the importance of each of the target time periods.

[0072] Specifically for the above steps, taking the th target time period as an example, as Figure 4 shown, the A target time period consists of the i-3 moment, the i-2 moment, the i-1 moment, and the i moment. Among them, the target dimensions at the i-3 moment are the (w-1)-th dimension, the (w-2)-th dimension, and the (w-3)-th dimension; the target dimensions at the i-2 moment are the (w-1)-th dimension, the (w-2)-th dimension, and the (w-3)-th dimension; the target dimensions at the i-1 moment are the w-th dimension, the (w-1)-th dimension, and the (w-2)-th dimension; the target dimensions at the i moment are the (w-1)-th dimension and the (w-2)-th dimension. Obtain the intersection of the target dimensions at the i moment and the i-1 moment to get the dimension intersection {the (w-1)-th dimension, the (w-2)-th dimension}; similarly, obtain the intersection of the target dimensions at the i-1 moment and the i-2 moment to get the dimension intersection {the (w-1)-th dimension, the (w-2)-th dimension}; obtain the intersection of the target dimensions at the i-2 moment and the i-3 moment to get the dimension intersection {the (w-1)-th dimension, the (w-2)-th dimension, the (w-3)-th dimension}. Thus, the intersections of the target dimensions of every two adjacent moments in the th target time period can be obtained, and the corresponding dimension intersections are obtained.

[0073] Record continuously adjacent identical dimension intersections as a new intersection. For example, the dimension intersections of the target dimensions at the i moment and the i-1 moment and the dimension intersections at the i-1 moment and the i-2 moment are both {the (w-1)-th dimension, the (w-2)-th dimension}, so this dimension intersection is merged into one intersection and recorded as the new intersection. Thus, the number of new intersections in the m-th target time period is 2, and the time length C spanned by each new intersection is obtained. For example, the time length spanned by the above new intersection {the (w-1)-th dimension, the (w-2)-th dimension} is 3, that is, the i moment, the i-1 moment, and the i-2 moment.

[0074] The more the number of target dimensions included in the new intersection, the more it indicates that data in different dimensions are affected. That is, there may be multiple factors causing environmental changes in this time period, and the environmental stability is poor. And the longer the time length spanned by each new intersection, the longer the state of unstable environment in the current production batch lasts, and the greater the impact of the data collected in this time period on the quality of the medicinal wine. The higher the precision of the environmental data in the same time period of the next production batch needs to be guaranteed. Therefore, determine the attention factor for each new intersection. The attention factor reflects the degree of key attention required for each new intersection in this target time period. The higher the degree of key attention required, the larger the value of the corresponding attention factor. Then, the increase amplitude of the acquisition frequency of environmental data in the next production batch should be higher. For example, the attention factor for each new intersection can be determined according to the proportion of the number of target dimensions included in each new intersection in all dimensions, the time length of the time period spanned by the new intersection, and the instability of the environmental data of all target dimensions included in the new intersections of all moments in the target time period.

[0075] Obtain the After all the new intersections of a target time period, determine the intersection of all the new intersections as the target dimension intersection of the th target time period. Since the th target time period has only one new intersection {the (w - 1)th dimension, the (w - 2)th dimension}, at this time, the target dimension intersection of the th target time period is {the (w - 1)th dimension, the (w - 2)th dimension}.

[0076] The ratio of the number of target dimensions included in the target dimension intersection of the th target time period to the maximum value among the number of target dimensions at all times in the th target time period , is denoted as the dimension data consistency of the th target time period . The larger the value of the dimension data consistency of the th target time period , the smaller the change of environmental factors at different times in the th target time period. For example, the temperature is controlled within a relatively stable range, and there will be no situation of sudden increase or decrease, providing a stable environmental basis for the processes such as the fermentation and aging of medicinal liquor. Therefore, the environmental data of this target time period has less impact on the production quality of medicinal liquor and lower importance.

[0077] Furthermore, based on the dimension data consistency of the th target time period , the duration of the th target time period, the attention factor of all the new intersections of the th target time period, and the number of target dimensions included in the new intersections, determine the importance of the th target time period, which reflects the degree of attention that should be paid to the environmental data of the th target time period. Among them, when the dimension data consistency of the th target time period is smaller, and the duration of the th target time period, the attention factor of all the new intersections of the th target time period, and the number of target dimensions included in the new intersections are larger, then the value of the importance of the corresponding th target time period is larger.

[0078] In this embodiment, by determining the attention factors and dimensional data consistency of the new intersections in each target period, the importance of each target period can be accurately determined according to the duration and dimensional data consistency of each target period, as well as the attention factors of all new intersections and the number of target dimensions included in the new intersections in each target period.

[0079] Further, in some possible implementation manners, determining the attention factor of each new intersection includes: Determining the average value of the instability of the environmental data of all target dimensions included in the new intersections at all times in each target period to obtain an instability mean value; Determining the product of the proportion of the number of target dimensions included in the new intersection in all dimensions, the time length of the time period spanned by the new intersection, and the instability mean value as the attention factor of the new intersection.

[0080] Specifically for the above steps, taking the th target period as an example, the attention factor of the th new intersection in the th target period is determined through the following formula : ; In the formula: represents the number of target dimensions included in the th new intersection in the th target period; represents the number of all dimensions; represents the time length of the time period spanned by the th new intersection in the th target period, represents the instability mean value corresponding to the th new intersection in the th target period, that is, the average value of the instability of the environmental data of all target dimensions included in the th new intersection at all times in the th target period.

[0081] In this embodiment, by calculating the product of the instability mean value corresponding to each new intersection in each target period, the proportion of the number of target dimensions included in each new intersection in all dimensions, and the time length of the time period spanned by each new intersection, the attention factor of each new intersection in each target period can be accurately determined.

[0082] Further, in some possible implementation manners, determining the importance of each target period includes: Determine the second product of the attention factor of each of the new intersections in each of the target time periods and the number of target dimensions included in each of the new intersections; Determine the third product of the accumulated value of all the second products and the duration of each of the target time periods; Determine the ratio of the third product to the dimensional data consistency as the importance of each of the target time periods.

[0083] Specifically for the above steps, taking the th target time period as an example, determine the importance of the th target time period through the following formula : ; In the formula: represents the number of new intersections in the th target time period; represents the number of target dimensions included in the th new intersection in the th target time period; represents the attention factor of the th new intersection in the th target time period; is the duration of the th target time period; represents the dimensional data consistency of the th target time period; represents the correction value of the consistency, is an infinitesimal value greater than 0, used to prevent the denominator from being zero, and can be reasonably valued according to needs. In this embodiment, is set.

[0084] In this embodiment, by determining the accumulated value of the product of the attention factor of each new intersection in each target time period and the number of target dimensions included in the new intersection, and based on this accumulated value, the duration of the target time period, and the dimensional data consistency of the target time period, the importance of each target time period can be accurately determined.

[0085] Furthermore, in some possible implementation manners, determining the environmental stability factor of each target time period includes: Determine the same dimensions in the target dimension intersections of each of the target time periods and the target time periods before and after it as the key dimensions; According to the number of dimensions of the key dimensions, and the time intervals between each of the target time periods and the target time periods before and after it, determine the sub-environmental stability factor between each of the target time periods and the target time periods before and after it; Determine the average value of the fourth product of the durations before and after each of the target time periods and the corresponding sub-environment stability factor as the environment stability factor for each of the target time periods.

[0086] Specifically for the above steps, in the process of determining the importance of each target time period, the intersection of the target dimensions of each target time period is determined. Taking the th target time period and the th target time period as an example for analysis, the same dimensions in the intersection of the target dimensions of the th target time period and the th target time period are recorded as the key dimensions of the th target time period and the th target time period. For example, since the intersection of the target dimensions of the th target time period is {the (w - 1)th dimension, the (w - 2)th dimension}, when the intersection of the target dimensions of the th target time period is {the (w - 1)th dimension, the (w - 2)th dimension, the (w - 3)th dimension}, then the key dimensions of the th target time period and the th target time period are {the (w - 1)th dimension, the (w - 2)th dimension}.

[0087] The more the number of key dimensions, it indicates that in two consecutive target time periods, the unstable dimension data appears more consistent and there is no significant change, indicating that the change of the environmental data in this th target time period is relatively stable, and the environmental change information contained in the dimension data of this th target time period is relatively small. Therefore, the acquisition frequency can be reduced in the next batch production process.

[0088] According to the number of dimensions of the key dimensions of the th target time period and the th target time period , and the time interval between the th target time period and the th target time period th target time period and the th target time period, determine the sub-environment stability factor between the . When the value of the time interval is smaller, and the number of dimensions of the key dimensions is smaller, it indicates that the environmental change in the medicinal liquor production workshop during the current batch production process is more complex and the stability is worse, and the value of the corresponding sub-environment stability factor is smaller. In this embodiment, determine the number of dimensions of the key dimensions The product is used as the sub - environment stability factor .

[0089] Similarly, the sub - environment stability factor between the th target time period and the th target time period can be determined . Respectively, take the duration of the th target time period and the duration of the th target time period as the weight coefficients of the sub - environment stability factors and . Calculate the product of each of the sub - environment stability factors and with their corresponding weight coefficients and then take the average, so as to obtain the environmental stability factor of the th target time period .

[0090] In this embodiment, by determining the key dimensions of each target time period and its adjacent target time periods before and after, and then based on the number of dimensions of the key dimensions and the time intervals between each target time period and its adjacent target time periods before and after, the environmental stability factor of each target time period can be accurately determined.

[0091] Furthermore, in some possible implementation manners, according to the retention factor, increase the acquisition frequency of the environmental data of the same time period corresponding to the target time period, including: Determine the frequency adjustment amount according to the acquisition frequency of the environmental data of the target time period and the retention factor; Determine the sum of the acquisition frequency of the environmental data of the target time period and the frequency adjustment amount, and use the sum as the increased acquisition frequency of the environmental data of the same time period corresponding to the target time period in the next - batch production process.

[0092] Specifically for the above steps, in this embodiment, in the next - batch production process, the acquisition frequency of the environmental data of the same time period corresponding to each target time period is increased through the following formula: ; In the formula: represents the acquisition frequency of the environmental data of the th target time period in the current - batch production process. In this embodiment, is once every 30 seconds; represents the retention factor of the th target time period; represents the ceiling symbol; represents the correction value of the retention factor, which is used to prevent When it is 0, it is impossible to increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period. It can be reasonably valued according to needs. In this embodiment, it is set ; It means the increased acquisition frequency of the environmental data in the same time period corresponding to the th target time period in the next batch of production processes.

[0093] In this embodiment, by determining the frequency adjustment amount according to the acquisition frequency of the environmental data in each target time period and the retention factor, and adding the acquisition frequency of the environmental data in each target time period to the frequency adjustment amount, the increased acquisition frequency of the environmental data in the same time period corresponding to each target time period in the next batch of production processes is accurately determined.

[0094] Based on the same inventive concept, an embodiment of the present invention also provides an environmental data acquisition system for a medicinal liquor production workshop, as Figure 5 shown. The acquisition system includes: a memory 501, a processor 502, and computer program code 503 stored in the memory 501 and running on the processor 502. Among them, when the processor 502 executes the computer program code 503, the system can execute any one of the environmental data acquisition methods for a medicinal liquor production workshop introduced above.

[0095] An embodiment of the present invention can divide the functions of the system according to the above method examples. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0096] Based on the same inventive concept, an embodiment of the present invention also provides a computer program product, which includes: computer program code. When the computer program code runs on a computer, the computer executes any one of the environmental data acquisition methods for a medicinal liquor production workshop introduced above.

[0097] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes any one of the environmental data acquisition methods for a medicinal liquor production workshop introduced above.

[0098] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for collecting environmental data in a medicinal liquor production workshop, characterized in that, Including the following steps: Obtain environmental data of different dimensions during the production process of the current batch in the medicinal liquor production workshop; Determine the instability of the environmental data of each dimension at each moment during the production process of the current batch; Determine the target dimension at each moment during the production process of the current batch for the dimension with instability greater than the instability threshold; Regard the moment when the proportion of the number of target dimensions in all dimensions is greater than the set ratio as the target moment, and consecutive adjacent target moments form a target time period, obtaining several target time periods; During the production process of the next batch, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period, and decrease the acquisition frequency of the environmental data in the same time period corresponding to the non-target time period.

2. The environmental data acquisition method for a medicinal liquor production workshop according to claim 1, wherein Determining the instability of the environmental data of each dimension at each moment during the production process of the current batch includes: Determine the time window at each moment during the production process of the current batch; Determine the dimension data sequence corresponding to the environmental data of each dimension in the time window; According to the fluctuation situation of the dimension data sequence, determine the instability of the environmental data of each dimension at each moment during the production process of the current batch.

3. A method for collecting environmental data in a medicinal liquor production workshop according to claim 2, characterized in that, Determining the instability of the environmental data of each dimension at each moment during the production process of the current batch includes: Determine the maximum value sequence composed of all maximum values in the dimension data sequence; Determine the maximum value and the minimum value in the maximum value sequence, the extreme difference formed by the difference between the maximum value and the minimum value, and the time interval between the maximum value and the minimum value; Determine the maximum value between the maximum value and the minimum value in the maximum value sequence as the reference maximum value; Determine the valley sequence composed of the dimension data of each dimension between every two adjacent reference maximum values in the dimension data sequence; Determine the fluctuation law factor according to the change trend difference between any two valley sequences; According to the number of reference maximum values, the extreme difference, the time interval, and the fluctuation law factor, determine the instability of the environmental data of each dimension at each moment during the production process of the current batch.

4. A method for collecting environmental data in a medicinal liquor production workshop according to any one of claims 1-3, characterized in that, Increasing the acquisition frequency of the environmental data in the same time period corresponding to the target time period includes: According to the distribution of the target dimensions at each moment in each target time period, determine the importance of each target time period; According to the time interval between adjacent target time periods and the distribution similarity of the target dimensions at each moment between adjacent target time periods, determine the environmental stability factor of each target time period; According to the importance and the environmental stability factor, determine the retention factor of each target time period; During the production process of the next batch, according to the retention factor, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period.

5. A method for collecting environmental data in a medicinal liquor production workshop according to claim 4, characterized in that, Determining the importance of each target time period includes: Determine the intersection of the target dimensions of every two adjacent moments in each target time period to obtain the dimension intersection; Regard consecutive adjacent identical dimension intersections as a new intersection, obtain several new intersections, and determine the attention factor of the new intersections; According to the several new intersections, determine the target dimension intersection of each target time period; Determine the maximum value of the number of target dimensions at all times in each of the target time periods; Determine the ratio of the number of target dimensions included in the intersection of the target dimensions to the maximum value of the number as the dimensional data consistency for each of the target time periods; Determine the importance of each of the target time periods based on the duration and dimensional data consistency of each of the target time periods, as well as the attention factors of all the new intersections and the number of target dimensions included in the new intersections for each of the target time periods.

6. The method for collecting environmental data of a medicinal liquor production workshop according to claim 5, wherein, Determine the attention factors of the new intersections, including: Determine the average value of the instability of the environmental data of all the target dimensions included in the new intersections at all times in each of the target time periods to obtain the instability mean value; Determine the product of the proportion of the number of target dimensions included in the new intersections in all dimensions, the time length of the time period spanned by the new intersections and the instability mean value as the attention factor of the new intersections.

7. A method for collecting environmental data in a medicinal liquor production workshop according to claim 5, characterized in that Determine the importance of each of the target time periods, including: Determine the second product of the attention factor of each of the new intersections and the number of target dimensions included in each of the new intersections for each of the target time periods; Determine the third product of the accumulated value of all the second products and the duration of each of the target time periods; Determine the ratio of the third product to the dimensional data consistency as the importance of each of the target time periods.

8. A method for collecting environmental data in a medicinal liquor production workshop according to claim 5, characterized in that, Determine the environmental stability factor of each of the target time periods, including: Determine the same dimensions in the intersections of the target dimensions of each of the target time periods and the target time periods before and after it as the key dimensions; Determine the sub-environmental stability factor between each of the target time periods and the target time periods before and after it according to the number of dimensions of the key dimensions and the time intervals between each of the target time periods and the target time periods before and after it; Determine the average value of the fourth product of the durations of the target time periods before and after each of the target time periods and the corresponding sub-environmental stability factors as the environmental stability factor of each of the target time periods.

9. A method for collecting environmental data in a medicinal liquor production workshop according to claim 4, characterized in that, According to the retention factor, increase the acquisition frequency of the environmental data in the same time period corresponding to the target time period, including: Determine the frequency adjustment amount according to the acquisition frequency of the environmental data of the target time period and the retention factor; Determine the sum of the acquisition frequency of the environmental data of the target time period and the frequency adjustment amount, and use the sum as the increased acquisition frequency of the environmental data in the same time period corresponding to the target time period in the next batch of production processes.

10. An environmental data acquisition system for a medicinal liquor production workshop, characterized in that, It includes a memory, a processor, and executable computer program code stored in the memory and executable on the processor. When the processor executes the computer program code, it executes a method for collecting environmental data in a medicinal liquor production workshop as described in any one of claims 1 to 9.

Images