Traditional Chinese medicine formula granule quality tracing system based on data fusion

Abstract

The invention provides a traditional Chinese medicine formula granule quality tracing system based on data fusion, and relates to the technical field of data processing. Multiplying the actual value of each key feature by the corresponding weight and the dynamic adjustment factor to obtain a weighted key feature value; summing all the weighted key characteristic values to obtain a comprehensive quality index; comparing the comprehensive quality index with a corresponding quality standard to obtain a comparison result; according to a comparison result, performing quality judgment on the new data of the traditional Chinese medicine formula granules, and if the comprehensive quality index is lower than a quality standard, judging that the traditional Chinese medicine formula granules in the corresponding batch have quality problems; and analyzing the weighted key feature values to obtain key features having negative effects on the comprehensive quality index. According to the invention, the efficiency and accuracy of quality problem processing are improved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a quality tracing system for Chinese medicine formula granules based on data fusion. Background Art

[0002] The traditional quality traceability system of Chinese medicine granules has the following defects: For example, some can only collect data from partial links and cannot fully cover the entire process of Chinese medicine granules from production to sales, which leads to incomplete data and reduced traceability accuracy.

[0003] Secondly, in traditional quality traceability, the extraction of key quality characteristics mostly relies on manual experience and simple statistical analysis. Therefore, it is difficult to accurately capture the key factors affecting the quality of Chinese medicine formula granules.

[0004] For example, when calculating the quality index, some traditional methods use fixed weights and static evaluation criteria, ignoring the dynamic changes in the actual situation. This evaluation method may not be able to adapt to the ever-changing production environment and market demand, resulting in insufficient accuracy and real-time quality evaluation results. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a quality traceability system for Chinese medicine formula granules based on data fusion, thereby improving the efficiency and accuracy of quality problem handling.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: First, the quality traceability method of Chinese medicine formula granules based on data fusion includes: Data collection module, used to collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; A data preprocessing module, used for preprocessing parameter data to obtain preprocessed data; An extraction module is used to extract key features related to the quality of Chinese medicine granules from preprocessed data; The quality assessment calculation module is used to assign a weight to each key feature based on historical data; define a dynamic adjustment factor based on actual conditions; multiply the actual value of each key feature by its corresponding weight and the dynamic adjustment factor to obtain a weighted key feature value; sum all weighted key feature values ​​to obtain a comprehensive quality index; compare the comprehensive quality index with the corresponding quality standard to obtain a comparison result; The quality traceability module is used to make quality judgments on the new TCM formula granules data based on the comparison results. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; analyze the weighted key feature values ​​to obtain the key features that have a negative impact on the comprehensive quality index; locate the key nodes with problems based on the production batch information and the weights and dynamic adjustment factors of the key features; The prediction module is used to connect key nodes with production information of each link through the established quality traceability model to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

[0007] Furthermore, key features related to the quality of TCM granules are extracted from the preprocessed data, including: Receiving pre-processed data related to the Chinese medicine formula granules, and referring to a pre-established feature library, which contains all features related to the quality of the Chinese medicine formula granules, including chemical component content, processing temperature, humidity and timestamp; According to the pre-established feature library, the corresponding feature fields are filtered out from the pre-processed data; The correlation coefficient between the feature fields and the quality indicators is calculated to obtain the key features.

[0008] Furthermore, the correlation coefficient between the feature fields and the quality indicators is calculated to obtain key features, including: pass Calculate the correlation coefficient between the feature field and the quality index; The key features are obtained by screening based on the correlation coefficient; in, represents the correlation coefficient, Represents the total number of observations, which indicates the number of samples in the data set; and Represents the first of two variables observations; From 1 to , traverse all samples; Represents the summation symbol, indicating from 1 to The summation of all sample points; Representation variables The mean of Representation variables The mean of Representation variables No. The difference between a value and its mean; Representation variables No. The difference between a value and its mean; Indicates the index of the sample point currently being processed, used to traverse from 1 to All sample points of and is the subscript in the summation, used to calculate the variable and The mean of , in the mean calculation, traverse from 1 to All and value.

[0009] Furthermore, a weight is assigned to each key feature based on historical data, including: According to historical data, Assign a weight to each key characteristic; in, It is The correlation coefficient between the key features and the quality indicators; and is the adjustment factor; is the total number of key features; It is The key feature is The probability of a value; It is The number of values ​​of the key features; It is The correlation coefficient between the key features and the quality indicators; It is The number of values ​​of the key features; It is The key feature is The probability of a value; is the index of the key feature; In calculating the The index used when the information entropy of the key features is is the index used when calculating the information entropy of each key feature in the denominator; is the index used in the denominator to iterate over all key features.

[0010] Furthermore, the calculation formula of the dynamic adjustment factor is: ; in, and is the weight coefficient; and are the weights of temperature and humidity respectively; and are the ideal temperature and humidity values ​​respectively; and are the actual measured temperature and humidity values ​​respectively; and are the maximum possible ranges of temperature and humidity, respectively; is the number of critical process parameters; It is The actual value of the process parameters; It is Target values ​​of process parameters; It is the maximum possible variation range of the process parameters.

[0011] Furthermore, the weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the comprehensive quality index, including: Performing data standardization processing on the weighted eigenvalues ​​to obtain standardized data; Calculate the statistics of each weighted eigenvalue in the standardized data, including the mean, standard deviation, median, and quartile, to understand the distribution of the standardized data and the range of variation of the eigenvalues; Based on the statistics, a threshold is set to determine whether the weighted feature value is abnormal; Traverse all weighted feature values, compare each weighted feature value with the threshold, and mark the abnormal weighted feature values; For each weighted feature value marked as abnormal, the corresponding weighted feature value is removed from the standardized data, and the comprehensive quality index is recalculated based on the remaining weighted feature values; Compare the difference between the original comprehensive quality index and the comprehensive quality index after removing abnormal features; All abnormal weighted eigenvalues ​​are sorted according to their respective impacts on the comprehensive quality index, from large to small; a negative impact threshold is set. When the impact of the abnormal weighted eigenvalues ​​on the comprehensive quality index exceeds the negative impact threshold, the abnormal weighted eigenvalues ​​are regarded as key features that have a negative impact on the overall quality; the key features that exceed the negative impact threshold are screened out from the sorted list of abnormal weighted eigenvalues.

[0012] Furthermore, based on the production batch information and the weights and dynamic adjustment factors of key features, the key nodes with problems are located, including: For each production batch, the actual measured value of each key characteristic is multiplied by its corresponding weight to obtain a weighted score; Multiply the weighted value by the corresponding dynamic adjustment factor to reflect the actual situation in the current production environment; Compare the weighted scores of different production batches to identify batches with abnormal scores; According to the batches with abnormal scores, analyze each link in their production process, including raw material quality and equipment status, to obtain the production process analysis results; Based on production process analysis and weighted scores, key nodes that lead to product quality decline are identified.

[0013] The second aspect is the quality traceability method of Chinese medicine granules based on data fusion, including: Collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; Preprocessing the parameter data to obtain preprocessed data; Extract key features related to the quality of TCM granules from preprocessed data; According to historical data, a weight is assigned to each key feature; a dynamic adjustment factor is defined according to actual conditions; the actual value of each key feature is multiplied by its corresponding weight and the dynamic adjustment factor to obtain the weighted key feature value; all weighted key feature values ​​are summed to obtain a comprehensive quality index; the comprehensive quality index is compared with the corresponding quality standard to obtain a comparison result; According to the comparison results, the quality of the new TCM formula granules data is judged. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; the weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the comprehensive quality index; based on the production batch information and the weights and dynamic adjustment factors of the key features, the key nodes with problems are located; Through the established quality traceability model, key nodes are connected with the production information of each link to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

[0014] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0015] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0016] The above scheme of the present invention includes at least the following beneficial effects.

[0017] Through the data acquisition module, the system can comprehensively and automatically collect parameter data of Chinese medicine formula granules in various links of production, processing, transportation, storage and sales. This comprehensive data collection ensures the integrity and accuracy of the data. The data preprocessing module can effectively clean, convert and standardize the collected parameter data to obtain high-quality preprocessed data, which not only improves the quality of the data, but also reduces the complexity and error rate of subsequent processing.

[0018] The extraction module uses data analysis technology to accurately extract key features related to the quality of Chinese medicine granules from preprocessed data. The quality assessment calculation module combines historical data with actual conditions, and realizes dynamic assessment of the quality of Chinese medicine granules by dynamically adjusting factors and weight distribution. The quality traceability module can quickly determine the quality problems of Chinese medicine granules based on the comparison results of the comprehensive quality index and quality standards, and accurately locate the key nodes with problems by analyzing the weighted key feature values, thereby improving the efficiency and accuracy of quality problem handling.

[0019] The prediction module can predict potential quality problems through the established quality traceability model, and connect key nodes with production information of each link to form a complete quality traceability chain. This prediction and prevention capability helps enterprises take measures in advance to avoid the occurrence of quality problems or reduce their impact. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of a quality traceability system for Chinese medicine formula granules based on data fusion provided by an embodiment of the present invention.

[0021] Figure 2 It is a schematic flow chart of a method for tracing the quality of Chinese medicine formula granules based on data fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] like Figure 1 As shown, the embodiment of the present invention proposes a Chinese medicine formula granule quality traceability system based on data fusion, including: Data collection module, used to collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; A data preprocessing module, used for preprocessing parameter data to obtain preprocessed data; An extraction module is used to extract key features related to the quality of Chinese medicine granules from preprocessed data; The quality assessment calculation module is used to assign a weight to each key feature based on historical data; define a dynamic adjustment factor based on actual conditions; multiply the actual value of each key feature by its corresponding weight and the dynamic adjustment factor to obtain a weighted key feature value; sum all weighted key feature values ​​to obtain a comprehensive quality index; compare the comprehensive quality index with the corresponding quality standard to obtain a comparison result; The quality traceability module is used to make quality judgments on the new TCM formula granules data based on the comparison results. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; analyze the weighted key feature values ​​to obtain the key features that have a negative impact on the comprehensive quality index; locate the key nodes with problems based on the production batch information and the weights and dynamic adjustment factors of the key features; The prediction module is used to connect key nodes with production information of each link through the established quality traceability model to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

[0024] In the embodiment of the present invention, the comprehensive and automated collection of data on the entire life cycle (production, processing, transportation, storage and sales) of Chinese medicine formula granules is achieved, ensuring the timeliness and accuracy of the data, and providing a rich data source for subsequent quality traceability. By performing pre-processing operations such as cleaning, conversion and standardization on the collected raw data, noise and outliers in the data are effectively eliminated, and the quality and availability of the data are improved. Using data mining technology, key features closely related to the quality of Chinese medicine formula granules are accurately extracted from the pre-processed data, and these features can directly reflect the quality status of the product.

[0025] By combining historical data and dynamic adjustment factors and assigning reasonable weights to each key feature, a dynamic and quantitative evaluation of the quality of Chinese medicine granules is achieved. This evaluation method is not only flexible, but also can more truly reflect the actual quality level of the product, thereby improving the accuracy and credibility of quality assessment.

[0026] Based on the comparison results between the comprehensive quality index and the quality standards, it is possible to quickly and accurately determine whether the Chinese medicine granules have quality problems, and by analyzing the weighted key feature values, accurately locate the specific links and factors that affect the product quality.

[0027] Through the established quality traceability model, the production information of each link is connected with the key nodes in series, forming a complete quality traceability chain. This not only helps enterprises to fully understand the formation process of product quality, but also predicts potential quality risks. At the same time, through the feedback of traceability results, the quality traceability model can be continuously improved and optimized to improve the accuracy and effectiveness of prediction.

[0028] In another preferred embodiment of the present invention, in the production link, data such as raw material information, production process parameters, and equipment operating status are collected; in the processing link, key data such as processing methods, processing environment, and processing time are collected; the transportation link focuses on transportation methods, transportation time, and changes in temperature and humidity during transportation; the storage link collects warehouse environment data (such as temperature and humidity, light), inventory status, and inventory time; the sales link records sales time, location, customer feedback, and other information.

[0029] Use sensor technology to monitor key environmental parameters such as temperature, humidity, and pressure in real time; use RFID (radio frequency identification) or QR code technology to track the movement of materials and products; integrate information systems such as enterprise resource planning (ERP) and manufacturing execution system (MES) to automatically capture data in the production and processing process; transmit the collected data to the data center in real time via wired or wireless networks, and establish a safe and reliable database in the data center to store and manage this data.

[0030] The data preprocessing module is immediately followed by the data acquisition module and is responsible for cleaning, converting and standardizing the collected raw data to improve data quality and availability; identifying and removing duplicate records to ensure data uniqueness; detecting and processing missing values, and using deletion, mean filling, interpolation and other methods to complete them according to data characteristics and business rules; using statistical methods (such as Z-score, IQR, etc.) to identify outliers and perform corresponding processing (such as replacement, deletion, or retention but marking); performing data type conversion, such as converting text data into numerical data, to facilitate subsequent analysis and calculation; performing data normalization or standardization processing to eliminate dimensional differences and make different features comparable, encoding categorical data (such as unique hot encoding), formulating data standards according to business needs, formatting data, and extracting key features related to the quality of Chinese medicine formula granules, such as raw material quality indicators, production process stability indicators, etc.

[0031] For example, taking a Chinese medicine granule manufacturer as an example, its data collection and preprocessing process is as follows: Install temperature and humidity sensors on the production line to monitor and record the temperature and humidity changes in the production environment in real time; use RFID tags to track the movement path of raw materials from storage to the production line to ensure that the source of raw materials can be traced; collect key process parameters in the production process, such as mixing time, drying temperature, etc. through the MES system. Install GPS and temperature and humidity monitoring equipment on transportation vehicles to record transportation trajectories and environmental changes. Clean the collected temperature and humidity data to remove outliers caused by sensor failure. Associate RFID tag data with raw material quality information to form a complete raw material traceability data set. Normalize the process parameter data in the MES system, combine GPS data and temperature and humidity monitoring data, and analyze the impact of environmental stability during transportation on product quality.

[0032] In another preferred embodiment of the present invention, key features related to the quality of Chinese medicine granules are extracted from the preprocessed data, including: Receive the pre-processed data related to the Chinese medicine formula granules, and refer to the pre-established feature library, which contains all the features related to the quality of the Chinese medicine formula granules, including chemical composition content, processing temperature, humidity and timestamp; according to the pre-established feature library, select the corresponding feature fields from the pre-processed data, including: Establish a secure data interface to receive the data related to Chinese medicine formula granules transmitted from the data preprocessing module; when the data preprocessing module completes the data preprocessing, the data receiving operation is triggered, and the preprocessed data is received and stored in the designated data warehouse or database through the data interface to ensure the integrity and security of the data; before the feature screening, a complete feature library has been established. The feature library should contain all the features related to the quality of Chinese medicine formula granules, such as chemical component content (such as active ingredients, impurity components, etc.), processing temperature, humidity, timestamp, etc.; each feature should have a clear definition and description in the feature library; from the preprocessed data, traverse each field one by one, and for each field, match it with the features in the feature library to determine whether it belongs to the feature related to the quality of Chinese medicine formula granules. If the field matches a feature in the feature library successfully, the field is marked as a key feature field; if the field does not match any feature in the feature library, it is regarded as a non-key feature field, which can be selectively retained or discarded, depending on business needs and data redundancy. The selected key feature fields are sorted to form a new data set or data table.

[0033] The correlation coefficient between the feature fields and the quality indicators is calculated to obtain the key features.

[0034] In an embodiment of the present invention, by referring to a pre-established feature library, the system can accurately screen out feature fields directly related to the quality of Chinese medicine formula granules from a large amount of preprocessed data. This targeted data extraction method greatly reduces the amount of calculation for subsequent analysis and improves data processing efficiency. Calculating the correlation coefficient between the feature field and the quality index helps to identify the key features that have the greatest impact on the quality of Chinese medicine formula granules. Quality assessment based on these key features can more accurately reflect the actual quality status of the product, thereby improving the reliability of quality assessment. After understanding which features are critical to quality, enterprises can allocate resources more reasonably, give priority to and improve those production links and process parameters that have a significant impact on quality. This optimized resource allocation strategy helps enterprises improve production efficiency while reducing quality risks. The extracted key features and their correlation information with quality indicators provide strong data support for enterprises to formulate quality management strategies and optimize production processes. In the quality traceability process, the extraction of key features helps to quickly locate specific links and factors that may affect product quality, thereby improving traceability efficiency.

[0035] In another preferred embodiment of the present invention, the correlation coefficient between the feature field and the quality index is calculated to obtain the key feature, including: pass Calculate the correlation coefficient between the feature field and the quality index; The key features are obtained by screening based on the correlation coefficient; in, represents the correlation coefficient, Represents the total number of observations, which indicates the number of samples in the data set; and Represents the first of two variables observations; From 1 to , traverse all samples; Represents the summation symbol, indicating from 1 to The summation of all sample points; Representation variables The mean of Representation variables The mean of Representation variables No. The difference between a value and its mean; Representation variables No. The difference between a value and its mean; Indicates the index of the sample point currently being processed, used to traverse from 1 to All sample points of and is the subscript in the summation, used to calculate the variable and The mean of , in the mean calculation, traverse from 1 to All and value.

[0036] In an embodiment of the present invention, by calculating the correlation coefficient, it is possible to accurately identify which feature fields have a significant impact on the quality of Chinese medicine formula granules, which helps companies focus on improving key production links. After understanding the key features, companies can optimize production processes in a targeted manner, improve product quality by adjusting these key features, and achieve more accurate quality control. By reducing excessive attention to non-critical links, companies can allocate resources more efficiently, thereby improving overall production efficiency. Accurately identifying key features helps companies avoid investing too many resources in unnecessary links, thereby reducing production costs.

[0037] In another preferred embodiment of the present invention, a weight is assigned to each key feature based on historical data, including: According to historical data, Assign a weight to each key characteristic; in, It is The correlation coefficient between the key features and the quality indicators; and is the adjustment factor; is the total number of key features; It is The key feature is The probability of a value; It is The number of values ​​of the key features; It is The correlation coefficient between the key features and the quality indicators; It is The number of values ​​of the key features; It is The key feature is The probability of a value; is the index of the key feature; In calculating the The index used when the information entropy of the key features is is the index used when calculating the information entropy of each key feature in the denominator; is the index used in the denominator to iterate over all key features.

[0038] In an embodiment of the present invention, by combining the correlation coefficient and information entropy, the weight allocation not only takes into account the direct correlation between the feature and the quality indicator, but also takes into account the diversity of the feature's values, thereby more comprehensively evaluating the importance of the feature. The weight allocation is based on historical data, which makes the evaluation more objective and accurate. By quantifying the impact of each key feature, enterprises can optimize production processes more specifically. After understanding the weight of each key feature, enterprises can allocate resources more reasonably and give priority to improving those features with larger weights and more significant impacts on quality. The weight allocation process is based on clear mathematical formulas and historical data, which enhances the transparency and credibility of decision-making.

[0039] In another preferred embodiment of the present invention, the calculation formula of the dynamic adjustment factor is: ; in, and is the weight coefficient; and are the weights of temperature and humidity respectively; and are the ideal temperature and humidity values ​​respectively; and are the actual measured temperature and humidity values ​​respectively; and are the maximum possible ranges of temperature and humidity, respectively; is the number of critical process parameters; It is The actual value of the process parameters; It is Target values ​​of process parameters; It is the maximum possible variation range of the process parameters.

[0040] In the embodiment of the present invention, the formula comprehensively considers multiple influencing factors, including temperature, humidity and key process parameters, and provides a comprehensive evaluation index for the dynamic adjustment of the production environment. and , and the weights of temperature and humidity and , the formula can flexibly adapt to different production needs and environmental conditions. Enterprises can adjust these weights according to actual conditions to reflect the different effects of various factors on product quality. The formula introduces the difference between the ideal value and the actual measured value, as well as the ratio of these values ​​to the maximum possible range of variation, so as to achieve precise control of the production environment. This helps to reduce fluctuations in the production process and improve the stability of product quality. By comparing the actual value of the process parameters with the target value, the formula helps enterprises to clarify the deviations in the production process, thereby improving production efficiency and product quality. Regularly calculate and monitor the changes in the dynamic adjustment factors, promptly identify problems in the production process and make improvements, which helps enterprises achieve continuous improvement and continuously improve production efficiency and product quality.

[0041] In another preferred embodiment of the present invention, the actual value of each key feature is multiplied by its corresponding weight and dynamic adjustment factor to obtain a weighted key feature value; all weighted key feature values ​​are summed to obtain a comprehensive quality index; the comprehensive quality index is compared with the corresponding quality standard to obtain a comparison result, including: Collect the actual values ​​of all key characteristics and ensure that they are up-to-date and accurate; obtain the weights corresponding to each key characteristic, which have been determined through previous analysis; calculate or obtain the current dynamic adjustment factor, which should be calculated based on real-time environmental conditions (such as temperature, humidity) and process parameters. For each key characteristic, multiply its actual value by the corresponding weight to obtain a preliminary weighted value. Multiply each preliminary weighted value by the dynamic adjustment factor to obtain the final weighted key characteristic value. This step is to take into account the current environmental and process conditions and adjust the characteristic values ​​in real time.

[0042] Sum up all weighted key feature values ​​to get a total value, namely the comprehensive quality index. This index comprehensively reflects the performance of all key features under the current environment and process conditions. Set or obtain the corresponding quality standard, which can be a common standard in the industry or a standard formulated within the enterprise according to actual needs. Compare the calculated comprehensive quality index with the quality standard. The result of the comparison can be a specific numerical difference or a grade rating (such as "qualified", "unqualified", "excellent", etc.). Output the comparison results in an appropriate form for relevant personnel to view and understand. The output can be in the form of reports, charts or other visualization tools. Interpret the comparison results to clarify the quality status of the current product or service, as well as the aspects that may need to be improved or optimized. According to the comparison results, formulate corresponding improvement measures or optimization plans. These measures may include adjusting process parameters, improving the production environment, strengthening quality control, etc. Repeat the above process regularly to continuously monitor and improve the quality of products or services.

[0043] In another preferred embodiment of the present invention, the new Chinese medicine formula granules data is subjected to quality judgment according to the comparison result. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of Chinese medicine formula granules has quality problems, including: Collect relevant data of new Chinese medicine formula granules, including actual test values ​​of each key feature, such as active ingredient content, dissolution rate, impurity content, etc., ensure the accuracy and completeness of the data, and perform necessary preprocessing on the data, such as cleaning and formatting, etc. Apply the previously determined weights and dynamic adjustment factor calculation formula, combined with the newly collected data, calculate the weighted key characteristic values, sum up all weighted key characteristic values, and obtain the comprehensive quality index of the batch of Chinese medicine formula granules.

[0044] Consult the relevant quality standard documents or databases to obtain the quality standard values ​​of Chinese medicine formula granules. This standard can be set according to historical data requirements, and the calculated comprehensive quality index is compared with the obtained quality standard. If the comprehensive quality index is lower than the quality standard, it is determined that there are quality problems in the batch of Chinese medicine formula granules. If the comprehensive quality index reaches or exceeds the quality standard, the quality of the batch of Chinese medicine formula granules is qualified. Record the judgment results in the quality management system or related documents. If there are quality problems, immediately generate a quality report to detail the problems and possible causes. For batches with quality problems, take corresponding corrective measures based on the analysis in the quality report, such as reprocessing, adjusting the formula, improving the production process, etc., monitor the implementation effect of the corrective measures, and make necessary adjustments to ensure the quality of subsequent production batches. Analyze the causes of quality problems, identify the weak links in the production process, and make continuous improvements to these links, such as optimizing production processes, improving employee skills, and updating equipment.

[0045] In another preferred embodiment of the present invention, the weighted key feature values ​​are analyzed to obtain key features that negatively affect the comprehensive quality index, including: Perform data standardization on the weighted eigenvalues ​​to obtain standardized data; calculate the statistics of each weighted eigenvalue in the standardized data, including the mean, standard deviation, median and quartile, to understand the distribution of the standardized data and the range of variation of the eigenvalues; set a threshold based on the statistics to determine whether the weighted eigenvalue is abnormal; traverse all weighted eigenvalues, compare each weighted eigenvalue with the threshold, and mark the abnormal weighted eigenvalues; for each weighted eigenvalue marked as abnormal, remove the corresponding weighted eigenvalue from the standardized data, and recalculate the comprehensive quality index based on the remaining weighted eigenvalues; compare the difference between the original comprehensive quality index and the comprehensive quality index after removing the abnormal features, specifically including: For each weighted eigenvalue, first calculate its overall mean and standard deviation; use the z-score standardization method to subtract the mean from each weighted eigenvalue and then divide it by the standard deviation to obtain the standardized data. For each weighted eigenvalue after standardization, calculate its mean and standard deviation to confirm whether the standardization process is correct (the mean should be close to 0 and the standard deviation should be close to 1 after standardization); calculate the median to understand the central trend of the data, and calculate the quartiles (Q1, Q2, Q3) to identify the distribution range of the data and potential outliers. Based on the calculated statistics, especially the quartiles, set the threshold for outlier detection. For example, 1.5 times or 3 times the IQR (interquartile range, i.e. Q3-Q1) can be used as the criterion for outliers. Any weighted eigenvalue lower than (Q1-1.5×IQR) or higher than (Q3+1.5×IQR) is considered anomaly. Traverse all the standardized weighted eigenvalues, compare each value with the threshold set above, and if a weighted eigenvalue exceeds the threshold range, it is marked as anomaly. For each weighted feature value marked as abnormal, it is removed from the standardized data set, and the remaining normal weighted feature values ​​are used to recalculate the comprehensive quality index according to the original comprehensive quality index calculation formula; the original comprehensive quality index is compared with the comprehensive quality index calculated after removing the abnormal features, and the difference between the two is analyzed to understand the impact of the abnormal feature values ​​on the comprehensive quality index.

[0046] All abnormal weighted feature values ​​are sorted according to their influence on the comprehensive quality index, from large to small; a negative impact threshold is set. When the influence of the abnormal weighted feature value on the comprehensive quality index exceeds the negative impact threshold, the abnormal weighted feature value is regarded as a key feature that has a negative impact on the overall quality; the key features that exceed the negative impact threshold are screened out from the sorted abnormal weighted feature value list, including: Ensure that the anomaly detection of weighted eigenvalues ​​has been completed and there is a list of all abnormal weighted eigenvalues; for each abnormal weighted eigenvalue, retain its original value, standardized value and the corresponding change in the comprehensive quality index (i.e., the change in the comprehensive quality index after removing the eigenvalue); for each abnormal weighted eigenvalue in the list, calculate its impact on the comprehensive quality index, which can be achieved by comparing the difference between the comprehensive quality index containing the eigenvalue and the comprehensive quality index after removing the eigenvalue, and save this impact (difference) as an important attribute of the abnormal eigenvalue. Sort them according to the impact of each abnormal weighted eigenvalue on the comprehensive quality index, and use an appropriate sorting algorithm (such as quick sort, merge sort, etc.) to arrange these eigenvalues ​​in order from the largest to the smallest impact. Based on historical data, industry specifications or expert advice, set a negative impact threshold, which is used to determine whether the abnormal weighted eigenvalue has a significant negative impact on the overall quality. Traverse the sorted list of abnormal weighted eigenvalues ​​and compare the impact of each eigenvalue with the negative impact threshold. If the impact of a certain eigenvalue exceeds the negative impact threshold, it is regarded as a key feature that has a negative impact on the overall quality. These key features are listed separately to form a new list or set.

[0047] In an embodiment of the present invention, by conducting an in-depth analysis of the weighted key eigenvalues, the method can accurately identify the key features that have a negative impact on the comprehensive quality index. By performing data standardization on the weighted eigenvalues, the impact of the dimensions and magnitudes between different eigenvalues ​​is eliminated, so that the eigenvalues ​​are comparable, and the accuracy and reliability of data analysis are improved. Calculating the statistics (such as mean, standard deviation, median and quartile) of each weighted eigenvalue in the standardized data helps to fully understand the distribution of the data and the range of variation of the eigenvalues. By setting a threshold based on the statistics, abnormal weighted eigenvalues ​​can be automatically detected. This greatly improves the efficiency of data processing and reduces the possibility of human errors. By comparing the difference between the original comprehensive quality index and the comprehensive quality index after removing the abnormal features, the method can quantitatively evaluate the specific impact of each abnormal feature on the comprehensive quality. Sorting all abnormal weighted eigenvalues ​​according to the degree of impact on the comprehensive quality index helps enterprises to clarify the priority of improvement work. Enterprises can give priority to those key features that have the greatest impact on the comprehensive quality index to achieve rapid improvement of product quality. Setting a negative impact threshold can ensure that the selected key features do have a significant negative impact on the overall quality. This avoids unnecessary improvement work and improves the efficient use of enterprise resources.

[0048] In another preferred embodiment of the present invention, locating the critical nodes with problems based on the production batch information and the weights and dynamic adjustment factors of the key features includes: For each production batch, multiply the actual measured value of each key feature by its corresponding weight to obtain a weighted score; multiply the weighted value by the corresponding dynamic adjustment factor to reflect the actual situation under the current production environment; compare the weighted scores of different production batches to obtain batches with abnormal scores; analyze each link in its production process, including raw material quality and equipment status, based on the batches with abnormal scores to obtain the production process analysis results; locate the key nodes that lead to product quality degradation based on the production process analysis and weighted scores, including: Collect the actual measurement values ​​of each key feature in each production batch, and ensure that each key feature has a corresponding weight value, which reflects the importance of the feature to the product quality; obtain or calculate the dynamic adjustment factor under the current production environment; for each production batch, multiply the actual measurement value of each key feature by its corresponding weight, and then add these products to obtain the weighted score of the batch; multiply the weighted score of each production batch by the corresponding dynamic adjustment factor to reflect the actual situation under the current production environment; compare the adjusted weighted scores of all production batches to identify batches with abnormally high or low scores, and use statistical methods (such as standard deviation, quartiles, etc.) to set the threshold of abnormal scores. For batches with abnormal scores, conduct in-depth analysis of each link in its production process, including quality inspection records of raw materials, equipment operation status logs, operator skill levels, etc., and find out the differences that may lead to a decline in product quality by comparing the production process data of normal batches and abnormal batches.

[0049] Integrate the production process analysis results and the adjusted weighted score data to ensure that they can correspond to the production batches; for production process analysis, pay special attention to key data such as raw material quality inspection reports, equipment maintenance records, operator logs, etc. Compare the weighted scores of different batches, and mark batches with abnormally low scores or large fluctuations, which may contain quality problems; preliminarily screen out the links in these abnormal batches that have problems in the production process analysis results, such as raw material quality fluctuations, equipment failure records, etc.

[0050] For the problematic links initially screened out, further analysis is performed on their correlation with abnormal weighted scores. For example, if a batch is found to have unstable quality during the raw material quality inspection, and the weighted score of the batch is also abnormally low, then the unstable raw material quality can be considered a key node.

[0051] Through statistical analysis and expert evaluation, we identify those links that have a significant impact on the weighted score and do have problems in the production process analysis as key nodes. Possible key nodes include but are not limited to: unstable raw material quality, performance degradation due to equipment aging, improper operation, unreasonable process parameter settings, etc.

[0052] In an embodiment of the present invention, by comprehensively considering the weights and dynamic adjustment factors of key features, the method can more accurately locate the key nodes that affect product quality. This helps enterprises to quickly find and solve problems and improve production efficiency and product quality. By comparing the weighted scores of different production batches, enterprises can allocate resources more wisely and devote more attention and resources to batches with abnormal scores and more problems, thereby improving the efficiency of resource utilization. The method enables enterprises to more clearly understand the impact of each link in the production process on product quality by conducting in-depth analysis of each link in the production process, thereby optimizing the production process. By locating the key nodes that lead to the decline in product quality, enterprises can more effectively manage risks and prevent potential product quality problems. The method not only helps enterprises locate existing problems, but also provides enterprises with a framework for continuous improvement. By continuously collecting and analyzing production data, enterprises can continuously optimize the weights and dynamic adjustment factors of key features, thereby continuously improving product quality and production efficiency.

[0053] In another preferred embodiment of the present invention, by establishing a quality traceability model, key nodes are connected in series with production information of each link to form a quality traceability chain; according to the quality traceability chain, the traceability results are obtained, including: Obtain and analyze the production process, understand the entire production process in detail, including raw material procurement, processing, quality inspection, packaging, warehousing, logistics and other links, determine the input, output and key control points of each link, and record them in the flow chart.

[0054] Identify key nodes, identify and record key nodes that have a significant impact on product quality, such as raw material acceptance, key process steps, important quality inspection links, etc., to ensure that each key node has a clear definition and identification. Design data structure and association, according to key nodes and business processes, design a reasonable data structure to store relevant information, such as raw material batches, production dates, process parameters, quality inspection results, etc. Use product unique identifiers (such as batch numbers, serial numbers) to associate data from each link to ensure data consistency and traceability.

[0055] Establish a data collection and transmission system, set up data collection points at each key node, and use automated means (such as barcode technology, RFID tags, sensors, etc.) to collect data in real time and accurately to ensure the efficiency and accuracy of data collection and reduce human errors. Build a database and traceability system, build a safe and reliable database to store and manage traceability data, develop user-friendly query interfaces and interfaces, and support quick query of traceability information through product identifiers. According to actual needs, set precise traceability query conditions, such as product batch number, production date, quality problem type, etc., to ensure that the query conditions can accurately match the products or production links that need to be traced.

[0056] To perform a drill-through query: Enter the set query conditions in the quality traceability system, and quickly locate the relevant key nodes and production information in the quality traceability chain according to the query conditions. The system displays the traceability results in an intuitive way (such as tables, charts or reports), including detailed data of key nodes and related information of production links. Users analyze the traceability results to determine the root cause and impact range of product quality problems. According to the analysis results, formulate corresponding improvement measures and prevention plans to improve product quality and production efficiency, feed back the improvement measures to the production process, and continuously improve and optimize the quality traceability model.

[0057] Regularly evaluate and optimize the quality traceability system to ensure that it adapts to changes in production processes and new traceability requirements, monitor data collection and transmission at key nodes to ensure the real-time and accuracy of data, and conduct regular training for employees to improve their proficiency and efficiency in using the quality traceability system.

[0058] like Figure 2 As shown, the embodiment of the present invention also provides a method for tracing the quality of Chinese medicine formula granules based on data fusion, including: Collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; Preprocessing the parameter data to obtain preprocessed data; Extract key features related to the quality of TCM granules from preprocessed data; According to historical data, a weight is assigned to each key feature; a dynamic adjustment factor is defined according to actual conditions; the actual value of each key feature is multiplied by its corresponding weight and the dynamic adjustment factor to obtain the weighted key feature value; all weighted key feature values ​​are summed to obtain a comprehensive quality index; the comprehensive quality index is compared with the corresponding quality standard to obtain a comparison result; According to the comparison results, the quality of the new TCM formula granules data is judged. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; the weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the comprehensive quality index; based on the production batch information and the weights and dynamic adjustment factors of the key features, the key nodes with problems are located; Through the established quality traceability model, key nodes are connected with the production information of each link to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

[0059] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0060] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0061] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0062] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The quality traceability system of Chinese medicine granules based on data fusion is characterized by: include: Data collection module, used to collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; A data preprocessing module, used for preprocessing parameter data to obtain preprocessed data; An extraction module is used to extract key features related to the quality of Chinese medicine granules from preprocessed data; A quality assessment calculation module is used to assign a weight to each key feature based on historical data; A dynamic adjustment factor is defined according to the actual situation; the actual value of each key feature is multiplied by its corresponding weight and the dynamic adjustment factor to obtain a weighted key feature value; all weighted key feature values ​​are summed to obtain a comprehensive quality index; the comprehensive quality index is compared with the corresponding quality standard to obtain a comparison result; The quality traceability module is used to make quality judgments on the new TCM formula granules data based on the comparison results. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; the weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the comprehensive quality index; Locate the critical nodes with problems based on production batch information, key feature weights, and dynamic adjustment factors; The prediction module is used to connect key nodes with production information of each link through the established quality traceability model to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

2. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 1 is characterized in that: Extract key features related to the quality of TCM granules from preprocessed data, including: Receiving pre-processed data related to the Chinese medicine formula granules, and referring to a pre-established feature library, which contains all features related to the quality of the Chinese medicine formula granules, including chemical component content, processing temperature, humidity and timestamp; According to the pre-established feature library, the corresponding feature fields are filtered out from the pre-processed data; The correlation coefficient between the feature fields and the quality indicators is calculated to obtain the key features.

3. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 2 is characterized in that: Calculate the correlation coefficient between the feature fields and the quality indicators to obtain key features, including: pass Calculate the correlation coefficient between the feature field and the quality index; The key features are obtained by screening based on the correlation coefficient; in, represents the correlation coefficient, Represents the total number of observations, which indicates the number of samples in the data set; and Represents the first of two variables observations; From 1 to , traverse all samples; Represents the summation symbol, indicating from 1 to The summation of all sample points; Representation variables The mean of Representation variables The mean of Representation variables No. The difference between a value and its mean; Representation variables No. The difference between a value and its mean; Indicates the index of the sample point currently being processed, used to traverse from 1 to All sample points of and is the subscript in the summation, used to calculate the variable and The mean of , in the mean calculation, traverse from 1 to All and value.

4. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 3 is characterized in that: Based on historical data, a weight is assigned to each key characteristic, including: According to historical data, Assign a weight to each key characteristic; in, It is The correlation coefficient between the key features and the quality indicators; and is the adjustment factor; is the total number of key features; It is The key feature is The probability of a value; It is The number of values ​​of the key features; It is The correlation coefficient between the key features and the quality indicators; It is The number of values ​​of the key features; It is The key feature is The probability of a value; is the index of the key feature; In calculating the The index used when the information entropy of the key features is is the index used when calculating the information entropy of each key feature in the denominator; is the index used in the denominator to iterate over all key features.

5. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 4 is characterized in that: The calculation formula of the dynamic adjustment factor is: ； in, and is the weight coefficient; and are the weights of temperature and humidity respectively; and are the ideal temperature and humidity values ​​respectively; and are the actual measured temperature and humidity values ​​respectively; and are the maximum possible ranges of temperature and humidity, respectively; is the number of critical process parameters; It is The actual value of the process parameters; It is Target values ​​of process parameters; It is the maximum possible variation range of process parameters.

6. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 5 is characterized in that: The weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the overall quality index, including: Performing data standardization processing on the weighted eigenvalues ​​to obtain standardized data; Calculate the statistics of each weighted eigenvalue in the standardized data, including the mean, standard deviation, median, and quartile, to understand the distribution of the standardized data and the range of variation of the eigenvalues; Based on the statistics, a threshold is set to determine whether the weighted feature value is abnormal; Traverse all weighted feature values, compare each weighted feature value with the threshold, and mark the abnormal weighted feature values; For each weighted feature value marked as abnormal, the corresponding weighted feature value is removed from the standardized data, and the comprehensive quality index is recalculated based on the remaining weighted feature values; Compare the difference between the original comprehensive quality index and the comprehensive quality index after removing abnormal features; All abnormal weighted eigenvalues ​​are sorted according to their respective impacts on the comprehensive quality index, from large to small; a negative impact threshold is set. When the impact of the abnormal weighted eigenvalues ​​on the comprehensive quality index exceeds the negative impact threshold, the abnormal weighted eigenvalues ​​are regarded as key features that have a negative impact on the overall quality; the key features that exceed the negative impact threshold are screened out from the sorted list of abnormal weighted eigenvalues.

7. The Chinese medicine formula granule quality traceability system based on data fusion according to claim 6 is characterized in that: Based on the production batch information, the weights of key features and dynamic adjustment factors, the key nodes with problems are located, including: For each production batch, the actual measured value of each key characteristic is multiplied by its corresponding weight to obtain a weighted score; Multiply the weighted value by the corresponding dynamic adjustment factor to reflect the actual situation in the current production environment; Compare the weighted scores of different production batches to identify batches with abnormal scores; According to the batches with abnormal scores, analyze each link in their production process, including raw material quality and equipment status, to obtain the production process analysis results; Based on production process analysis and weighted scores, key nodes that lead to product quality decline are identified.

8. A method for tracing the quality of Chinese medicine granules based on data fusion, characterized in that: Applicable to the system according to any one of claims 1 to 7, comprising: Collect parameter data of Chinese medicine granules in various links of production, processing, transportation, storage and sales; Preprocessing the parameter data to obtain preprocessed data; Extract key features related to the quality of TCM granules from preprocessed data; According to historical data, a weight is assigned to each key feature; a dynamic adjustment factor is defined according to actual conditions; the actual value of each key feature is multiplied by its corresponding weight and the dynamic adjustment factor to obtain the weighted key feature value; all weighted key feature values ​​are summed to obtain a comprehensive quality index; the comprehensive quality index is compared with the corresponding quality standard to obtain a comparison result; According to the comparison results, the quality of the new TCM formula granules data is judged. If the comprehensive quality index is lower than the quality standard, it is determined that the corresponding batch of TCM formula granules has quality problems; the weighted key feature values ​​are analyzed to obtain the key features that have a negative impact on the comprehensive quality index; based on the production batch information and the weights and dynamic adjustment factors of the key features, the key nodes with problems are located; Through the established quality traceability model, key nodes are connected with the production information of each link to form a quality traceability chain; based on the quality traceability chain, the traceability results are obtained.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 8 when executed by a processor.

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