A synchronous method, system and device for collecting and processing order information

Abstract

The present invention relates to the field of data management technology, and specifically discloses a synchronous method, system and device for collecting and processing order information, including the following steps: Step S01: Collect data of indicators of various dimensions of the supply chain, including but not limited to data sets of product attributes, quality supervision, capital flow monitoring and supplier relationships; Step S02: Preprocess and standardize the data collected in Step S01; Step S03: Set a multi-dimensional evaluation indicator system according to the characteristics of the supply chain and actual needs; Step S04: Given the historical data of indicators of various dimensions on a time section, calculate the change rate of each single indicator. The synchronous method and system for collecting and processing order information of the present invention can effectively improve the scientificity and efficiency of enterprise supply chain management, and provide a strong guarantee for enterprises to maintain their competitive advantages in a complex and changing market environment.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a synchronous method, system and device for collecting and processing order information. Background Art

[0002] In modern supply chain management, the effective collection, processing and synchronization of order information are key factors in ensuring the efficient operation of the supply chain. With the intensification of globalization and market competition, companies are facing increasingly complex supply chain networks and need to process large amounts of multi-dimensional data. These data include but are not limited to product attributes, quality supervision, capital flow monitoring and supplier relationships. Traditional supply chain management systems often have difficulty coping with such a complex data environment, resulting in problems such as information lag, inaccurate data and untimely management decisions.

[0003] Information asymmetry and data silos are common in supply chain management, leading to a lack of coordination and synchronization between various links in the supply chain, which in turn affects the efficiency and response speed of the overall supply chain. In addition, the diversity and complexity of data increase the difficulty of data processing, and traditional data processing methods are difficult to meet the needs of modern supply chain management. Especially in the face of emergencies (such as supply disruptions, drastic changes in market demand, etc.), companies need to quickly and accurately obtain and process data from all links in the supply chain in order to make timely decisions and adjustments.

[0004] In the existing technology, most supply chain management systems focus on single-dimensional data processing and lack the ability to comprehensively analyze multi-dimensional data. At the same time, the lack of effective data preprocessing and standardization methods makes it difficult to ensure data quality, affecting the accuracy of subsequent analysis and decision-making. The existing system also lacks a flexible evaluation index system and a difference coefficient calculation method, making it difficult to fully and accurately reflect the overall operation status of the supply chain.

[0005] In order to meet the above challenges, there is an urgent need for a method, system and device that can efficiently collect, process and synchronize order information, comprehensively collect, preprocess, standardize and analyze data in all dimensions of the supply chain, and provide accurate supply chain performance evaluation through a multi-dimensional evaluation index system and difference coefficient calculation to support the decision-making of corporate management. This method and system can not only improve the efficiency and accuracy of data processing, but also help companies optimize supply chain management and improve overall operational efficiency and market competitiveness through risk warning mechanisms and improvement suggestions. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a synchronous method, system and device for collecting and processing order information, aiming to solve the problems raised in the background technology, and realize comprehensive management and optimization of the supply chain through systematic data collection, preprocessing, analysis and evaluation. This method and system can help enterprises better understand and manage the supply chain, improve operational efficiency and decision-making accuracy.

[0007] To achieve the above object, the present invention provides the following technical solution: a synchronous method for collecting and processing order information, comprising the following steps:

[0008] Step S01: Collect data on indicators of various dimensions of the supply chain, including but not limited to data sets on product attributes, quality supervision, capital flow monitoring, and supplier relationships;

[0009] Step S02: preprocess and standardize the data collected in step S01, using the following formula for standardization: ;in, is the original data, is the mean of the data set, is the standard deviation of the data set, It is the standardized data;

[0010] Step S03: A multi-dimensional evaluation index system is set up according to the characteristics of the supply chain and actual needs. The system includes at least four dimensions: product attributes, quality supervision, capital flow monitoring, and supplier relationships;

[0011] Step S04: Given the historical data of each dimension indicator on the time section, calculate the change rate of each single indicator , and converted into the difference coefficient , the calculation formula is as follows: ;in, and are the indicator values ​​of the current period and the previous period respectively, is a function that maps the rate of change to the coefficient of difference;

[0012] Step S05: The difference coefficient of each index Multiply by their relative importance weights Get the overall evaluation score , the calculation formula is: ;in, is the number of indicators;

[0013] Step S06: Based on the obtained overall evaluation score And the corresponding timeline is drawn to show the overall operation status of the supply chain.

[0014] Preferably, the overall supply chain score is To analyze, Below the preset threshold When a risk occurs, the risk warning mechanism is triggered and remedial measures are recommended.

[0015] Preferably, the specific steps for triggering the risk warning mechanism are as follows:

[0016] S13a: The overall supply chain score calculated by the real-time monitoring system will automatically trigger a risk warning when the score is lower than the preset threshold;

[0017] S13b: Automatically generate a library of remediation recommendations based on risk type and severity, and intelligently select or combine the best remediation measures from the library to reduce the impact of supply chain disruptions;

[0018] S13c: Track the effectiveness of remedial measures, dynamically evaluate the operation of the supply chain, and adjust measures until the problem is resolved.

[0019] Preferably, a regression model is established by statistically processing the actual change range of each indicator, and using the absolute value of the correlation coefficient Determine the importance of each indicator, including It is The correlation coefficient between each indicator and the overall evaluation score.

[0020] Preferably, in step S02, the method for enhancing data preprocessing and standardization comprises the following steps:

[0021] S02a: Detect and fill missing values ​​in the collected data of each dimension of the supply chain, and use appropriate interpolation methods or predictions based on other relevant variables to fill missing data;

[0022] S02b: Identify and process outliers, use the IQR method to identify and remove or correct outliers to ensure data quality;

[0023] S02c: Implement multi-stage standardization processing, adopt different standardization strategies for different types of data, and improve the accuracy of standardization effects.

[0024] Preferably, the difference coefficient function in step S04 is It is further defined as a piecewise function, which divides the rate of change into different intervals according to the positive and negative sign and absolute value of the rate of change, and assigns each interval a specific difference coefficient value to more accurately reflect the impact of the degree and direction of indicator change on the overall difference of the supply chain.

[0025] A synchronous system for collecting and processing order information, comprising:

[0026] The data collection subunit is used to collect data on indicators of various dimensions of the supply chain;

[0027] The data mining subunit is used to preprocess and standardize the collected data;

[0028] The data analysis subunit is used to design a multi-dimensional evaluation index system, calculate the coefficient of difference, calculate the overall evaluation score, and modify and improve the evaluation report based on feedback.

[0029] Preferably, the operation process of the data acquisition subunit is as follows: configure the data source and interface parameters; execute the data acquisition task, capture data regularly or in real time; perform preliminary verification and cleaning on the collected data; store the processed data in the designated location;

[0030] The operation flow of the data mining subunit is as follows: reading the original data stored in the data acquisition subunit; applying the preprocessing algorithm to clean and correct the data; using the standardization processing module to standardize the data; storing the processed data for use by the data analysis subunit;

[0031] The operation process of the data analysis subunit is as follows: using the evaluation index system design tool to set up the evaluation index system; reading the data processed by the data mining subunit; calculating the difference coefficient and the overall evaluation score of each index; generating an evaluation report and displaying the result charts and suggestions; collecting user feedback and modifying and improving the evaluation report based on the feedback.

[0032] The synchronization device for collecting and processing the order information stores a synchronization method program for collecting and processing the order information. When the synchronization method program for collecting and processing the order information is executed by the processor, the steps of a synchronization method for collecting and processing the order information are implemented.

[0033] The invention provides a method, system and device for synchronously collecting and processing order information, which has the following beneficial effects:

[0034] 1. Multi-dimensional data collection: covering multiple dimensions such as product attributes, quality supervision, capital flow monitoring and supplier relationships, comprehensively reflecting all aspects of the supply chain.

[0035] 2. Data preprocessing and standardization: Use scientific preprocessing and standardization methods to ensure data accuracy and consistency.

[0036] 3. Calculation of coefficient of variation: Quantitatively analyze the variation of the supply chain by calculating the rate of change and coefficient of variation of each indicator.

[0037] 4. Comprehensive scoring and graph display: Calculate the overall score based on the difference coefficient and weight, and draw a graph to display the supply chain operation status.

[0038] 5. Real-time monitoring and risk warning: Real-time monitoring of supply chain scores, timely warning of potential risks, and generation of remedial action suggestions.

[0039] In summary, the synchronous method and system for collecting and processing order information of the present invention can effectively improve the scientificity and efficiency of enterprise supply chain management, and provide a strong guarantee for enterprises to maintain their competitive advantages in a complex and changing market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a principle block diagram of a synchronous system for collecting and processing order information of the present invention;

[0041] Figure 2 This is a flow chart of a data acquisition subunit of a synchronous system for acquiring and processing order information of the present invention;

[0042] Figure 3 This is a flow chart of a data mining subunit of a synchronization system for collecting and processing order information of the present invention;

[0043] Figure 4 This is a flow chart of a data analysis subunit of a synchronization system for collecting and processing order information of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment, the present invention provides a technical solution: a synchronous method for collecting and processing order information, comprising the following steps:

[0046] Step S01: Collect data on indicators of various dimensions of the supply chain, including but not limited to data sets on product attributes, quality supervision, capital flow monitoring, and supplier relationships;

[0047] Collect relevant data from all dimensions of the supply chain for subsequent analysis, including but not limited to data sets such as product attributes (such as product specifications, production quantity), quality supervision (such as quality control indicators, pass rate), capital flow monitoring (such as cash flow, financial statements) and supplier relationships (such as the number of suppliers, length of cooperation). Obtain relevant data through the company's internal information system, database or other data sources to ensure the integrity and accuracy of the data.

[0048] Step S02: preprocess and standardize the data collected in step S01, using the following formula for standardization: ;in, is the original data, is the mean of the data set, is the standard deviation of the data set, It is the standardized data;

[0049] Formula explanation:

[0050] Raw data ; Specific values ​​collected from the data set, such as the inventory quantity of a product;

[0051] Mean ; The average of all data points in a data set, indicating the central tendency of the data;

[0052] Standard Deviation ; Describes the degree of dispersion of the data, indicating the average deviation of the data points from the mean;

[0053] Standardizing Data ; Standardized data is obtained by subtracting the mean from the original data and dividing by the standard deviation. This transforms the data into a standard normal distribution with a mean of 0 and a standard deviation of 1;

[0054] After standardization, each data point is expressed as a multiple of the standard deviation relative to the mean, so that data with different characteristics can be analyzed and compared on the same scale, eliminating the impact of dimension.

[0055] Step S03: A multi-dimensional evaluation index system is set up according to the characteristics of the supply chain and actual needs. The system includes at least four dimensions: product attributes, quality supervision, capital flow monitoring, and supplier relationships;

[0056] Step S04: Given the historical data of each dimension indicator on the time section, calculate the change rate of each single indicator , and converted into the difference coefficient , the calculation formula is as follows: ;in, and are the indicator values ​​of the current period and the previous period respectively, is a function that maps the rate of change to the coefficient of difference;

[0057] Formula explanation:

[0058] is the indicator value for the current period; for example, if quarterly data is being analyzed, it is the sales for the current quarter;

[0059] is the indicator value of the previous period; for example, the sales of the previous quarter;

[0060] Rate of change , this formula calculates the rate of change between two consecutive time points; it indicates the degree of change of the indicator value in the current period relative to the previous period. Through this formula, a ratio can be obtained, indicating growth (positive value), decrease (negative value) or no change (zero value);

[0061] is a function that converts the rate of change Mapping to the coefficient of difference ; This coefficient can be used to compare the impact size between different indicators and help identify which factors contribute most to changes in the supply chain;

[0062] Rate of change Provides a simple and direct indicator for measuring changes in a dimension; the coefficient of variation This change is further quantified as an impact on the overall assessment, allowing companies to more accurately identify and manage key change factors in the supply chain.

[0063] Step S05: The difference coefficient of each index Multiply by their relative importance weights Get the overall evaluation score , the calculation formula is: ;in, is the number of indicators;

[0064] Formula explanation:

[0065] The difference coefficient of each indicator Indicates the degree of influence of the change of this indicator on the overall differentiation of the supply chain; it is calculated in step S04;

[0066] Weight Reflects the relative importance of each indicator in the entire evaluation; the weight is usually set based on the company's strategic goals, industry standards or expert opinions;

[0067] The sum of the weights is 1, Ensure the rationality of the evaluation scores;

[0068] Evaluation score It is a comprehensive score used to reflect the overall operating status of the supply chain in a given time period. By multiplying the difference coefficient of each indicator by its weight and summing them up, the evaluation score S can reflect the comprehensive impact of each indicator on the overall performance of the supply chain.

[0069] This formula allows companies to combine multiple dimensions of indicators into a single score, thereby simplifying complex supply chain analysis; evaluation score Can be used as a basis for management decision-making to help identify areas that need attention and improvement; by adjusting the weights Enterprises can flexibly adjust the focus of the evaluation according to different strategic priorities and changes in the market environment; the evaluation score It can also be used for comparative analysis under different time periods or different plans to help companies track improvement effects and optimize decisions.

[0070] Step S06: Based on the obtained overall evaluation score And the corresponding timeline is drawn to show the overall operation status of the supply chain.

[0071] The resulting overall supply chain score To analyze, Below the preset threshold When a supply chain disruption occurs, the risk warning mechanism is triggered and remedial measures are recommended. The specific steps are as follows; S13a: Real-time monitoring of the overall supply chain score calculated by the system. When the score is lower than the preset threshold, a risk warning is automatically triggered; S13b: Based on the risk type and severity, a remedial measures recommendation library is automatically generated, and the best remedial measures are intelligently selected or combined from the library to reduce the impact of supply chain disruptions; S13c: Track the implementation effect of remedial measures, dynamically evaluate the supply chain operation status, and adjust measures until the problem is resolved.

[0072] The actual change range of each indicator is statistically processed, a regression model is established, and the absolute value of the correlation coefficient is used Determine the importance of each indicator, including It is The correlation coefficient between each indicator and the overall evaluation score;

[0073] The instructions are as follows: First, collect the actual change data of each indicator; these data can be time series data, reflecting the changes of each indicator in different time periods; use these change data to build a regression model and convert the overall evaluation score as the dependent variable, and the change range of each indicator as the independent variable; the regression model is used to analyze the influence of the change of each indicator on the overall evaluation score;

[0074] Correlation coefficient Indicates Indicators and overall evaluation scores The degree of linear correlation between

[0075] Its value is between -1 and 1, 1 means perfect positive correlation, -1 means perfect negative correlation, and 0 means no correlation;

[0076] Absolute value The absolute value of the correlation coefficient is used to judge the importance of each indicator; the larger the absolute value, the greater the impact of the indicator on the overall evaluation score; by comparing size and identify the indicators that have the greatest impact on overall performance.

[0077] In step S02, the method for enhancing data preprocessing and standardization includes the following steps: S02a: detecting and filling missing values ​​in the collected data of each dimension of the supply chain, and filling missing data with appropriate interpolation methods or predictions based on other related variables; S02b: identifying and processing outliers, and using the IQR method to identify and eliminate or correct outliers to ensure data quality; S02c: implementing multi-stage standardization processing, adopting different standardization strategies for different types of data, and improving the accuracy of the standardization effect.

[0078] The difference coefficient function in step S04 It is further defined as a piecewise function, which divides the rate of change into different intervals according to the positive or negative and absolute value of the rate of change, and assigns a specific difference coefficient value to each interval to more accurately reflect the impact of the degree and direction of the indicator change on the overall difference of the supply chain;

[0079] Sign of the rate of change:

[0080] Positive rate of change: indicates an increase in the indicator;

[0081] Negative rate of change: indicates a decrease in the indicator;

[0082] The absolute value of the rate of change: According to the absolute value of the rate of change, it is divided into different intervals; for example, small change, medium change, large change, etc.

[0083] The rate of change Divided into multiple intervals, for example: (−∞,−a), [−a,0), [0,b), [b,∞); a and b are pre-defined thresholds to distinguish different degrees of change;

[0084] Assign a specific difference coefficient to each interval :

[0085] Large negative change: high negative variance coefficient;

[0086] Small positive change: low positive coefficient of variation;

[0087] Large positive change: high positive variance coefficient;

[0088] The piecewise function carefully captures the degree and direction of the rate of change, more accurately reflects its impact on the overall difference, and flexibly adjusts the interval division and coefficient value according to business needs and strategic goals to adapt to different analysis scenarios.

[0089] A synchronous system for collecting and processing order information, comprising: a data collection subunit, a data mining subunit and a data analysis subunit;

[0090] Data acquisition subunit:

[0091] Function: Collect data on indicators of various dimensions of the supply chain;

[0092] Operation process:

[0093] 1. Configure data source and interface parameters: set data source (such as database, API interface) and related parameters (such as access rights, data format);

[0094] 2. Perform data collection tasks: Capture supply chain data regularly or in real time to ensure the timeliness and integrity of the data;

[0095] 3. Preliminary verification and cleaning: Perform preliminary verification on the collected data to remove obvious errors and invalid data;

[0096] 4. Data storage: Store the processed data in a designated location for subsequent processing and analysis.

[0097] Data Mining Subunit:

[0098] Function: Preprocess and standardize the collected data;

[0099] Operation process:

[0100] 1. Read raw data: read the stored raw data from the data acquisition subunit;

[0101] 2. Data cleaning and correction: Apply preprocessing algorithms (such as missing value filling and outlier processing) to clean and correct data to ensure data quality;

[0102] 3. Data standardization: Use the standardization processing module to standardize the data to make it consistent and comparable;

[0103] 4. Data storage: Store the processed data for use by the data analysis subunit.

[0104] Data Analysis Subunit:

[0105] Function: Design a multi-dimensional evaluation index system, calculate the difference coefficient and overall evaluation score, and generate an evaluation report;

[0106] Operation process:

[0107] 1. Set up the evaluation indicator system: Use the evaluation indicator system design tool to set the indicators and their weights required for the evaluation;

[0108] 2. Read processed data: Read processed and standardized data from the data mining subunit;

[0109] 3. Calculate the difference coefficient and overall evaluation score: Calculate the difference coefficient and overall evaluation score of each indicator according to the set evaluation indicator system;

[0110] 4. Generate evaluation report: Generate an evaluation report containing result charts and recommendations to show the performance of each dimension of the supply chain;

[0111] 5. Collect user feedback and modify the report: Modify and improve the evaluation report based on user feedback to ensure its accuracy and practicality.

[0112] Through the collaborative work of data collection, mining and analysis sub-units, we can achieve comprehensive integration and processing of data in various dimensions of the supply chain, calculate multi-dimensional evaluation indicator systems and difference coefficients, provide accurate supply chain performance evaluation, support management's decision-making, and continuously optimize evaluation reports and analysis methods through user feedback mechanisms to improve the accuracy and practicality of the system, which can help companies fully understand all aspects of the supply chain and improve the efficiency and effectiveness of supply chain management.

[0113] A synchronization device for collecting and processing order information, wherein the synchronization device for collecting and processing order information stores a synchronization method program for collecting and processing order information, and when the synchronization method program for collecting and processing order information is executed by a processor, the steps of the synchronization method for collecting and processing order information are implemented.

[0114] When executed by a processor, the program can perform the functions of a synchronous method for collecting and processing order information, including:

[0115] Data collection: The program can collect supply chain-related data from different data sources, perform preliminary verification and cleaning, and store the data in a suitable location;

[0116] Data mining and preprocessing: The program can further clean, correct, and standardize the collected data to ensure data consistency and accuracy;

[0117] Data analysis: The program can design and apply a multi-dimensional evaluation index system, calculate the difference coefficient and overall evaluation score, and generate an evaluation report;

[0118] Feedback and Improvement: The program can collect user feedback, adjust and improve the analysis methods and report content to improve the accuracy and practicality of the analysis.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A synchronous method for collecting and processing order information, characterized in that: The following steps are involved: Step S01: Collect data on indicators of various dimensions of the supply chain, including but not limited to data sets on product attributes, quality supervision, capital flow monitoring, and supplier relationships; Step S02: preprocess and standardize the data collected in step S01, using the following formula for standardization: ;in, is the original data, is the mean of the data set, is the standard deviation of the data set, It is the standardized data; Step S03: A multi-dimensional evaluation index system is set up according to the supply chain characteristics and actual needs. The system includes at least four dimensions: product attributes, quality supervision, capital flow monitoring, and supplier relationships; Step S04: Given the historical data of each dimension indicator on the time section, calculate the change rate of each single indicator , and converted into the difference coefficient , the calculation formula is as follows: ;in, and are the indicator values ​​of the current period and the previous period respectively, is a function that maps the rate of change to the coefficient of difference; Step S05: The difference coefficient of each index Multiply by their relative importance weights Get the overall evaluation score , the calculation formula is: ;in, is the number of indicators; Step S06: Based on the obtained overall evaluation score And the corresponding timeline is drawn to show the overall operation status of the supply chain; The resulting overall supply chain score To analyze, Below the preset threshold When a risk occurs, the risk warning mechanism is triggered and remedial measures are recommended; The specific steps to trigger the risk early warning mechanism are as follows: S13a: The overall supply chain score calculated by the real-time monitoring system will automatically trigger a risk warning when the score is lower than the preset threshold; S13b: Automatically generate a library of remediation recommendations based on risk type and severity, and intelligently select or combine the best remediation measures from the library to reduce the impact of supply chain disruptions; S13c: Track the effectiveness of remedial measures, dynamically evaluate the supply chain operation status, and adjust measures until the problem is resolved; By statistically processing the actual change range of each indicator, a regression model is established, and the absolute value of the correlation coefficient is used Determine the importance of each indicator, including It is The correlation coefficient between each indicator and the overall evaluation score; In step S02, the method for enhancing data preprocessing and standardization includes the following steps: S02a: Detect and fill missing values ​​in the collected data of each dimension of the supply chain, and use appropriate interpolation methods or predictions based on other relevant variables to fill missing data; S02b: Identify and process outliers, use the IQR method to identify and remove or correct outliers to ensure data quality; S02c: Implement multi-stage standardization processing, adopt different standardization strategies for different types of data, and improve the accuracy of standardization effects; The difference coefficient function in step S04 It is further defined as a piecewise function, which divides the rate of change into different intervals according to the positive and negative sign and absolute value of the rate of change, and assigns each interval a specific difference coefficient value to more accurately reflect the impact of the degree and direction of indicator change on the overall difference of the supply chain.

2. A synchronous system for collecting and processing order information, which is applied to the synchronous method for collecting and processing order information as claimed in claim 1, characterized in that: include: The data collection subunit is used to collect data on indicators of various dimensions of the supply chain; The data mining subunit is used to preprocess and standardize the collected data; The data analysis subunit is used to design a multi-dimensional evaluation index system, calculate the coefficient of difference, calculate the overall evaluation score, and modify and improve the evaluation report based on feedback.

3. A synchronous system for collecting and processing order information according to claim 2, characterized in that: The operation process of the data acquisition subunit: configure the data source and interface parameters; execute the data acquisition task, and capture data regularly or in real time; Conduct preliminary verification and cleaning of the collected data; Store the processed data in the specified location; The operation flow of the data mining subunit is as follows: reading the original data stored in the data acquisition subunit; Apply preprocessing algorithms to clean and correct data; use standardization processing modules to standardize data; store processed data for use by the data analysis subunit; The operation process of the data analysis subunit is as follows: using the evaluation index system design tool to set the evaluation index system; reading the data processed by the data mining subunit; Calculate the difference coefficient and overall evaluation score of each indicator; generate an evaluation report and display the result charts and suggestions; Collect user feedback and modify and improve the evaluation report based on the feedback.

4. A synchronous device for collecting and processing order information, characterized in that: The synchronization device for collecting and processing order information stores a synchronization method program for collecting and processing order information. When the synchronization method program for collecting and processing order information is executed by the processor, the steps of the synchronization method for collecting and processing order information as described in claim 1 are implemented.

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