Data processing method and device for medical instrument digital supply chain consignment and sale mode

Abstract

The embodiment of the invention provides a data processing method and device for a medical device digital supply chain consignment and sale mode, and the method comprises the steps: creating a consignment and sale order for a medical device, and building a multi-stage traceability structure comprising first-stage product source information, second-stage inventory state information and third-stage flow information; the method comprises the following steps: establishing a mapping relation table of cargo flow data and account period data, retrieving updated three-level flow direction information based on a tracing identification code, inputting time sequence consumption data into a long short-term memory network, dynamically adjusting a time attenuation factor by adopting a self-adaptive mechanism, determining a fluctuation reference value by calculating a standard deviation of recent consumption data, and determining a time sequence consumption value according to the fluctuation reference value. Weighting the historical data based on the updated time attenuation factor, predicting an optimal account period and generating a settlement bill, and synchronously updating the cargo flow data and the account period data through a mapping relation table; the method can achieve the whole-course tracing of the product, has the intelligent account period management capability, and can carry out the accurate consumption prediction based on historical data.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and specifically to a data processing method and device for a medical device digital supply chain consignment model. Background Art

[0002] Medical device consignment management is an important part of the medical equipment supply chain, but the current data processing solutions still have many shortcomings. Traditional consignment management systems often use simple inventory records and regular inventory checks, lacking effective tracking of the entire product life cycle, resulting in inefficient inventory management and slow capital turnover.

[0003] The existing system has obvious shortcomings in data traceability and flow management. Most systems only focus on single-dimensional data records and cannot establish effective links between product sources, inventory status, and flow information, which makes it difficult to quickly locate the source of the problem when an abnormal situation occurs. At the same time, the existing account management method is too rigid, usually using a fixed settlement cycle, and failing to dynamically adjust according to actual consumption, resulting in inefficient capital utilization.

[0004] Another prominent problem is the lack of data forecasting capabilities. Most current systems rely on simple statistical methods to predict consumption trends, and fail to fully utilize the time series characteristics contained in historical data, resulting in low forecast accuracy. Especially when dealing with seasonal fluctuations or sudden changes in demand, the forecast results often deviate greatly from the actual situation. Summary of the invention

[0005] In response to the problems in the prior art, the present application provides a data processing method and device for a medical device digital supply chain consignment model, which can achieve full product traceability, has intelligent account period management capabilities, and can make accurate consumption forecasts based on historical data.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a data processing method for a medical device digital supply chain consignment model, comprising:

[0008] Create a consignment order for medical devices, establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generate a unique traceability identification code, split the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establish a first association index between the product source identification segment and the inventory status identification segment, establish a second association index between the inventory status identification segment and the flow identification segment, and set a data integrity check;

[0009] Retrieving the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, registering the consumption of the consignment inventory of the medical device, generating a consumption record including the consumption time and the consumption quantity, and updating the consumption record to the tertiary flow direction information through the second association index;

[0010] A mapping relationship table between cargo flow data and account period data is established, and the updated three-level flow direction information is retrieved based on the traceability identification code. The time series consumption data is input into the long short-term memory network, and the time decay factor is dynamically adjusted by an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the decay rate parameter is updated in real time according to the fluctuation reference value. The historical data is weighted based on the updated time decay factor, the optimal account period is predicted and a settlement statement is generated, and the cargo flow data and account period data are synchronously updated through the mapping relationship table.

[0011] Furthermore, the creation of a consignment order for medical devices, establishing a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, and generating a unique traceability identification code includes:

[0012] Generate a 36-bit unique identification code based on the basic information of the medical device, the unique identification code including the device number, production batch number and timestamp information, and create a consignment order based on the unique identification code;

[0013] Medical devices are classified according to suppliers, production dates and models to establish a product source information database; inventory status information database is established according to inventory locations, storage conditions and quantities; flow information database is established according to use departments, consumption records and flow destinations; the product source information database, inventory status information database and flow information database are combined to form a multi-level traceability structure.

[0014] Further, the tracing identification code is split into a product source identification segment, an inventory status identification segment and a flow direction identification segment, a first association index is established between the product source identification segment and the inventory status identification segment, a second association index is established between the inventory status identification segment and the flow direction identification segment, and a data integrity check is set, including:

[0015] The 36-digit traceability identification code is split into a fixed-length format of 12-12-12 digits. The first 12 digits are used as the product source identification segment to store supplier and production information, the middle 12 digits are used as the inventory status identification segment to store inventory location and status information, and the last 12 digits are used as the flow identification segment to store the use department and consumption information.

[0016] A first associated index value is generated by performing a bit-by-bit XOR operation on the product source identification segment and the inventory status identification segment, a second associated index value is generated by performing a bit-by-bit XOR operation on the inventory status identification segment and the flow direction identification segment, and a cyclic redundancy check algorithm is used to perform data verification on the first associated index value and the second associated index value.

[0017] Further, the retrieving the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, registering the consumption of the consignment inventory of the medical device, generating a consumption record including the consumption time and the consumption quantity, and updating the consumption record to the tertiary flow direction information through the second association index, includes:

[0018] Obtaining an inventory status identification segment through the first associated index value, comparing and matching the inventory status identification segment with a corresponding record in an inventory status information library, reading inventory status information of the medical device, and recording a usage time point and a specific usage quantity of the medical device in the inventory status information;

[0019] The usage record in the inventory status information of the medical device is associated with the second associated index value, the corresponding position in the three-level flow information library is located based on the mapping relationship, and the usage time point and usage quantity are written into the consumption record field of the flow information library to complete data synchronization.

[0020] Furthermore, the time series consumption data is input into the long short-term memory network, and the time decay factor is dynamically adjusted using an adaptive mechanism, including:

[0021] Constructing a time series data structure based on a 64-bit floating point format, writing the consumption data of medical devices into the data structure according to the time dimension, normalizing the data through an input gate of a long short-term memory network, and setting a recurrent neuron structure including three hidden layers in the long short-term memory network;

[0022] The consumption data difference between adjacent time nodes is calculated to obtain the time interval matrix, and the time interval matrix is ​​input into the exponential function to generate the initial time decay factor. The cardinality parameter of the exponential function is adjusted in real time according to the prediction error of the long short-term memory network, so that the time decay factor decreases exponentially with the increase of the time interval.

[0023] Furthermore, the method of determining the fluctuation reference value by calculating the standard deviation of recent consumption data and updating the decay rate parameter in real time according to the fluctuation reference value includes:

[0024] The medical device consumption data for nearly 30 days is used to form a statistical sample, and based on the statistical sample, the mean of the consumption and the sum of squares of deviations of each sampling point are calculated, and the square root of the sum of squares of deviations is divided by the number of sampling points to obtain a fluctuation standard deviation, and the fluctuation standard deviation is compared with a preset threshold to obtain a fluctuation reference value;

[0025] The fluctuation reference value is substituted into the sigmoid function to calculate the initial value of the attenuation rate, the slope coefficient of the sigmoid function is adjusted based on the changing trend of the fluctuation reference value, and the updated attenuation rate parameter is output through the dynamically adjusted sigmoid function.

[0026] Furthermore, the historical data is weighted based on the updated time decay factor, the optimal account period is predicted and a settlement statement is generated, and the cargo flow data and the account period data are synchronously updated through the mapping relationship table, including:

[0027] Performing a point multiplication operation on the historical consumption data of medical devices and the updated time decay factor to obtain a weighted sequence, inputting the weighted sequence into the output layer of the long short-term memory network, calculating the optimal account period interval according to the output result, and generating settlement data including consumption summary, amount calculation and account period time limit based on the optimal account period interval;

[0028] Construct a two-dimensional mapping table containing goods flow records and account period data, establish a key-value pair index for the two-dimensional mapping table according to the goods number, locate the target record in the two-dimensional mapping table through the key-value pair index, write the settlement data into the account period field of the target record and update the corresponding goods flow status.

[0029] In a second aspect, the present application provides a data processing device for a medical device digital supply chain consignment model, comprising:

[0030] A multi-level traceability module is used to create a consignment order for medical devices, establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generate a unique traceability identification code, split the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establish a first association index between the product source identification segment and the inventory status identification segment, establish a second association index between the inventory status identification segment and the flow identification segment, and set a data integrity check;

[0031] a consumption registration module, configured to retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index;

[0032] The account period prediction module is used to establish a mapping relationship table between goods flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the goods flow data and account period data through the mapping relationship table.

[0033] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the data processing method for the medical device digital supply chain consignment model are implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the data processing method for the medical device digital supply chain consignment model.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the data processing method of the medical device digital supply chain consignment model.

[0036] It can be seen from the above technical solution that the present application provides a data processing method and device for a medical device digital supply chain consignment model, which creates a consignment order for medical devices and establishes a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information; establishes a mapping relationship table between goods flow data and account period data, retrieves the updated third-level flow information based on the traceability identification code, inputs the time series consumption data into the long short-term memory network, and dynamically adjusts the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the historical data is weighted based on the updated time decay factor. The optimal account period is predicted and a settlement statement is generated. The goods flow data and account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is one of the flow charts of the data processing method of the medical device digital supply chain consignment model in the embodiment of the present application;

[0039] Figure 2 This is a second flow chart of a data processing method for a medical device digital supply chain consignment model in an embodiment of the present application;

[0040] Figure 3 The third flowchart of the data processing method of the medical device digital supply chain consignment model in the embodiment of the present application;

[0041] Figure 4 This is a fourth flow chart of a data processing method for a medical device digital supply chain consignment model in an embodiment of the present application;

[0042] Figure 5 This is a fifth flow chart of a data processing method for a medical device digital supply chain consignment model in an embodiment of the present application;

[0043] Figure 6 This is a sixth flow chart of the data processing method of the medical device digital supply chain consignment model in the embodiment of the present application;

[0044] Figure 7 FIG7 is a flowchart of a data processing method for a medical device digital supply chain consignment model in an embodiment of the present application;

[0045] Figure 8 A structural diagram of a data processing device for a medical device digital supply chain consignment model in an embodiment of the present application;

[0046] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0047] Reference numerals:

[0048] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0051] In view of the problems existing in the prior art, the present application provides a data processing method and device for a consignment model of a digital supply chain of medical devices. By creating a consignment order for medical devices, a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information is established; a mapping relationship table between cargo flow data and account period data is established, and the updated tertiary flow information is retrieved based on the traceability identification code, and the time series consumption data is input into a long short-term memory network. An adaptive mechanism is used to dynamically adjust the time decay factor, and the fluctuation reference value is determined by calculating the standard deviation of recent consumption data. The historical data is weighted based on the updated time decay factor, and the optimal account period is predicted and a settlement statement is generated. The cargo flow data and the account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data.

[0052] In order to achieve full product traceability, intelligent account management capabilities, and accurate consumption forecasting based on historical data, this application provides an embodiment of a data processing method for a medical device digital supply chain consignment model, see Figure 1 The data processing method of the medical device digital supply chain consignment model specifically includes the following contents:

[0053] Step S101: creating a consignment order for medical devices, establishing a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generating a unique traceability identification code, splitting the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establishing a first association index between the product source identification segment and the inventory status identification segment, establishing a second association index between the inventory status identification segment and the flow identification segment, and setting a data integrity check;

[0054] Optionally, in this embodiment, this step implements the data traceability structure establishment and identification coding process in the consignment management of medical devices, and ensures the integrity and traceability of the data through hierarchical design and associated indexing mechanism. First, the system creates a 36-digit unique identification code based on the basic information of the medical device, such as the device model, specification parameters, production batch, etc., which includes a 12-digit device number, a 12-digit batch information and a 12-digit timestamp. This fixed-length encoding method ensures the uniqueness and standardization of the identification.

[0055] In terms of data structure design, a three-level hierarchical architecture is used to store relevant information. The first-level product source information includes basic data such as supplier qualifications, production date, model specifications, etc.; the second-level inventory status information records dynamic data such as inventory location, storage conditions, quantity changes, etc.; the third-level flow information tracks terminal data such as the use department, consumption records, and destination. This hierarchical information organization method makes data query and management more efficient, and also facilitates the realization of data association between different levels.

[0056] In terms of identification code processing, the system splits the 36-digit traceability identification code into a fixed format of 12-12-12 digits. The first 12-digit product source identification segment is used to store supplier and production information, with the first 4 digits representing the supplier code, the middle 4 digits representing the product type, and the last 4 digits representing the production batch. The middle 12-digit inventory status identification segment is used to store inventory location and status information, including a 4-digit inventory location code, a 4-digit storage condition code, and a 4-digit status identifier. The last 12-digit flow direction identification segment is used to store usage information, including a 4-digit department code, a 4-digit usage type, and a 4-digit flow direction identifier.

[0057] In order to establish an efficient data indexing mechanism, the system performs bit operations on the identification segments of adjacent levels. Specifically, an XOR operation is performed on the product source identification segment and the inventory status identification segment to generate a first associated index value, and an XOR operation is performed on the inventory status identification segment and the flow direction identification segment to generate a second associated index value. This bit operation-based indexing mechanism is not only computationally efficient, but also can effectively detect data integrity.

[0058] In terms of data integrity verification, the system uses a cyclic redundancy check (CRC) algorithm to verify the associated index value. The CRC check generates a check value through polynomial division operations, which can effectively detect errors in data transmission and storage. When data anomalies are detected, the system will trigger a verification failure warning and record an abnormality log to ensure the accuracy and reliability of the data.

[0059] This technical solution effectively solves the data traceability problem in medical device consignment management through multi-level data structure design and associated index mechanism. The system realizes standardized storage and efficient indexing of data, significantly improving data query efficiency. At the same time, the integrity verification mechanism ensures the reliability of data, laying a solid foundation for subsequent inventory management and flow tracking. In addition, the solution has strong scalability and can adapt to the management needs of different types of medical devices, providing reliable technical support for medical device consignment management.

[0060] Step S102: Retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index;

[0061] Optionally, in this embodiment, this step realizes the consumption management and data update process of the medical device consignment inventory, and realizes the real-time synchronization of inventory status information and flow information through the association index mechanism. The system first uses the first association index value as the search key, and restores the inventory status identification segment through reverse analysis of bit operations. This process uses the reversible characteristics of XOR operations to ensure the accuracy of data restoration.

[0062] After obtaining the inventory status identification segment, the system matches it with the records in the inventory status information database. The matching process uses a binary search algorithm to quickly locate the corresponding inventory status information record by comparing the inventory location code, storage condition code and status identification. This efficient retrieval mechanism greatly reduces the time overhead of data query.

[0063] In the consumption registration phase, the system records the use of medical devices in real time. Specifically, when a medical device is used, the system automatically captures the usage timestamp and records the specific consumption quantity. This information is organized into structured data with time and quantity dimensions. At the same time, the system checks whether the consumption quantity exceeds the inventory threshold. If it is close to the threshold, it will automatically trigger the replenishment warning mechanism.

[0064] In order to ensure synchronous data update, the system uses the second association index to establish a mapping relationship between inventory status information and flow direction information. This mapping relationship is implemented using a hash table structure, and the index value obtained by XORing the inventory status identification segment and the flow direction identification segment is used as the hash key to achieve fast data positioning and updating.

[0065] When a consumption record is generated, the system automatically locates the corresponding record in the third-level flow information library through the second associated index. The update process uses a transaction mechanism to ensure the atomicity and consistency of the data. Specifically, the system first packages the new consumption record into an update transaction, and then uses a two-phase commit protocol to ensure reliable data writing. If an exception occurs during the update process, the system will automatically roll back to the state before the update to ensure data integrity.

[0066] This technical solution effectively solves the data consistency problem in the consignment process of medical devices through the association index and real-time synchronization mechanism. The solution realizes the real-time tracking and automatic synchronization of inventory consumption data, significantly improving the timeliness and accuracy of the data. At the same time, the transaction mechanism and exception handling ensure the reliability of data updates, providing strong support for the refined management of medical devices. In addition, the solution has a high degree of automation, reduces manual operation links, reduces the risk of data entry errors, and lays the foundation for the standardized operation of medical device consignment management.

[0067] Through the comprehensive application of the above technical means, this step not only realizes the accurate inventory management of medical equipment during the consignment process, but also provides a reliable data basis for subsequent data analysis and decision support. The scalability and fault tolerance of the system further enhance the practical value of the solution and can adapt to the needs of medical institutions of different sizes and types.

[0068] Step S103: Establish a mapping relationship table between cargo flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the cargo flow data and account period data through the mapping relationship table.

[0069] Optionally, in this embodiment, this step implements the intelligent billing period prediction and data synchronization update mechanism in the consignment management of medical devices. First, the system establishes a bidirectional mapping relational table structure for storing the correspondence between the goods flow data and the billing period data. The mapping table is in the form of a key-value pair, where the key is the traceability identification code and the value is a composite data structure containing the flow status and billing period information.

[0070] The system uses the traceability identification code as the index key to retrieve the latest consumption records from the three-level flow information library. The retrieval process adopts a multi-level cache mechanism to cache high-frequency access data in memory, significantly improving data reading efficiency. The acquired consumption records are organized into time series data in chronological order, including key features such as consumption time and quantity.

[0071] In the data processing stage, the system uses the long short-term memory network (LSTM) for time series analysis. The LSTM network contains three control units: input gate, forget gate, and output gate, which can effectively capture the long-term dependencies of data. The input layer receives the normalized time series consumption data, extracts the time series features through multi-layer nonlinear transformation, and the output layer generates the prediction results.

[0072] In order to improve the adaptability of the model, the system introduces an adaptive time decay mechanism. First, the sliding window standard deviation of recent consumption data is calculated as a quantitative indicator of data volatility. The larger the standard deviation, the more drastic the data fluctuations. The system will increase the time decay factor accordingly to reduce the weight of historical data. The decay rate parameter is associated with the fluctuation baseline value through an exponential function to achieve dynamic adjustment of the decay intensity.

[0073] During the billing period forecasting process, the system time-weights historical data. The weighting coefficient is determined by the updated time decay factor, and the more recent data is given a higher weight. The weighted data is input into the LSTM network for training and forecasting to obtain the optimal billing period estimate. Based on the forecast results, the system automatically generates a settlement slip containing information such as billing period and amount.

[0074] Finally, the system synchronizes data through a mapping table. When the flow status of goods changes, the system automatically updates the corresponding account period record; conversely, when the account period data is updated, the relevant flow status will also be adjusted synchronously. This two-way synchronization mechanism ensures data consistency.

[0075] This technical solution effectively solves the problem of billing period prediction in the consignment process of medical devices through an adaptive time decay mechanism and an intelligent prediction model. The system realizes intelligent analysis and automatic synchronization of data, significantly improving the accuracy and efficiency of billing period management. At the same time, the adaptive mechanism enhances the model's ability to respond to market changes and provides reliable support for the company's capital management decisions. In addition, the solution has a high degree of automation, reduces manual intervention, improves management efficiency, and provides technical support for the standardized operation of medical device consignment business.

[0076] From the above description, it can be seen that the data processing method of the medical device digital supply chain consignment model provided in the embodiment of the present application can establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information by creating consignment orders for medical devices; establish a mapping relationship table between goods flow data and account period data, retrieve the updated third-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, and dynamically adjust the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the historical data is weighted based on the updated time decay factor. The optimal account period is predicted and a settlement statement is generated. The goods flow data and account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data.

[0077] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 2 , and can also include the following:

[0078] Step S201: Generate a 36-bit unique identification code based on the basic information of the medical device, the unique identification code including the device number, production batch number and timestamp information, and create a consignment order based on the unique identification code;

[0079] Step S202: Classify the medical devices according to supplier, production date and model to establish a product source information database, classify them according to inventory location, storage conditions and quantity to establish an inventory status information database, and classify them according to use department, consumption record and flow destination to establish a flow information database. The product source information database, inventory status information database and flow information database are combined to form a multi-level traceability structure.

[0080] Optionally, in this embodiment, these two steps together realize the creation of medical device consignment orders and the construction of data structures. First, the system generates a standardized unique identification code based on the basic information of the medical device. The identification code is designed with a fixed length of 36 bits, of which the equipment number occupies 12 bits to identify the specific medical device model and specifications; the production batch number occupies 12 bits, including the production date and batch information; the timestamp information occupies 12 bits to record the time when the device enters the warehouse, using a time coding format accurate to seconds. This coding scheme ensures that each medical device has a unique identity.

[0081] The identification code generation process adopts a segmented combination method. The system first converts the equipment number into a 12-digit string, where the first 4 digits represent the equipment category, the middle 4 digits represent the subcategory, and the last 4 digits represent the specific model. Of the 12 digits of the production batch number, the first 6 digits represent the production date and the last 6 digits represent the batch number. The timestamp information is converted into a 12-digit number using the Unix timestamp. The three segments of information are directly spliced ​​to form a complete 36-digit identification code. This encoding method not only ensures uniqueness, but also facilitates subsequent information extraction and parsing.

[0082] In terms of database structure design, the system adopts a three-level hierarchical architecture. The first-level product source information database uses a tree structure to organize data, with supplier information as the root node, production date as the second-level node, and model information as the leaf node. Each node contains detailed attribute information, such as supplier qualifications, production licenses, quality certifications, etc. This hierarchical data organization method facilitates rapid retrieval and updating of information.

[0083] The secondary inventory status information database adopts a matrix structure, with inventory locations as rows, storage conditions as columns, and quantity information as matrix elements. Each inventory location is equipped with an environmental monitoring module to record storage parameters such as temperature and humidity in real time. The system realizes rapid inventory statistics and deployment through matrix operations, improving inventory management efficiency.

[0084] The three-level flow information database uses a time series database storage structure to record the use trajectory of medical devices according to the time dimension. The data contains detailed information such as the use department, operator, and use time. The system uses time index technology to support efficient historical data query and statistical analysis.

[0085] The three information bases form a complete multi-level traceability structure through associated fields. The system uses the primary key-foreign key mechanism to establish inter-base associations, and uses the unique identification code as the association key to achieve vertical association and horizontal expansion of data. This multi-dimensional data structure design provides a solid foundation for subsequent data analysis and decision support.

[0086] This technical solution effectively solves the data organization and traceability problems in medical device consignment management through a standardized coding system and multi-level data structure design. The system realizes standardized storage and efficient retrieval of data, providing reliable technical support for the full life cycle management of medical devices. At the same time, the hierarchical data structure design improves the scalability and maintainability of the system, and can adapt to the management needs of medical institutions of different sizes.

[0087] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 3 , and can also include the following:

[0088] Step S301: Split the 36-digit traceability identification code into a fixed-length format of 12-12-12 digits, with the first 12 digits used as the product source identification segment to store supplier and production information, the middle 12 digits used as the inventory status identification segment to store inventory location and status information, and the last 12 digits used as the flow identification segment to store the use department and consumption information;

[0089] Step S302: performing a bit-by-bit XOR operation on the product source identification segment and the inventory status identification segment to generate a first associated index value, performing a bit-by-bit XOR operation on the inventory status identification segment and the flow direction identification segment to generate a second associated index value, and using a cyclic redundancy check algorithm to perform data verification on the first associated index value and the second associated index value.

[0090] Optionally, in this embodiment, these two steps implement the segmentation processing and associated index establishment process of the medical device traceability identification code. The system first performs standardized splitting of the 36-bit traceability identification code, using a three-segment structure of fixed length. The product source identification segment occupies the first 12 bits, using a 4-4-4 internal division method, with the first 4 bits storing the supplier code, the middle 4 bits storing the product type code, and the last 4 bits storing the production batch number. The inventory status identification segment occupies the middle 12 bits, also using a 4-4-4 structure, storing the inventory location code, storage condition code and status identification in sequence. The flow direction identification segment occupies the last 12 bits, divided into 4-4-4 bits, and stores the use department code, use type code and flow direction identification respectively.

[0091] The identification code splitting process is implemented by bit operation. The system extracts each segment of information through shift and mask operations to ensure the accuracy and efficiency of data extraction. Each identification segment adopts an independent encoding rule to ensure the uniqueness and traceability of the data. This fixed-length segmented storage method not only facilitates data parsing and processing, but also improves storage efficiency.

[0092] In the process of establishing the associated index, the system uses XOR operation to generate the index value. For the first associated index, the system performs bit-by-bit XOR operation on the 12-bit binary data of the product source identification segment and the 12-bit data of the inventory status identification segment. The XOR operation is reversible and commutative, which ensures the uniqueness and traceability of the index value. Similarly, the second associated index is generated by the XOR operation of the inventory status identification segment and the flow direction identification segment. This indexing mechanism establishes a logical association between adjacent data segments, which facilitates the rapid location and associated query of data.

[0093] To ensure the integrity and reliability of the data, the system uses a cyclic redundancy check (CRC) algorithm to check the associated index value. The CRC check process first treats the index value as a binary polynomial coefficient, selects a specific generating polynomial for modulo-2 division, and obtains the check value. The system appends the check value to the original data to form a complete check data packet. This check mechanism can effectively detect bit errors during data transmission and storage.

[0094] When data anomalies are detected, the system will automatically trigger a verification failure warning and record an abnormality log. At the same time, the system will try to reconstruct the original data through the reversible characteristics of the XOR operation to achieve automatic data repair. If the repair fails, an alarm message will be sent to the administrator, requiring manual intervention.

[0095] This technical solution effectively solves the data association and integrity problems in medical device consignment management through standardized data segmentation and association indexing mechanisms. The system achieves efficient data storage and fast retrieval, significantly improving data processing efficiency. At the same time, the CRC check mechanism ensures the reliability of data transmission and storage, providing technical support for the refined management of medical devices. In addition, the solution has strong fault tolerance and can detect and handle data anomalies in a timely manner, ensuring the stable operation of the system.

[0096] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 4 , and can also include the following:

[0097] Step S401: obtaining an inventory status identification segment through a first associated index value, comparing and matching the inventory status identification segment with a corresponding record in an inventory status information library, reading inventory status information of the medical device, and recording a usage time point and a specific usage quantity of the medical device in the inventory status information;

[0098] Step S402: associate and map the usage record in the inventory status information of the medical device with the second associated index value, locate the corresponding position in the three-level flow information library based on the mapping relationship, write the usage time point and usage quantity into the consumption record field of the flow information library to complete data synchronization.

[0099] Optionally, in this embodiment, these two steps implement the inventory status update and data synchronization mechanism during the use of medical devices. First, the system uses the first associated index value as the search key to restore the inventory status identification segment through reverse parsing. This process uses bit operations to restore the original inventory status identification information through the inverse operation of the XOR operation of the 12-bit index value. The system uses a cache mechanism to optimize retrieval efficiency, store frequently accessed index values ​​in memory, and reduce the number of database accesses.

[0100] After obtaining the inventory status identification segment, the system performs a comparison and matching operation. The matching process uses multi-level indexing technology, first quickly locating the data interval through the inventory location code, then using the storage condition code for secondary screening, and finally accurately locating the target record through the status identification. This multi-level indexing strategy significantly improves data retrieval efficiency. After a successful match, the system immediately reads the complete inventory status information of the medical device.

[0101] The update of usage records adopts an atomic operation mechanism. The system first obtains a timestamp accurate to milliseconds as the usage time point, and records the specific usage quantity. This information is organized into structured data, including fields such as operation time, operator, and usage quantity. The system uses write-time copy technology to ensure the atomicity and consistency of the data update process.

[0102] In the data synchronization phase, the system first establishes a mapping relationship between the usage record and the second associated index value. This mapping is implemented using a hash table structure, using the index value as the hash key and the related usage record as the value. The design of the hash table takes into account the load factor and conflict handling, ensuring the efficiency of the mapping operation.

[0103] Based on the established mapping relationship, the system can quickly locate the target record in the three-level flow information library. The positioning process uses the B+ tree index structure to support efficient range query and precise search. The system writes information such as the time point and the amount of use into the corresponding fields of the flow information library to complete data synchronization. The synchronization process uses a transaction mechanism to ensure data consistency and integrity.

[0104] To prevent data loss, the system implements a multi-level backup mechanism. Each data update operation generates an operation log, recording the detailed update content and timestamp. If a system abnormality occurs, the data can be quickly restored to the correct state through log records. At the same time, the system also implements an incremental backup strategy to regularly synchronize updated data to the backup server.

[0105] This technical solution effectively solves the problem of data consistency during the use of medical devices through an efficient indexing mechanism and a reliable synchronization strategy. The system realizes real-time synchronization of inventory status and flow information, significantly improving the timeliness and accuracy of data. At the same time, multiple security mechanisms ensure the reliability of data and provide strong support for the use and management of medical devices. In addition, the solution has a high degree of automation, reduces manual intervention, improves management efficiency, and provides technical support for the standardized operation of medical device consignment business.

[0106] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 5 , and can also include the following:

[0107] Step S501: constructing a time series data structure based on a 64-bit floating point format, writing the consumption data of medical devices into the data structure according to the time dimension, normalizing the data through the input gate of the long short-term memory network, and setting a recurrent neuron structure including 3 hidden layers in the long short-term memory network;

[0108] Step S502: Calculate the difference in consumption data between adjacent time nodes to obtain a time interval matrix, input the time interval matrix into an exponential function to generate an initial time decay factor, and adjust the cardinality parameter of the exponential function in real time according to the prediction error of the long short-term memory network, so that the time decay factor decreases exponentially with the increase of the time interval.

[0109] Optionally, in this embodiment, these two steps implement the time series processing and dynamic attenuation mechanism of medical device consumption data. The system first constructs a time series data structure using the 64-bit floating point format of the IEEE 754 standard, where 1 bit is used for the sign bit, 11 bits are used for the exponent bit, and 52 bits are used for the mantissa bit. This format provides sufficient precision and range to accurately represent the numerical characteristics and time information of the consumption data. The system writes the consumption data into the data structure in timestamp order to form an ordered time series.

[0110] In the data preprocessing phase, the system performs normalization through the input gate of the LSTM network. The normalization process uses the min-max standardization method to map the data to the [0,1] interval. This processing method not only retains the relative size relationship of the data, but also effectively eliminates the dimension effect and improves the training effect of the model. The input gate controls the inflow of information through the sigmoid activation function, realizing adaptive screening of data.

[0111] The core structure of the LSTM network adopts a three-layer hidden layer design. The first hidden layer is responsible for feature extraction and contains 128 neurons; the second hidden layer performs feature fusion and uses 64 neurons; the third hidden layer completes feature mapping and is configured with 32 neurons. Each layer of neurons adopts a cyclic connection structure to maintain long-term memory of historical information. The network uses the back-propagation algorithm to update parameters and uses the Adam optimizer to adjust weights.

[0112] In the process of implementing the time decay mechanism, the system first calculates the difference of consumption data of adjacent time nodes. The difference calculation adopts the sliding window method, and the window size is dynamically adjusted according to the data characteristics. The time interval matrix obtained by the difference operation reflects the time span relationship between data points. This matrix structure facilitates subsequent batch calculation and parallel processing.

[0113] The system inputs the time interval matrix into the exponential function to generate the initial time decay factor. The exponential function is based on the natural logarithm, and the initial value of the cardinality parameter is determined by historical data statistics. The calculation of the decay factor takes into account the nonlinear effect of the time span, and the weight of the long-term data decays exponentially.

[0114] In order to improve the adaptability of the model, the system monitors the prediction error of the LSTM network in real time. When the prediction error exceeds the preset threshold, the adjustment mechanism of the exponential function cardinality parameter is triggered. The adjustment process uses the gradient descent method to optimize the cardinality parameter by minimizing the prediction error. This adaptive mechanism ensures that the attenuation factor can accurately reflect the timeliness of the data.

[0115] This technical solution effectively solves the timeliness problem in medical device consumption forecasting through precise data structure design and intelligent decay mechanism. The system realizes high-precision data storage and intelligent processing, significantly improving the accuracy of forecasting. At the same time, the adaptive decay mechanism enhances the model's responsiveness to market changes and provides reliable support for the company's inventory management decisions. In addition, the solution has high computational efficiency and can meet the needs of real-time forecasting, providing technical support for the intelligent management of medical device consignment business.

[0116] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 6 , and can also include the following:

[0117] Step S601: The medical device consumption data for nearly 30 days is used to form a statistical sample, and based on the statistical sample, the mean of the consumption and the sum of squares of deviations of each sampling point are calculated, and the sum of squares of deviations is divided by the number of sampling points and then squared to obtain a fluctuation standard deviation, and the fluctuation standard deviation is compared with a preset threshold to obtain a fluctuation reference value;

[0118] Step S602: Substitute the fluctuation reference value into the sigmoid function to calculate the initial value of the attenuation rate, adjust the slope coefficient of the sigmoid function based on the change trend of the fluctuation reference value, and output the updated attenuation rate parameter through the dynamically adjusted sigmoid function.

[0119] Optionally, in this embodiment, these two steps implement the statistical analysis of medical device consumption data and the dynamic attenuation rate adjustment mechanism. First, the system collects the consumption data of the last 30 days as statistical samples. The sampling process adopts the method of equal time intervals, taking data at fixed time points every day to ensure the uniformity of the time distribution of the samples. The system preprocesses these sample data, including outlier detection and missing value processing, to improve the data quality.

[0120] In the statistical calculation phase, the system first calculates the arithmetic mean of the consumption. The calculation process uses a sliding window method to achieve dynamic update of the mean. Then, the system calculates the difference between each sampling point and the mean, and squares these differences to obtain a deviation square sequence. The system divides the sum of the deviation square sequence by the number of sampling points and performs a square root operation to obtain the standard deviation of fluctuation. This standard deviation reflects the degree of dispersion of the consumption data.

[0121] The system compares the calculated standard deviation of fluctuations with the preset threshold. The preset threshold is determined based on historical data analysis and reflects the normal fluctuation range. Through the comparison operation, the system generates a fluctuation benchmark value. This benchmark value comprehensively considers the relationship between actual fluctuations and expected fluctuations, and provides a basis for subsequent attenuation rate adjustments.

[0122] In the process of calculating the decay rate, the system uses the sigmoid function as the basic conversion function. The sigmoid function has good nonlinear characteristics and can map the input to the (0,1) interval, which is suitable as the basis for calculating the decay rate. The system uses the fluctuation reference value as the input of the sigmoid function and obtains the initial value of the decay rate through function calculation.

[0123] In order to improve the adaptability of the system, the system monitors the changing trend of the fluctuation baseline value in real time. When a trend change is detected, the system automatically adjusts the slope coefficient of the sigmoid function. The adjustment process uses the gradient descent method to optimize the slope coefficient by minimizing the prediction error. This adaptive mechanism enables the decay rate to better reflect the dynamic characteristics of the data.

[0124] Through the adjusted sigmoid function, the system calculates the updated decay rate parameters in real time. The update process uses an atomic operation mechanism to ensure the consistency of parameter updates. The system also implements parameter smoothing to avoid adverse effects of sudden changes in the decay rate on predictions.

[0125] This technical solution effectively solves the dynamic adaptation problem in the consumption forecast of medical devices through precise statistical analysis and flexible rate adjustment mechanism. The system realizes accurate quantification of fluctuation characteristics and intelligent adjustment of attenuation rate, which significantly improves the adaptability of the prediction model. At the same time, the adaptive parameter adjustment mechanism enhances the model's responsiveness to changes in consumption patterns and provides reliable decision-making support for the inventory management of medical devices. In addition, the solution has high computational efficiency and can meet the needs of real-time forecasting, providing technical support for the intelligent management of medical device consignment business. The implementation of the solution reduces inventory management costs, improves supply chain efficiency, and has significant economic benefits.

[0126] In one embodiment of the data processing method of the medical device digital supply chain consignment model of the present application, see Figure 7, and can also include the following:

[0127] Step S701: performing a dot multiplication operation on the historical consumption data of the medical device and the updated time decay factor to obtain a weighted sequence, inputting the weighted sequence into the output layer of the long short-term memory network, calculating the optimal billing period interval according to the output result, and generating settlement data including consumption summary, amount calculation and billing period time limit based on the optimal billing period interval;

[0128] Step S702: construct a two-dimensional mapping table containing goods flow records and account period data, establish a key-value pair index for the two-dimensional mapping table according to the goods number, locate the target record in the two-dimensional mapping table through the key-value pair index, write the settlement data into the account period field of the target record and update the corresponding goods flow status.

[0129] Optionally, in this embodiment, these two steps implement the generation and status update mechanism of medical device settlement data. First, the system performs a dot multiplication operation on the historical consumption data and the updated time decay factor. The dot multiplication process uses vectorized calculation and parallel processing technology to improve the calculation efficiency. Through this weighted processing, the system effectively balances the influence weights of recent data and long-term data, making the prediction results more in line with the actual situation.

[0130] The weighted sequence is input to the output layer of the LSTM network for processing. The output layer adopts a fully connected structure and uses the softmax activation function to predict the probability distribution. At this stage, the network will consider the long-term dependencies of time series features, filter important information through the gating mechanism, and generate prediction results. Based on the prediction results, the system uses a dynamic programming algorithm to calculate the optimal account period interval.

[0131] In the process of optimizing the payment period, the system comprehensively considers multiple factors, including capital cost, inventory turnover rate and market demand fluctuations. The optimization goal is to maximize the efficiency of capital use while ensuring the stability of the supply chain. The system uses a sliding time window method to dynamically adjust the range of the payment period to adapt to market changes.

[0132] The generation process of settlement data adopts modular design. The consumption summary module is responsible for counting the usage within a specific time period, the amount calculation module calculates the cost according to the price policy, and the account period time limit module determines the payment node. These modules are loosely coupled through the message queue mechanism, which improves the maintainability of the system.

[0133] In the data storage phase, the system builds a two-dimensional mapping structure based on a hash table. The structure stores cargo flow records in the row direction and account period data in the column direction, achieving efficient organization and fast retrieval of data. The system assigns a unique hash key to each cargo, establishes a key-value pair index, and supports search operations with O(1) time complexity.

[0134] The key-value pair index is implemented using distributed hash table technology, and the consistent hash algorithm is used to achieve uniform distribution of data. The system uses a double-layer index structure, with the first-layer index locating data shards and the second-layer index locating specific records, which significantly improves the processing efficiency of large-scale data.

[0135] In the data update phase, the system uses an optimistic locking mechanism to handle concurrent access. The update operation first obtains the version number of the record, and the write operation is performed only when the version number matches. If a version conflict occurs, the system automatically retries the update process. This mechanism ensures data consistency and avoids long lock waits.

[0136] This technical solution effectively solves the complexity of medical device consignment settlement through intelligent account period optimization and efficient data management mechanism. The system realizes the automatic generation of settlement data and real-time update of status, which significantly improves settlement efficiency. At the same time, the distributed storage structure and concurrency control mechanism ensure the high availability of the system and provide technical support for the large-scale application of medical device consignment business. In addition, the solution has strong scalability and can adapt to the growth of business scale, providing reliable guarantee for the long-term development of the enterprise.

[0137] In order to achieve full product traceability, intelligent account management capabilities, and accurate consumption forecasting based on historical data, the present application provides an embodiment of a data processing device for a medical device digital supply chain consignment model for implementing all or part of the data processing method for the medical device digital supply chain consignment model, see Figure 8 The data processing device of the medical device digital supply chain consignment model specifically includes the following contents:

[0138] The multi-level traceability module 10 is used to create a consignment order for medical devices, establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generate a unique traceability identification code, split the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establish a first association index between the product source identification segment and the inventory status identification segment, establish a second association index between the inventory status identification segment and the flow identification segment, and set a data integrity check;

[0139] The consumption registration module 20 is used to retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the third-level flow direction information through the second association index;

[0140] The account period prediction module 30 is used to establish a mapping relationship table between goods flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, and dynamically adjust the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the decay rate parameter is updated in real time according to the fluctuation reference value. The historical data is weighted based on the updated time decay factor, the optimal account period is predicted and a settlement statement is generated, and the goods flow data and account period data are synchronously updated through the mapping relationship table.

[0141] From the above description, it can be seen that the data processing device of the medical device digital supply chain consignment model provided in the embodiment of the present application can establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information by creating consignment orders for medical devices; establish a mapping relationship table between goods flow data and account period data, retrieve the updated third-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, and dynamically adjust the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the historical data is weighted based on the updated time decay factor. The optimal account period is predicted and a settlement statement is generated. The goods flow data and account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data.

[0142] From the hardware level, in order to achieve full product traceability, have intelligent account period management capabilities, and be able to make accurate consumption forecasts based on historical data, the present application provides an embodiment of an electronic device for implementing all or part of the content of the data processing method of the medical device digital supply chain consignment model, and the electronic device specifically includes the following content:

[0143] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the data processing device of the medical device digital supply chain consignment model and the core business system, user terminal and related databases and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., and the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the data processing method of the medical device digital supply chain consignment model and the embodiment of the data processing device of the medical device digital supply chain consignment model in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0144] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0145] In practical applications, part of the data processing method of the medical device digital supply chain consignment model can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0146] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0147] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0148] In one embodiment, the data processing method function of the medical device digital supply chain consignment model can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0149] Step S101: creating a consignment order for medical devices, establishing a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generating a unique traceability identification code, splitting the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establishing a first association index between the product source identification segment and the inventory status identification segment, establishing a second association index between the inventory status identification segment and the flow identification segment, and setting a data integrity check;

[0150] Step S102: Retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index;

[0151] Step S103: Establish a mapping relationship table between cargo flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the cargo flow data and account period data through the mapping relationship table.

[0152] From the above description, it can be seen that the electronic device provided in the embodiment of the present application creates a consignment order for medical devices, establishes a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information; establishes a mapping relationship table between cargo flow data and account period data, retrieves the updated tertiary flow information based on the traceability identification code, inputs the time series consumption data into the long short-term memory network, and dynamically adjusts the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the historical data is weighted based on the updated time decay factor. The optimal account period is predicted and a settlement statement is generated. The cargo flow data and account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data.

[0153] In another embodiment, the data processing device of the medical device digital supply chain consignment model can be configured separately from the central processor 9100. For example, the data processing device of the medical device digital supply chain consignment model can be configured as a chip connected to the central processor 9100, and the data processing method function of the medical device digital supply chain consignment model is implemented through the control of the central processor.

[0154] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0155] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0156] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0157] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0158] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0159] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0160] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0161] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0162] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the data processing method of the medical device digital supply chain consignment model in the above-mentioned embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the data processing method of the medical device digital supply chain consignment model in the above-mentioned embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0163] Step S101: creating a consignment order for medical devices, establishing a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generating a unique traceability identification code, splitting the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establishing a first association index between the product source identification segment and the inventory status identification segment, establishing a second association index between the inventory status identification segment and the flow identification segment, and setting a data integrity check;

[0164] Step S102: Retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index;

[0165] Step S103: Establish a mapping relationship table between cargo flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the cargo flow data and account period data through the mapping relationship table.

[0166] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application creates a consignment order for medical devices, establishes a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information; establishes a mapping relationship table between cargo flow data and account period data, retrieves the updated tertiary flow information based on the traceability identification code, inputs the time series consumption data into the long short-term memory network, dynamically adjusts the time decay factor using an adaptive mechanism, determines the fluctuation reference value by calculating the standard deviation of recent consumption data, weights the historical data based on the updated time decay factor, predicts the optimal account period and generates a settlement statement, and synchronously updates the cargo flow data and account period data through the mapping relationship table, thereby enabling full product traceability, intelligent account period management capabilities, and accurate consumption prediction based on historical data.

[0167] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the data processing method for the medical device digital supply chain consignment model in the above-mentioned embodiment, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the data processing method for the medical device digital supply chain consignment model are implemented. For example, the computer program / instruction implements the following steps:

[0168] Step S101: creating a consignment order for medical devices, establishing a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generating a unique traceability identification code, splitting the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establishing a first association index between the product source identification segment and the inventory status identification segment, establishing a second association index between the inventory status identification segment and the flow identification segment, and setting a data integrity check;

[0169] Step S102: Retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index;

[0170] Step S103: Establish a mapping relationship table between cargo flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the cargo flow data and account period data through the mapping relationship table.

[0171] From the above description, it can be seen that the computer program product provided in the embodiment of the present application creates a consignment order for medical devices, establishes a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information; establishes a mapping relationship table between goods flow data and account period data, retrieves the updated tertiary flow information based on the traceability identification code, inputs the time series consumption data into the long short-term memory network, and dynamically adjusts the time decay factor using an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the historical data is weighted based on the updated time decay factor. The optimal account period is predicted and a settlement statement is generated. The goods flow data and account period data are synchronously updated through the mapping relationship table, thereby realizing full product traceability, having intelligent account period management capabilities, and being able to make accurate consumption predictions based on historical data.

[0172] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0176] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A data processing method for a medical device digital supply chain consignment model, characterized in that: The method comprises: Create a consignment order for medical devices, establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generate a unique traceability identification code, split the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establish a first association index between the product source identification segment and the inventory status identification segment, establish a second association index between the inventory status identification segment and the flow identification segment, and set a data integrity check; Retrieving the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, registering the consumption of the consignment inventory of the medical device, generating a consumption record including the consumption time and the consumption quantity, and updating the consumption record to the tertiary flow direction information through the second association index; A mapping relationship table between cargo flow data and account period data is established, and the updated three-level flow direction information is retrieved based on the traceability identification code. The time series consumption data is input into the long short-term memory network, and the time decay factor is dynamically adjusted by an adaptive mechanism. The fluctuation reference value is determined by calculating the standard deviation of recent consumption data, and the decay rate parameter is updated in real time according to the fluctuation reference value. The historical data is weighted based on the updated time decay factor, the optimal account period is predicted and a settlement statement is generated, and the cargo flow data and account period data are synchronously updated through the mapping relationship table.

2. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The above-mentioned creation of a consignment order for medical devices, establishment of a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, and generation of a unique traceability identification code includes: Generate a 36-bit unique identification code based on the basic information of the medical device, the unique identification code including the device number, production batch number and timestamp information, and create a consignment order based on the unique identification code; Medical devices are classified according to suppliers, production dates and models to establish a product source information database; inventory status information database is established according to inventory locations, storage conditions and quantities; flow information database is established according to use departments, consumption records and flow destinations; the product source information database, inventory status information database and flow information database are combined to form a multi-level traceability structure.

3. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The step of splitting the traceability identification code into a product source identification segment, an inventory status identification segment, and a flow direction identification segment, establishing a first association index between the product source identification segment and the inventory status identification segment, establishing a second association index between the inventory status identification segment and the flow direction identification segment, and setting a data integrity check includes: The 36-digit traceability identification code is split into a fixed-length format of 12-12-12 digits. The first 12 digits are used as the product source identification segment to store supplier and production information, the middle 12 digits are used as the inventory status identification segment to store inventory location and status information, and the last 12 digits are used as the flow identification segment to store the use department and consumption information. A first associated index value is generated by performing a bit-by-bit XOR operation on the product source identification segment and the inventory status identification segment, a second associated index value is generated by performing a bit-by-bit XOR operation on the inventory status identification segment and the flow direction identification segment, and a cyclic redundancy check algorithm is used to perform data verification on the first associated index value and the second associated index value.

4. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The retrieving the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, registering the consumption of the consignment inventory of the medical device, generating a consumption record including the consumption time and the consumption quantity, and updating the consumption record to the tertiary flow direction information through the second association index, includes: Obtaining an inventory status identification segment through the first associated index value, comparing and matching the inventory status identification segment with a corresponding record in an inventory status information library, reading inventory status information of the medical device, and recording a usage time point and a specific usage quantity of the medical device in the inventory status information; The usage record in the inventory status information of the medical device is associated with the second associated index value, the corresponding position in the three-level flow information library is located based on the mapping relationship, and the usage time point and usage quantity are written into the consumption record field of the flow information library to complete data synchronization.

5. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The time series consumption data is input into the long short-term memory network, and the time decay factor is dynamically adjusted using an adaptive mechanism, including: Constructing a time series data structure based on a 64-bit floating point format, writing the consumption data of medical devices into the data structure according to the time dimension, normalizing the data through an input gate of a long short-term memory network, and setting a recurrent neuron structure including three hidden layers in the long short-term memory network; The consumption data difference between adjacent time nodes is calculated to obtain the time interval matrix, and the time interval matrix is ​​input into the exponential function to generate the initial time decay factor. The cardinality parameter of the exponential function is adjusted in real time according to the prediction error of the long short-term memory network, so that the time decay factor decreases exponentially with the increase of the time interval.

6. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The method of determining the fluctuation reference value by calculating the standard deviation of recent consumption data and updating the decay rate parameter in real time according to the fluctuation reference value includes: The medical device consumption data for nearly 30 days is used to form a statistical sample, and based on the statistical sample, the mean of the consumption and the sum of squares of deviations of each sampling point are calculated, and the square root of the sum of squares of deviations is divided by the number of sampling points to obtain a fluctuation standard deviation, and the fluctuation standard deviation is compared with a preset threshold to obtain a fluctuation reference value; The fluctuation reference value is substituted into the sigmoid function to calculate the initial value of the attenuation rate, the slope coefficient of the sigmoid function is adjusted based on the changing trend of the fluctuation reference value, and the updated attenuation rate parameter is output through the dynamically adjusted sigmoid function.

7. The data processing method of the medical device digital supply chain consignment model according to claim 1 is characterized in that: The method of weighting the historical data based on the updated time decay factor, predicting the optimal account period and generating a settlement statement, and synchronously updating the cargo flow data and the account period data through the mapping relationship table includes: Performing a point multiplication operation on the historical consumption data of medical devices and the updated time decay factor to obtain a weighted sequence, inputting the weighted sequence into the output layer of the long short-term memory network, calculating the optimal account period interval according to the output result, and generating settlement data including consumption summary, amount calculation and account period time limit based on the optimal account period interval; Construct a two-dimensional mapping table containing goods flow records and account period data, establish a key-value pair index for the two-dimensional mapping table according to the goods number, locate the target record in the two-dimensional mapping table through the key-value pair index, write the settlement data into the account period field of the target record and update the corresponding goods flow status.

8. A data processing device for a medical device digital supply chain consignment model, characterized in that: The device comprises: A multi-level traceability module is used to create a consignment order for medical devices, establish a multi-level traceability structure including primary product source information, secondary inventory status information and tertiary flow information, generate a unique traceability identification code, split the traceability identification code into a product source identification segment, an inventory status identification segment and a flow identification segment, establish a first association index between the product source identification segment and the inventory status identification segment, establish a second association index between the inventory status identification segment and the flow identification segment, and set a data integrity check; a consumption registration module, configured to retrieve the secondary inventory status information corresponding to the inventory status identification segment according to the first association index, register the consumption of the consignment inventory of the medical device, generate a consumption record including the consumption time and the consumption quantity, and update the consumption record to the tertiary flow direction information through the second association index; The account period prediction module is used to establish a mapping relationship table between goods flow data and account period data, retrieve the updated three-level flow information based on the traceability identification code, input the time series consumption data into the long short-term memory network, dynamically adjust the time decay factor using an adaptive mechanism, determine the fluctuation reference value by calculating the standard deviation of recent consumption data, and update the decay rate parameter in real time according to the fluctuation reference value, weight the historical data based on the updated time decay factor, predict the optimal account period and generate a settlement statement, and synchronously update the goods flow data and account period data through the mapping relationship table.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the data processing method for the medical device digital supply chain consignment model according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data processing method for the medical device digital supply chain consignment model described in any one of claims 1 to 7 are implemented.

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