Intelligent blood management method and system

Abstract

The invention provides an intelligent blood management method and system. The method comprises the following steps: identifying a mapping relationship between a blood product and a preset blood classification unit; determining an estimated value of the use number of the blood products in response to the mapping relationship meeting a preset rule; and performing Bayesian updating on the use quantity estimated value according to historical inventory data of the blood products in the preset blood classification unit to obtain real-time inventory data of the blood products in the preset blood classification unit at the current moment. Through the mapping relation between the blood products and the preset blood classification unit, the inventory of different types of blood products can be accurately tracked and managed. And the estimated value of the use number is determined according to the weight change of the blood products in the preset blood classification unit at the current moment, so that the real-time performance and accuracy of inventory management are further improved. And a Bayesian updating method is adopted to correct the estimated value of the use quantity, so that the inventory condition at the current moment can be reflected more accurately, and the intelligent level of blood product inventory management is improved.

Description

Technical Field

[0001] The present application relates to the field of blood management technology, and in particular to an intelligent blood management method and system. Background Art

[0002] In modern healthcare, blood product inventory management is directly related to the safety and efficiency of clinical transfusions. However, the widespread availability of diverse specifications makes inventory counting and management of blood products extremely complex. Operators must frequently open and inspect blood boxes during inventory counts, which is not only inefficient but also prone to errors in blood product specifications due to manual input, posing a serious safety risk.

[0003] It should be noted that the above introduction to the technical background is merely intended to provide a clear and complete description of the technical solutions of this application and facilitate understanding by those skilled in the art. Simply because these solutions are described in the background technology section of this application, it should not be assumed that the above technical solutions are well known to those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first purpose of this application is to propose an intelligent blood management method.

[0006] The second objective of this application is to provide an intelligent blood management system.

[0007] The third objective of this application is to provide an electronic device.

[0008] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first embodiment of the present application proposes a smart blood management method, including:

[0011] Identify the mapping relationship between blood products and pre-set blood classification units;

[0012] In response to the mapping relationship meeting a preset rule, determining an estimated usage quantity of the blood product according to a weight change of the blood product in the preset blood classification unit at a current moment;

[0013] The usage quantity estimate is subjected to Bayesian update based on historical inventory data of blood products in the preset blood classification unit to obtain real-time inventory data of blood products in the preset blood classification unit at the current moment.

[0014] To achieve the above objectives, the second embodiment of the present application proposes an intelligent blood management system, including:

[0015] an identification module, the identification module being used to identify a mapping relationship between blood products and preset blood classification units;

[0016] an acquisition module, configured to determine an estimated usage quantity of the blood product based on a weight change of the blood product in the preset blood classification unit at a current moment in response to the mapping relationship conforming to a preset rule;

[0017] An updating module is configured to perform a Bayesian update on the usage quantity estimate based on the historical inventory data of the blood products in the preset blood classification unit to obtain the real-time inventory data of the blood products in the preset blood classification unit at the current moment.

[0018] To achieve the above-mentioned purpose, the third embodiment of the present application proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the intelligent blood management method proposed in the first embodiment of the present application.

[0019] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the intelligent blood management method proposed in the first embodiment of the present application.

[0020] To achieve the above-mentioned purpose, the fifth embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor in a communication device, implements the intelligent blood management method proposed in the first embodiment of the present application.

[0021] In the embodiment of the present application, the mapping relationship between blood products and preset blood classification units can be used to more accurately track and manage the inventory of different types of blood products, which helps to avoid inventory shortages or surpluses. The estimated usage quantity is determined based on the weight change of the blood products in the preset blood classification unit at the current moment, further improving the real-time and accuracy of inventory management. Based on the historical inventory data of blood products in the preset blood classification unit, the Bayesian update method is used to correct the estimated usage quantity, which can more accurately reflect the inventory status at the current moment, thereby optimizing the resource allocation decision of blood products and improving the intelligent level of blood product inventory management.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A schematic diagram of a flow chart of an intelligent blood management method provided in an embodiment of the present application;

[0025] Figure 2 A flow chart of another intelligent blood management method provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of an intelligent blood management system provided in an embodiment of the present application;

[0027] Figure 4 The figure is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numbers in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0029] The terms used in the embodiments of this application are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of this application. The singular forms "a" and "the" used in the embodiments of this application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to a determination."

[0031] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] In the modern healthcare environment, inventory management of blood products is directly related to the safety and efficiency of clinical blood transfusions. However, the widespread availability of diverse specifications makes inventory counting and management of blood products extremely complex. Specifically, blood products come in a variety of specifications, including different blood types (such as type A, type B, type O, and type AB) and volumes (such as 50ml, 100ml, 200ml, etc.), which increases the complexity of management. In addition, the shelf life of blood products varies, ranging from a few days to several months, which further increases the difficulty of inventory management.

[0033] When conducting inventory counts, operators need to frequently open and inspect blood product boxes. This process is not only inefficient but also prone to errors in blood product specifications due to manual input. For example, manual inventory recording and management is prone to errors, which can lead to the transfusion of mismatched blood, endangering patient safety. Frequent unpacking increases temperature fluctuations in refrigeration equipment and can cause blood product temperatures to rise above -18°C, seriously affecting their quality. Furthermore, prolonged operation in low-temperature environments can easily lead to frostbite, making it difficult to ensure worker safety. This high-risk operation not only threatens blood quality but also poses potential risks to worker health. Furthermore, manual inventory recording and management methods fail to track inventory changes in real time, resulting in discrepancies between inventory information and actual inventory, increasing the risk of blood expiration and waste.

[0034] Furthermore, the use of radio frequency identification (RFID) technology can improve the efficiency of blood management to a certain extent. Specifically, RFID can transmit and receive signals to identify the presence of blood product bags, but it is more difficult to determine the specific storage location of blood products (such as the drawer or column of the refrigerated equipment). If the RFID tag is damaged, obscured, or interfered with by other materials, it may cause recognition failure, thereby affecting the accuracy of inventory data.

[0035] The following describes the smart blood management method and system thereof according to embodiments of the present application with reference to the accompanying drawings.

[0036] Figure 1 A flow chart of an intelligent blood management method provided in an embodiment of the present application.

[0037] like Figure 1As shown, the method includes but is not limited to the following steps:

[0038] S101, identifying a mapping relationship between blood products and preset blood classification units.

[0039] In a feasible implementation, in the medical system, blood products are important therapeutic resources, with a wide variety and different uses. In order to manage these blood products efficiently and safely, medical institutions usually preset a series of blood classification units to classify, store and distribute different types of blood products. Identifying the mapping relationship between blood products and preset blood classification units is a key step in achieving this goal. Through this mapping, medical institutions can have a clearer understanding of the category of each blood product, thereby optimizing inventory management, improving resource utilization efficiency, and ensuring that blood products can be quickly and accurately supplied to clinical use when needed.

[0040] In one feasible embodiment, during the storage of blood products, blood products, including red blood cell products, platelet products, plasma products, cryoprecipitate products, immunoglobulin products, and recombinant blood products, are typically matched to pre-defined blood classification units based on their specific composition, function, and clinical use. A detailed mapping table or database is developed to record the correspondence between each blood product and the classification unit. This correspondence is represented as a mapping relationship.

[0041] In a feasible embodiment, by identifying the mapping relationship between blood products and preset blood classification units, the required blood products can be quickly located and found, reducing inventory search time, thereby helping to achieve accurate inventory management and avoid inventory backlogs and waste. Furthermore, based on the mapping relationship, the usage of blood products in different preset blood classification units can be obtained, and resources can be allocated more reasonably to ensure the supply of key blood products (such as plasma products of rare blood types). Furthermore, an accurate mapping relationship can ensure that blood products can be quickly and accurately supplied to clinical use when needed, thereby improving the success rate of patient treatment. An accurate mapping relationship can also reduce the risks caused by useless or cross-contamination of blood products and ensure patient safety.

[0042] S102 , in response to the mapping relationship conforming to the preset rule, determining an estimated usage quantity of the blood product according to the weight change of the blood product in the preset blood classification unit at the current moment.

[0043] In one feasible embodiment, before performing usage quantity estimation, it is first necessary to confirm that the mapping relationship complies with the preset rules. Furthermore, the mapping rules between blood products and preset blood classification units are clarified. For example, a specific blood product can only be classified into one or several specific classification units. Furthermore, the mapping relationship data in the current system is checked to ensure that all blood products have been correctly classified into the preset classification units. Furthermore, for blood products that do not comply with the mapping rules, exception processing is performed, such as reclassification or marking as pending status, to ensure the accuracy of subsequent calculations.

[0044] In one feasible embodiment, after confirming that the mapping relationship complies with preset rules, the estimated usage quantity can be determined based on the weight change of the blood products in the preset blood classification unit at the current moment. Furthermore, weight data of the blood products in the preset blood classification unit is regularly collected, including the initial weight, current weight, and weight change timestamps. The accuracy and real-time nature of the collected data are ensured to promptly reflect the usage of the blood products.

[0045] Furthermore, for each preset blood classification unit, the weight change between the current moment and the previous moment is calculated, where weight change = previous moment weight - current moment weight. Furthermore, based on the unit weight of the blood product (e.g., per milliliter, per gram, per bag, etc.) and the known weight change, an estimated usage quantity is calculated, where the usage quantity is the number of units of the blood product used, and the estimated usage quantity = weight change / unit weight. For example, one unit of plasma is equal to 100 milliliters of plasma, which has a unit weight of approximately 100 grams.

[0046] It should be noted that in the process of opening the refrigeration equipment to take out blood products, there will be weight changes due to non-usage reasons such as evaporation and loss, so the estimated usage quantity needs to be corrected.

[0047] S103, performing Bayesian update on the usage quantity estimate based on the historical inventory data of the blood products in the preset blood classification unit to obtain the real-time inventory data of the blood products in the preset blood classification unit at the current moment.

[0048] In one feasible implementation, blood product inventory management typically follows a normal distribution, so historical inventory data for blood products within pre-set blood classification units provides usage patterns and inventory changes at different points in time. This data can be used to construct a priori probability models to help predict future usage trends. For example, by analyzing blood product usage data over the past few months or years, it is possible to understand changes in blood product demand across different seasons and time periods.

[0049] In one feasible implementation, a prior probability distribution for each blood product classification unit can be determined based on historical inventory data. Using a Bayesian updating method, the prior probabilities are combined to determine posterior probabilities. Based on the posterior probabilities and the corresponding current usage quantity corrections, the most likely state of the blood product inventory at the current moment is determined.

[0050] For example, based on the weight change of blood products within the preset blood classification unit at the current moment, the estimated quantity of blood products used is determined to be 200 units. Based on the posterior probability, the corresponding revised quantity used at the current moment is determined to be 197 units. Therefore, the current blood product inventory is the difference between 200 units and 110 units, i.e., 3 units.

[0051] In summary, the intelligent blood management method provided in the embodiment of the present application can more accurately track and manage the inventory of different types of blood products through the mapping relationship between blood products and preset blood classification units, which helps to avoid inventory shortages or surpluses. The estimated usage quantity is determined based on the weight change of the blood products in the preset blood classification unit at the current moment, which further improves the real-time and accuracy of inventory management. Based on the historical inventory data of blood products in the preset blood classification unit, the Bayesian update method is used to correct the estimated usage quantity, which can more accurately reflect the inventory status at the current moment, thereby optimizing the resource allocation decision of blood products and improving the intelligence level of blood product inventory management.

[0052] Figure 2 A flowchart of another intelligent blood management method provided in an embodiment of the present application.

[0053] like Figure 2 As shown, the method includes but is not limited to the following steps:

[0054] S201, identifying a mapping relationship between blood products and preset blood classification units.

[0055] For further details on step S201, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0056] S202 , in response to the mapping relationship meeting the preset rule, obtaining first inventory data of blood products in the preset blood classification unit after weighing at the current moment, and second inventory data of blood products in the preset blood classification unit after weighing at the previous moment.

[0057] In one feasible embodiment, if the blood product's blood type and weight match the identification code in a preset blood classification unit, the mapping relationship is determined to comply with the preset rules. Furthermore, the preset blood classification unit has a unique identification code (including a master code, blood product category code, blood type code, expiration date code, RFID tag, etc.), and the blood product's identification code must be completely consistent with the identification code of the preset blood classification unit to ensure the accuracy of the mapping relationship. Furthermore, the identification code includes the blood product type (such as the blood type of plasma products), units (including milliliters and weight), etc.

[0058] In one feasible embodiment, if the mapping relationship does not meet the preset rules, the blood type and weight of the blood product are obtained. Further, the identification codes in each preset blood classification unit are traversed to obtain a target preset blood classification unit whose identification code matches the blood type and weight, and the blood product is stored in the target preset blood classification unit.

[0059] In a feasible implementation, according to a preset weighing device in the preset blood classification unit, weight data of the blood products in the preset blood classification unit after weighing at the current moment is recorded, and the weight data is represented as the first inventory data.

[0060] Furthermore, the first inventory data is recorded in an inventory management database, including information such as the identification code of the preset blood classification unit, weighing time, weight data, etc., for subsequent query and analysis.

[0061] Furthermore, the inventory management database is searched for the inventory data of the preset blood classification unit at the last moment, and the inventory data at the last moment is verified, including information such as identification code, weighing time, weight data, etc. The weight data at the last moment is represented as the second inventory data.

[0062] S203: Determine an estimated usage quantity of the blood product based on the first inventory data and the second inventory data.

[0063] In one feasible embodiment, the weight change value is determined based on the first inventory data and the second inventory data. Furthermore, a difference operation is performed on the first inventory data and the second inventory data to obtain a difference value, which is used as the weight change value, wherein the weight change value is equal to the first inventory data minus the second inventory data.

[0064] In a feasible implementation, the estimated usage quantity of the blood product in the preset blood classification unit is determined based on the weight change value.

[0065] For example, according to the weight change value ΔW and the weight of each unit of blood product W unit Determine the estimated number of blood products used within the preset blood classification unit N removed , where Nremoved =ΔW / W unit .

[0066] S204 , performing Bayesian update on the usage quantity estimate based on the historical inventory data of the blood products in the preset blood classification unit to obtain the real-time inventory data of the blood products in the preset blood classification unit at the current moment.

[0067] In a feasible embodiment, the historical inventory data of blood products in the preset blood classification unit is obtained. The prior mean μ is determined based on the historical inventory data. pr ior and prior standard deviation σ pr ior.

[0068] Furthermore, the prior mean μ prior It is the average inventory level calculated based on historical inventory data. The calculation formula is:

[0069]

[0070] Where n represents the number of data points in the historical inventory data, x i Expressed as the inventory at the i-th time point.

[0071] Furthermore, the prior standard deviation σ pr IOR is the discrete degree of inventory calculated based on historical inventory data. Its calculation formula is:

[0072]

[0073] In a feasible implementation, according to the prior mean μ pr ior and prior standard deviation σ pr ior, perform Bayesian update on the usage quantity estimate to obtain the real-time inventory data of blood products in the preset blood classification unit at the current moment.

[0074] Furthermore, according to the first inventory data of the blood products in the preset blood classification unit after weighing at the current moment, the estimated mean μ of the weighing at the current moment is determined. likelihood and estimated standard deviation σ likelihood About estimating the mean μ likelihood and estimated standard deviation σ likelihood The acquisition process of can refer to the relevant contents in the above embodiment, which will not be repeated here.

[0075] Furthermore, according to the prior mean μ pr ior, prior standard deviation σ pr ior, estimated mean μlikelihood and estimated standard deviation σlikelihood, determine the posterior mean μ poster ior and posterior standard deviation σposter ior. It should be noted that the following formula is used to determine the posterior mean μ poster ior and posterior standard deviation σ poster ior:

[0076]

[0077] Among them, the posterior standard deviation σ poster ior is used to measure the posterior mean μ poster The uncertainty of ior, the smaller the posterior standard deviation σ poster ior means the posterior mean μ poster ior is more precise and has lower uncertainty. Larger posterior standard deviation σ poster ior means the posterior mean μ posterior The uncertainty is high and more historical inventory data is needed for further correction. The specific correction process will not be described here.

[0078] Furthermore, by the posterior mean μ posterior and the posterior standard deviation σ posterior Estimated value of the number of uses N removed Perform Bayesian updating to obtain the corrected real-time inventory data of blood products in each preset blood classification unit. It should be noted that the real-time inventory data is determined using the following relationship:

[0079]

[0080] Among them, N updated Represents real-time inventory data, from the interval Determine real-time inventory data within N updated The unit weight represents the weight of the blood product in each unit, and the estimated quantity × unit weight equals the weight change.

[0081] For example, if the weight change and posterior mean μ poster ior and posterior standard deviation σ poster ior, the interval obtained The value 18 is determined from the interval [17.13, 18.56] as the real-time inventory data N updated The value of .

[0082] For example, if the interval is [17.13, 19.74], which can be achieved by adjusting the prior mean μ pr ior and prior standard deviation σ pr ior, thereby updating the posterior mean μ poster ior and posterior standard deviation σ posterThe value of ior. According to the updated posterior mean μ poster ior and posterior standard deviation σ poster ior, adjustment interval The lower limit integer part and the upper limit integer part of the lower limit integer part and the upper limit integer part are adjusted to make the step size between the lower limit integer part and the upper limit integer part 1, for example, the adjusted interval [17.23, 18.48]. Determine the value 18 from the interval [17.23, 18.48] as the real-time inventory data N updated The value of .

[0083] It should be noted that the prior mean μ can be adjusted by adjusting the number of samples in the historical inventory data. prior and the prior standard deviation σ pr ior adjustment operation, thereby adjusting the posterior mean μ poster ior and posterior standard deviation σ poster ior is updated, wherein the methods of adjusting the number of samples in historical inventory data include: increasing the sampling period to expand the number of data points, keeping the sampling period unchanged and interpolating and filling according to the trend of data points.

[0084] It should be noted that the prior mean μ pr ior and prior standard deviation σ pr ior can be used to construct a priori probability distributions, providing a basis for Bayesian updating. In practical applications, these parameters can help more accurately predict the real-time inventory data of blood products within pre-set blood classification units, thereby optimizing inventory management.

[0085] In summary, the intelligent blood management method provided in the embodiment of the present application can more accurately track and manage the inventory of different types of blood products through the mapping relationship between blood products and preset blood classification units, which helps to avoid inventory shortages or surpluses. The estimated usage quantity is determined based on the weight change of the blood products in the preset blood classification unit at the current moment, which further improves the real-time and accuracy of inventory management. Based on the historical inventory data of blood products in the preset blood classification unit, the Bayesian update method is used to correct the estimated usage quantity, which can more accurately reflect the inventory status at the current moment, thereby optimizing the resource allocation decision of blood products and improving the intelligence level of blood product inventory management.

[0086] Figure 3 This is a schematic diagram of the structure of a smart blood management system provided by an embodiment of the present application. Figure 3 As shown, the smart blood management system 300 includes:

[0087] Identification module 301, identification module 301 is used to identify the mapping relationship between blood products and preset blood classification units;

[0088] An acquisition module 302 is configured to determine an estimated usage quantity of the blood product based on a weight change of the blood product in the preset blood classification unit at a current moment in response to the mapping relationship meeting a preset rule;

[0089] The updating module 303 is used to perform Bayesian update on the usage quantity estimation value based on the historical inventory data of blood products in the preset blood classification unit to obtain the real-time inventory data of blood products in the preset blood classification unit at the current moment.

[0090] In particular, according to an embodiment of the present application, the intelligent blood management system described in the structural diagram above can be installed in a refrigerated storage device, wherein the refrigerated storage device includes drawers arranged in an array, within which preset blood classification units are located; and a status display screen is provided on the outer surface of the refrigerated storage device, which is used to display the real-time inventory data of the blood products in the preset blood classification units at the current moment. It should be noted that during the process of opening the refrigerated storage device to remove blood products or storing blood products, the real-time inventory data is updated accordingly.

[0091] Figure 4 The figure is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0092] like Figure 4 As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the memory 406 into the random access memory (RAM) 403. Various programs and data required for the operation of the electronic device 400 are also stored in the RAM 403. The processor 401, ROM 402 and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0093] The following components are connected to the I / O interface 405: a memory 406 including a hard disk, etc.; and a communication part 407 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., which performs communication processing via a network such as the Internet; a drive 408 is also connected to the I / O interface 405 as needed.

[0094] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 407. When the computer program is executed by the processor 401, the above-mentioned functions defined in the method of the present application are performed.

[0095] In an exemplary embodiment, a storage medium including instructions is further provided, such as a memory including instructions, and the instructions can be executed by the processor 401 of the electronic device 400 to perform the above method. Alternatively, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0096] In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0097] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0098] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A smart blood management method, characterized in that: include: Identify the mapping relationship between blood products and pre-set blood classification units; In response to the mapping relationship meeting a preset rule, determining an estimated usage quantity of the blood product according to a weight change of the blood product in the preset blood classification unit at a current moment; The usage quantity estimate is subjected to Bayesian update based on historical inventory data of blood products in the preset blood classification unit to obtain real-time inventory data of blood products in the preset blood classification unit at the current moment.

2. The method according to claim 1, characterized in that The response that the mapping relationship complies with a preset rule includes: If the blood type and weight of the blood product match the identification code in the preset blood classification unit, it is determined that the mapping relationship complies with the preset rule.

3. The method according to claim 2, characterized in that The step of determining the estimated usage quantity of the blood product according to the weight change of the blood product in the preset blood classification unit at the current moment includes: Acquire first inventory data of blood products in the preset blood classification unit after weighing at a current moment, and second inventory data of blood products in the preset blood classification unit after weighing at a previous moment; determining a weight change value based on the first inventory data and the second inventory data; An estimated usage quantity of the blood product in the preset blood classification unit is determined according to the weight change value, wherein the usage quantity is the unit number of the blood product used.

4. The method according to claim 3, characterized in that The determining of the weight change value according to the first inventory data and the second inventory data includes: performing a differential operation on the first inventory data and the second inventory data to obtain a differential value; The difference value is taken as the weight change value.

5. The method according to any one of claims 1 to 4, characterized in that The method of performing Bayesian updating on the usage quantity estimate based on the historical inventory data of the blood products in the preset blood classification unit to obtain the real-time inventory data of the blood products in the preset blood classification unit at the current moment includes: Obtaining historical inventory data of blood products in the preset blood classification unit; determining a priori mean and a priori standard deviation based on the historical inventory data; The usage quantity estimate is subjected to Bayesian updating according to the prior mean and the prior standard deviation to obtain real-time inventory data of blood products in the preset blood classification unit at the current moment.

6. The method according to claim 5, characterized in that The method of performing Bayesian updating on the usage quantity estimate based on the prior mean and the prior standard deviation to obtain real-time inventory data of blood products in the preset blood classification unit at the current moment includes: Determining an estimated mean and an estimated standard deviation of the current weighing according to first inventory data of blood products in a preset blood classification unit after the current weighing; Determining a posterior mean and a posterior standard deviation based on the prior mean, the prior standard deviation, the estimated mean, and the estimated standard deviation; The usage quantity estimate is subjected to Bayesian updating based on the posterior mean and the posterior standard deviation to obtain corrected real-time inventory data of blood products in each of the preset blood classification units.

7. The method according to claim 6, characterized in that The following formula is used to determine the posterior mean and posterior standard deviation: Among them, the μ poster ior represents the posterior mean, σ poster ior represents the posterior standard error, μ pr ior represents the prior mean, σprior represents the prior standard deviation, μlikelihood represents the estimated mean, and σlikelihood represents the estimated standard deviation.

8. The method according to claim 6, characterized in that The real-time inventory data is determined using the following relationship: Among them, the N updated Represents real-time inventory data, the unit weight represents the weight of each unit of blood products, from the interval Determine the quantity of real-time inventory data.

9. The method according to claim 1, characterized in that Also includes: In response to the mapping relationship not meeting a preset rule, obtaining the blood type and weight of the blood product; The identification codes in the preset blood classification units are traversed to obtain a target preset blood classification unit whose identification code matches the blood type and weight, and the blood product is stored in the target preset blood classification unit.

10. An intelligent blood management system, characterized in that: include: an identification module, the identification module being used to identify a mapping relationship between blood products and preset blood classification units; an acquisition module, configured to determine an estimated usage quantity of the blood product based on a weight change of the blood product in the preset blood classification unit at a current moment in response to the mapping relationship conforming to a preset rule; An updating module is configured to perform a Bayesian update on the usage quantity estimate based on the historical inventory data of the blood products in the preset blood classification unit to obtain the real-time inventory data of the blood products in the preset blood classification unit at the current moment.

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