Artificial intelligence-based consumable monitoring data analysis system and method

Abstract

The application discloses a consumable monitoring data analysis system and method based on artificial intelligence, and relates to the technical field of data analysis.The application comprises the following steps: calling historical taking and using records of consumables, using the consumables by various departments of a service organization after taking and using, judging and extracting target records; obtaining reference value ranges of various physical properties of the consumables, deploying various sensors, obtaining target values of the consumables in each physical property at each moment, and obtaining target properties of the consumables; extracting a historical date window and a target city, obtaining a feature duration of the consumables according to weather conditions of the target city, further obtaining an early warning moment of a current traceability code, and giving a code early warning prompt to the consumables after the early warning moment.The application analyzes the historical taking and using records and various physical properties of the consumables, gives a code early warning prompt to the produced consumables, effectively avoids the loss of timeliness of the traceability code, and guarantees the full-cycle reliability of the traceability data of the consumables.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a consumables monitoring data analysis system and method based on artificial intelligence. Background Technology

[0002] Frequent safety incidents involving consumables threaten public health and damage patient rights and industry reputation. Full-chain traceability of consumable production, transportation, and use—recording, storing, and tracing data at each stage—is a crucial safety management method that clarifies the nodes from production to distribution and ensures legal accountability for safe consumable manufacturing. However, because the quality of some consumables can change significantly over time, the information recorded in consumable safety traceability may lose its timeliness and reference value. For example, inputting traceability information recorded in the early stages of consumable production into a later traceability system reduces reliability, rendering the traceability code meaningless. Therefore, a real-time information verification mechanism is needed to ensure the reliability of consumable traceability data throughout its entire lifecycle, prevent traceability codes from becoming invalid, and improve the reliability of consumable manufacturing. Summary of the Invention

[0003] The purpose of this invention is to provide an artificial intelligence-based consumable monitoring data analysis system and method to solve the problems raised in the prior art.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: The artificial intelligence-based data analysis method for consumable monitoring includes the following steps: Retrieve historical usage records of consumables. Consumables used after usage are supplied to various departments under the service organization. Extract the user and department corresponding to the usage record. Based on the user's authority and department, determine and extract the target record in the usage record. Obtain the storage environment information from the instruction manual of the consumables, and extract the reference value range of various physical characteristics from the storage environment information; deploy sensors in the service facility, and record the location of the corresponding consumables after they are picked up and the sensor location according to the target, so as to obtain the target value of the consumables in each physical characteristic at each moment. Based on the target value and the reference value range of various physical characteristics, the monitoring period of the physical characteristics is obtained; the monitoring area in the consumable is obtained, the number of bacteria per unit in the monitoring area during the monitoring period is analyzed, the fitting function corresponding to each physical characteristic of the consumable is obtained, and the target characteristic of the consumable is obtained based on the fitting function. Based on the current date, extract the historical date window to obtain the recent weather conditions of the target city corresponding to the consumable, as well as the weather conditions within the date window. Based on the change in the number of bacteria per unit in the monitoring area of ​​the consumable within the date window, obtain the current characteristic duration of the consumable. Obtain the current traceability code assignment time on the consumable, get the warning time of the current traceability code, and assign a warning code to consumables after the warning time.

[0005] Preferably, the target record in the retrieval record is determined and extracted, including: retrieving the historical retrieval record of consumables, which is the record in which the user fills in the relevant information after retrieving consumables according to the department's needs; extracting the user and the department corresponding to a certain retrieval record. If the user has the authority to retrieve consumables and the department to which the user belongs is consistent with the department to which a certain retrieval record is retrieved, then the certain retrieval record is taken as the target record.

[0006] Preferably, the target values ​​of the consumables at each moment in terms of each physical characteristic are obtained, including: Several sensors are deployed within the service facility to monitor various physical characteristics, and the location of each sensor is obtained. The surveillance video of consumable M after its use is retrieved, corresponding to a specific target record R. The position of consumable M at each moment in the surveillance video is captured, and the position at a certain moment T is taken as P. T To obtain the positions of all sensors monitoring a certain type of physical characteristic X, if the position of a certain sensor is related to position P... T If the distance between them is less than a preset distance threshold, then the sensing value of a certain sensor at time T will be used as the target value of consumable M in terms of physical property X at time T. If there is no sensor location and location P T If the distance between them is less than a distance threshold, then the position P is obtained. T The distance between each sensor is calculated, and the reciprocal of each distance is taken as the characteristic distance of each corresponding sensor. All characteristic distances are added together to obtain the total characteristic distance. Each characteristic distance is divided by the total characteristic distance to obtain the numerical weight corresponding to each sensor. Then, based on the sensing value of each sensor at time T and the corresponding numerical weight, the target value of the consumable at time T in terms of physical characteristic X is obtained. Thus, the target value of the consumable at each time in terms of each physical characteristic is obtained.

[0007] Preferably, the target characteristics of the consumables include: Based on the reference value ranges of various physical properties of consumables, the time when the target value is outside the reference value range is designated as the marked time; the time period when the number of marked times for a certain physical property X exceeds a preset threshold, and the number of marked times for all other physical properties is 0, is designated as the monitoring period D for physical property X. X ; The area on the consumable that comes into contact with the ROI area is designated as the monitoring area; the monitoring area is obtained during the monitoring period D. XThe number of bacteria per unit at several time points is used to fit a linear function using the least squares method to obtain the fitting function of the number of bacteria per unit over time corresponding to the physical characteristic X of the consumable. The fitting function corresponding to each physical characteristic is obtained, and the physical characteristic with the largest slope is taken as the target characteristic of the consumable.

[0008] Preferably, the current characteristic duration of the consumable is obtained, including: Extract dates from the past year that share the same month and day as the current date, and extract a date window consisting of several adjacent days centered on that date; since the cities where consumables are produced and sold are the same, use that city as the target city and obtain the weather conditions of the target city within the date window; Based on the recent weather conditions of the target city, the actual values ​​of the target city in various physical characteristics are obtained, the average values ​​of various physical characteristics are obtained recently, and based on the weather conditions within the date window, the average values ​​of various physical characteristics within each historical date window are calculated. The date windows in which the difference between the average value of the target characteristic and the corresponding recent average value is less than a preset first difference threshold, and the difference between the average values ​​of other physical characteristics and the corresponding recent average values ​​is less than a preset second difference threshold, are designated as the marked windows. In this scheme, the target characteristic is different from the other physical characteristics. The target characteristic is the standard that best represents whether the consumable is usable. Therefore, in this scheme, different evaluation criteria are needed for the target characteristic and the other physical characteristics. Here, in order to make the judgment result more accurate and reliable, the first difference threshold is less than the second difference threshold, and then the marking window is obtained.

[0009] Get a consumable that has been produced within the marked window. Starting from the time of production completion, extract several detection times and perform bacterial detection on the monitoring area of ​​the consumable to obtain the number of bacteria per unit in the monitoring area at each detection time. The earliest detection time in which the number of bacteria per unit in the monitored area exceeds the preset bacterial count threshold is defined as T2. The number of bacteria per unit at detection time T2 is defined as N2, and the bacterial count threshold is defined as N0. The number of bacteria per unit at the previous detection time T1 is defined as N1. According to the formula... The target time T0 is obtained, and the duration between the target time T0 and the completion time of a certain consumable is used as the current characteristic duration CD of the consumable.

[0010] Preferably, the steps for obtaining the warning time of the current traceability code include: taking the earliest coding time of the current traceability code on the consumable as T1, and taking the time after coding time T1 when the duration is equal to the feature duration CD as the warning time of the current traceability code.

[0011] The consumables monitoring data analysis system based on artificial intelligence includes a target value calculation module, a target characteristic analysis module, and a coding early warning module; Target value calculation module: used to retrieve historical usage records of consumables. After the consumables are used, they are used by various departments under the service organization. The module extracts the user and department corresponding to the usage record and judges and extracts the target record in the usage record based on the user's permissions and department. Obtain the storage environment information from the instruction manual of the consumables, and extract the reference value range of various physical characteristics from the storage environment information; deploy sensors in the service facility, and record the location of the corresponding consumables after they are picked up and the sensor location according to the target, so as to obtain the target value of the consumables in each physical characteristic at each moment. Target characteristic analysis module: used to obtain the monitoring period of physical characteristics based on the target value and the reference value range of various physical characteristics; obtain the monitoring area in the consumables; analyze the number of bacteria per unit in the monitoring area during the monitoring period; obtain the fitting function corresponding to each physical characteristic of the consumables; and obtain the target characteristics of the consumables based on the fitting function. The coding warning module is used to extract historical date windows based on the current date, obtain the recent weather conditions of the target city corresponding to the consumable, as well as the weather conditions within the date window, and obtain the current characteristic duration of the consumable based on the change in the number of bacteria per unit in the monitoring area of ​​the consumable within the date window. Obtain the current traceability code assignment time on the consumable, get the warning time of the current traceability code, and assign a warning code to consumables after the warning time.

[0012] Preferably, the target numerical calculation module includes a target record extraction unit and a target numerical calculation unit; Target record extraction unit: used to extract the user and department corresponding to a certain retrieval record. If the user has the authority to retrieve consumables and the department to which the user belongs is the same as the department to which a certain retrieval record is retrieved, then the certain retrieval record is taken as the target record. Target value calculation unit: used to deploy several sensors within the service facility to monitor various physical characteristics; capture the location of consumables at each moment after they are used, and obtain the target value of the consumables in each physical characteristic at each moment based on the sensor location and type.

[0013] Preferably, the coding warning module includes a feature duration calculation unit and a coding warning unit; Feature duration calculation unit: used to obtain the current weather conditions of the target city and the weather conditions of the date window, obtain the marked window, and obtain the current feature duration of the consumables based on the number of bacteria per unit in the monitoring area; Code assignment warning unit: used to obtain the coding time of the current traceability code on the consumable, obtain the warning time of the current traceability code, and provide coding warning prompts for consumables after the warning time.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides an artificial intelligence-based consumable monitoring data analysis system and method, including: retrieving historical usage records of consumables; providing used consumables to various departments under the service organization; judging and extracting target records; obtaining reference value ranges for various physical characteristics of consumables; deploying various sensors to obtain target values ​​for each physical characteristic of the consumables at each moment, thus obtaining the target characteristics of the consumables; extracting historical date windows and target cities; obtaining the characteristic duration of consumables based on the weather conditions of the target cities; thereby obtaining the warning time of the current traceability code; and assigning codes to consumables after the warning time for warning prompts. This invention, by analyzing historical usage records and various physical characteristics of consumables, assigns codes to produced consumables for warning prompts, effectively preventing the traceability codes from losing their timeliness and ensuring the reliability of consumable traceability data throughout its entire lifecycle. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the artificial intelligence-based consumable monitoring data analysis method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example: Figure 1 As shown, this invention provides a technical solution for consumable monitoring data analysis based on artificial intelligence, including the following steps: (1) Retrieve historical usage records of consumables. After the consumables are used by various departments under the service organization, extract the users and departments corresponding to the usage records, and judge and extract the target records in the usage records according to the user's authority and department.

[0019] Retrieve historical consumption records of consumables. These records are filled out by personnel after consuming consumables according to departmental needs. Extract the personnel and department corresponding to a specific consumption record. If the personnel have consumable consumption authority and their department matches the department of a given consumption record, that record is used as the target record. Unreliable consumption records will lead to unreliable calculation and analysis results; therefore, analysis of the personnel and departments is necessary to obtain reliable target records.

[0020] (2) Obtain the storage environment information in the instruction manual of the consumables, and extract the reference value range of various physical characteristics in the storage environment information; deploy sensors in the service organization, and record the location of the consumables after they are picked up and the sensor location according to the target record, so as to obtain the target value of the consumables in each physical characteristic at each moment.

[0021] Several sensors are deployed within the service facility to monitor various physical characteristics, and the location of each sensor is obtained. The surveillance video of consumable M after its use is retrieved, corresponding to a specific target record R. The position of consumable M at each moment in the surveillance video is captured, and the position at a certain moment T is taken as P. T To obtain the positions of all sensors monitoring a certain type of physical characteristic X, if the position of a certain sensor is related to position P... T If the distance between them is less than a preset distance threshold, then the sensing value of a certain sensor at time T will be used as the target value of consumable M in terms of physical property X at time T. Consumables generally have a defined storage environment. For example, for production consumables and daily consumables, temperature and humidity are key factors to ensure their storage stability and prevent deterioration and damage. Each physical characteristic has a corresponding reference value range. When the physical characteristic deviates from the reference value range, it will accelerate the rate at which the consumables become unusable, leading to aging or damage and deterioration. In this solution, this is characterized by the number of bacteria per unit area in the monitoring area.

[0022] If there is no sensor location and location P T If the distance between them is less than a distance threshold, then the position P is obtained. T The distance between each sensor is calculated, and the reciprocal of each distance is taken as the characteristic distance of each corresponding sensor. All characteristic distances are added together to obtain the total characteristic distance. Each characteristic distance is divided by the total characteristic distance to obtain the numerical weight corresponding to each sensor. Then, based on the sensing value of each sensor at time T and the corresponding numerical weight, the target value of the consumable at time T in terms of physical characteristic X is obtained. Thus, the target value of the consumable at each time in terms of each physical characteristic is obtained.

[0023] Due to the sensor and position P TThe closer the distance between them, the closer the monitored value is to position P. T The sensor value is used to obtain the characteristic distance in this scheme. The larger the characteristic distance, the closer the sensor is to the position P. T The closer the distance between them, the greater the corresponding numerical weight. And the sum of all the numerical weights is 1. Then, the target value can be obtained based on the sensing value of each sensor and its corresponding numerical weight.

[0024] (3) Based on the target value and the reference value range of various physical characteristics, the monitoring period of the physical characteristics is obtained; the monitoring area in the consumable is obtained, the number of bacteria per unit in the monitoring area during the monitoring period is analyzed, the fitting function corresponding to each physical characteristic of the consumable is obtained, and the target characteristic of the consumable is obtained based on the fitting function.

[0025] Based on the reference value ranges of various physical properties of consumables, the time when the target value is outside the reference value range is designated as the marked time; the time period when the number of marked times for a certain physical property X exceeds a preset threshold, and the number of marked times for all other physical properties is 0, is designated as the monitoring period D for physical property X. X ; The area on the consumable that comes into contact with the ROI area is designated as the monitoring area; the monitoring area is obtained during the monitoring period D. X The number of bacteria per unit at several time points is used to fit a linear function using the least squares method, resulting in a fitting function for the change of the number of bacteria per unit over time in terms of the physical characteristic X of the consumable. The fitting function for each physical characteristic is then obtained, and the physical characteristic with the largest slope is selected as the target characteristic of the consumable. The ROI (Region of Interest) refers to the area in contact with the consumable during use; it can be a part of the human body or a part of an object, depending on the specific circumstances.

[0026] (4) Based on the current date, extract the historical date window, obtain the recent weather conditions of the target city corresponding to the consumable, as well as the weather conditions within the date window, and obtain the current characteristic duration of the consumable based on the change in the number of bacteria per unit in the monitoring area of ​​the consumable within the date window.

[0027] Extract dates from the past year that share the same month and day as the current date, and extract a date window consisting of several adjacent days centered on that date; since the cities where consumables are produced and sold are the same, use that city as the target city and obtain the weather conditions of the target city within the date window; Based on the recent weather conditions of the target city, the actual values ​​of the target city in various physical characteristics are obtained, the average values ​​of various physical characteristics are obtained recently, and based on the weather conditions within the date window, the average values ​​of various physical characteristics within each historical date window are calculated. The date windows in which the difference between the average value of the target characteristic and the corresponding recent average value is less than a preset first difference threshold, and the difference between the average values ​​of other physical characteristics and the corresponding recent average values ​​is less than a preset second difference threshold, are designated as the marked windows. Get a consumable that has been produced within the marked window. Starting from the time of production completion, extract several detection times and perform bacterial detection on the monitoring area of ​​the consumable to obtain the number of bacteria per unit in the monitoring area at each detection time. The earliest detection time in which the number of bacteria per unit in the monitored area exceeds the preset bacterial count threshold is defined as T2. The number of bacteria per unit at detection time T2 is defined as N2, and the bacterial count threshold is defined as N0. The number of bacteria per unit at the previous detection time T1 is defined as N1. According to the formula... The target time T0 is obtained, and the duration between the target time T0 and the completion time of a certain consumable is used as the current characteristic duration CD of the consumable.

[0028] (5) Obtain the current traceability code assignment time on the consumable, obtain the warning time of the current traceability code, and assign a code warning prompt to the consumable after the warning time.

[0029] The earliest time the current traceability code is assigned to the consumable is taken as T1. The moment when the duration equals the feature duration CD after the assignment time T1 is taken as the warning moment of the current traceability code.

[0030] The coding time is the actual time when the consumable is completed and coded. In the current traceability system, there are situations where the same traceability code is used for the same batch of consumables. For example, the earliest coding time of a certain traceability code is the 1st, and then all consumables produced on the 1st, 2nd, 3rd, etc., use the same traceability code. However, due to the time-sensitive nature of consumables, such as a feature duration CD of 8 days, if consumables produced after the 8th still use the traceability code of the 1st, the reliability and reference value of the traceability code will gradually decrease. Therefore, it is necessary to issue a coding warning and recode consumables produced after the 8th to avoid the traceability code losing its timeliness and to ensure the reliability of the traceability data of consumables throughout the entire life cycle.

[0031] This embodiment also provides an artificial intelligence-based consumable monitoring data analysis system, including a target value calculation module, a target characteristic analysis module, and a coding warning prompt module. The target value calculation module includes a target record extraction unit and a target value calculation unit, and the coding warning prompt module includes a feature duration calculation unit and a coding warning prompt unit. When the system executes the computer program, it implements the above-mentioned artificial intelligence-based consumable monitoring data analysis method. Since the artificial intelligence-based consumable monitoring data analysis method has been described in detail above, it will not be repeated here.

[0032] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0033] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0034] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data analysis method for consumable monitoring based on artificial intelligence, characterized in that, Includes the following steps: Retrieve historical usage records of consumables. Consumables used after usage are supplied to various departments under the service organization. Extract the user and department corresponding to the usage record. Based on the user's authority and department, determine and extract the target record in the usage record. Obtain the storage environment information from the instruction manual of the consumables, and extract the reference value range of various physical characteristics from the storage environment information; deploy sensors in the service facility, and record the location of the corresponding consumables after they are picked up and the sensor location according to the target, so as to obtain the target value of the consumables in each physical characteristic at each moment. Based on the target value and the reference value range of various physical characteristics, the monitoring period of the physical characteristics is obtained; the monitoring area in the consumable is obtained, the number of bacteria per unit in the monitoring area during the monitoring period is analyzed, the fitting function corresponding to each physical characteristic of the consumable is obtained, and the target characteristic of the consumable is obtained based on the fitting function. Based on the current date, extract the historical date window to obtain the recent weather conditions of the target city corresponding to the consumable, as well as the weather conditions within the date window. Based on the change in the number of bacteria per unit in the monitoring area of ​​the consumable within the date window, obtain the current characteristic duration of the consumable. Obtain the current traceability code assignment time on the consumable, get the warning time of the current traceability code, and assign a warning code to consumables after the warning time. Among them, the target characteristics of the consumables are obtained, including: Based on the reference value ranges of various physical properties of consumables, the time when the target value is outside the reference value range is designated as the marked time; the time period when the number of marked times for a certain physical property X exceeds a preset threshold, and the number of marked times for all other physical properties is 0, is designated as the monitoring period D for physical property X. X ; The area on the consumable that comes into contact with the ROI area is designated as the monitoring area; the monitoring area is obtained during the monitoring period D. X The number of bacteria per unit at several time points is fitted using the least squares method to obtain a fitting function for the change of the number of bacteria per unit over time in terms of physical characteristic X of the consumable. The fitting function for each physical characteristic is obtained, and the physical characteristic with the largest slope is taken as the target characteristic of the consumable. The current characteristic duration of the consumables is obtained, including: Extract dates from the past year that share the same month and day as the current date, and use those dates as the center to extract a date window consisting of several adjacent days before and after them; since the cities where consumables are produced and sold are the same, use those cities as the target cities and obtain the weather conditions of the target cities within the date window; Based on the recent weather conditions of the target city, the actual values ​​of the target city in various physical characteristics are obtained, the average values ​​of various physical characteristics are obtained recently, and based on the weather conditions within the date window, the average values ​​of various physical characteristics within each historical date window are calculated. The date windows in which the difference between the average value of the target characteristic and the corresponding recent average value is less than a preset first difference threshold, and the difference between the average values ​​of other physical characteristics and the corresponding recent average values ​​is less than a preset second difference threshold, are designated as marked windows. A consumable that has completed production within a marked window is obtained. Starting from the time of production completion, several detection times are extracted. Bacterial detection is performed on the monitoring area of ​​the consumable to obtain the number of bacteria per unit in the monitoring area at each detection time. The earliest detection time in which the number of bacteria per unit in the monitored area exceeds the preset bacterial count threshold is defined as T2. The number of bacteria per unit at detection time T2 is defined as N2, and the bacterial count threshold is defined as N0. The number of bacteria per unit at the previous detection time T1 is defined as N1. According to the formula... The target time T0 is obtained, and the duration between the target time T0 and the completion time of the production of a certain consumable is used as the current characteristic duration CD of the consumable.

2. The consumable monitoring data analysis method based on artificial intelligence according to claim 1, characterized in that, The target record in the retrieval record is determined and extracted, including: retrieving the historical retrieval record of consumables, which is the record in which the user fills in the relevant information after retrieving consumables according to the department's needs; extracting the user and department corresponding to a certain retrieval record. If the user has the authority to retrieve consumables and the user's department is consistent with the department of the retrieval record, then the retrieval record is taken as the target record.

3. The consumable monitoring data analysis method based on artificial intelligence according to claim 2, characterized in that, Obtain the target values ​​of the consumables at each moment in terms of each physical characteristic, including: Several sensors are deployed within the service facility to monitor various physical characteristics, and the location of each sensor is obtained. The surveillance video of consumable M after its use is retrieved, corresponding to a specific target record R. The position of consumable M at each moment in the surveillance video is captured, and the position at a certain moment T is taken as P. T To obtain the positions of all sensors monitoring a certain type of physical characteristic X, if the position of a certain sensor is related to position P... T If the distance between them is less than a preset distance threshold, then the sensing value of a certain sensor at time T will be used as the target value of consumable M in terms of physical property X at time T. If there is no sensor location and location P T If the distance between them is less than a distance threshold, then the position P is obtained. T The distance between each sensor is calculated, and the reciprocal of each distance is taken as the characteristic distance of each corresponding sensor. All characteristic distances are added together to obtain the total characteristic distance. Each characteristic distance is divided by the total characteristic distance to obtain the numerical weight corresponding to each sensor. Then, based on the sensing value of each sensor at time T and the corresponding numerical weight, the target value of the consumable at time T in terms of physical characteristic X is obtained. Thus, the target value of the consumable at each time in terms of each physical characteristic is obtained.

4. The consumable monitoring data analysis method based on artificial intelligence according to claim 1, characterized in that, The steps to obtain the warning time of the current traceability code include: taking the earliest coding time of the current traceability code on the consumable as T3, and taking the time after the earliest coding time T3, where the duration is equal to the feature duration CD, as the warning time of the current traceability code.

5. A consumables monitoring data analysis system, used to execute the artificial intelligence-based consumables monitoring data analysis method according to any one of claims 1-4, characterized in that, The system includes a target numerical calculation module, a target characteristic analysis module, and a coding early warning module; Target value calculation module: used to retrieve historical usage records of consumables. After the consumables are used, they are used by various departments under the service organization. The module extracts the user and department corresponding to the usage record and judges and extracts the target record in the usage record based on the user's permissions and department. Obtain the storage environment information from the instruction manual of the consumables, and extract the reference value range of various physical characteristics from the storage environment information; deploy sensors in the service facility, and record the location of the corresponding consumables after they are picked up and the sensor location according to the target, so as to obtain the target value of the consumables in each physical characteristic at each moment. Target characteristic analysis module: used to obtain the monitoring period of physical characteristics based on the target value and the reference value range of various physical characteristics; obtain the monitoring area in the consumables; analyze the number of bacteria per unit in the monitoring area during the monitoring period; obtain the fitting function corresponding to each physical characteristic of the consumables; and obtain the target characteristics of the consumables based on the fitting function. The coding warning module is used to extract historical date windows based on the current date, obtain the recent weather conditions of the target city corresponding to the consumable, as well as the weather conditions within the date window, and obtain the current characteristic duration of the consumable based on the change in the number of bacteria per unit in the monitoring area of ​​the consumable within the date window. Obtain the current traceability code assignment time on the consumable, get the warning time of the current traceability code, and assign a warning code to consumables after the warning time.

6. The consumables monitoring data analysis system according to claim 5, characterized in that, The target numerical calculation module includes a target record extraction unit and a target numerical calculation unit; Target record extraction unit: used to extract the user and department corresponding to a certain retrieval record. If the user has the authority to retrieve consumables and the department to which the user belongs is the same as the department to which the retrieval record is retrieved, then the certain retrieval record is taken as the target record. Target value calculation unit: used to deploy several sensors within the service facility to monitor various physical characteristics; capture the location of consumables at each moment after they are used, and obtain the target value of the consumables in each physical characteristic at each moment based on the sensor location and type.

7. The consumables monitoring data analysis system according to claim 5, characterized in that, The coding warning module includes a feature duration calculation unit and a coding warning unit; Feature duration calculation unit: used to obtain the current weather conditions of the target city and the weather conditions of the date window, obtain the marked window, and obtain the current feature duration of the consumables based on the number of bacteria per unit in the monitoring area; Code assignment warning unit: used to obtain the coding time of the current traceability code on the consumable, obtain the warning time of the current traceability code, and provide coding warning prompts for consumables after the warning time.

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