Application of artificial intelligence technology in the transformation of test reagent benefits

Abstract

The present invention belongs to the field of test reagent management technology, and specifically relates to the application of artificial intelligence technology in the conversion of test reagent benefits. The invention can monitor the use status of test reagents in real time by assigning consumable codes to test reagents, thereby realizing effective management and tracing of reagent information. During the use of test reagents, the fluctuation of reagents can be monitored and analyzed in real time, thereby tracing back to the cause of abnormal fluctuation of test reagents, providing corresponding data support for subsequent correction and optimization. At the same time, for abnormal states that occur, corresponding traceability processing can be performed, and refined to upper and lower limit abnormalities, so that the direction of abnormalities can be clarified, which is convenient for management personnel to investigate. It can also count the abnormal causes during abnormal periods and generate abnormal reports, which are helpful for management personnel to promptly discover and solve problems that arise during the inspection process and improve the inspection quality of subsequent test reagents.

Description

Technical Field

[0001] The present invention belongs to the technical field of test reagent management, and specifically relates to the application of artificial intelligence technology in the efficiency conversion of test reagents. Background Art

[0002] With the continuous development of science and technology, artificial intelligence (AI) technology has achieved remarkable results in various fields. Especially in the medical field, the application of AI technology has made breakthrough progress in diagnosis, treatment, rehabilitation and other aspects. Test reagents play a pivotal role in the medical field. They can be used to detect pathogens, biomarkers, gene mutations, etc. in patients, thus providing important diagnostic basis for clinicians.

[0003] At present, there is a lack of methods and means to manage the test reagents during the use and consumption stage, which has formed a blind spot in supervision. During the use of reagents, quality control fluids, etc. will be used, and there will be corresponding loss ratios. At present, the hospital laboratory department has no method or feasible measures to accurately count the reagent loss ratios. Most of them are based on experience. The reasonable use of test reagents cannot be evaluated and supervised in time, resulting in some abnormal phenomena that cannot be discovered in time. For example, reagent consumables that can be used for 100 tests were actually used for 200 tests, which is obviously inconsistent with reality. Based on this, this solution provides an application of artificial intelligence technology in the transformation of test reagent benefits to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide the application of artificial intelligence technology in the benefit conversion of test reagents, which can effectively supervise the usage status of test reagents, discover abnormal phenomena in time, and improve the benefit conversion effect of test reagents.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The application of artificial intelligence technology in the conversion of test reagent benefits includes the following steps:

[0007] Add consumable codes to the test reagents, wherein the consumable codes corresponding to each test reagent are unique, and scan the consumable codes to record the storage information of the test reagents;

[0008] Counting the charge report catalog of the test reagents and their corresponding test items, wherein each of the test items corresponds to a number of test reagents;

[0009] When the test item is executed, the consumables code corresponding to the test reagent is scanned to obtain the test reagent, and the number of testers and charging information of the test reagent taken are recorded;

[0010] Determine an effective charging rate of the test reagent according to the charging information, and output a usage status of the test reagent based on the effective charging rate, wherein the usage status includes a normal state and an abnormal state;

[0011] Under the normal state, the fluctuation of the test reagent at different application time nodes is traced and calibrated as condition parameters, and a calibration report is output according to the condition parameters;

[0012] Under the abnormal state, the conversion rate of the test reagent is traced back, and based on the conversion rate, the abnormal time period is traced back, and then the abnormal causes in the abnormal time period are counted, and an abnormal report is generated.

[0013] In a preferred embodiment, the step of adding consumable codes to the test reagents includes:

[0014] Obtaining the types of all the test reagents, and classifying and processing the test reagents according to the types of the test reagents;

[0015] Record the basic information of each test reagent, wherein the basic information includes the number of testers, test fees, specifications, and storage time, and generate a unique consumables code based on the basic information;

[0016] The consumable codes are added to the classified test reagents one by one.

[0017] In a preferred embodiment, when taking the test reagent, the remaining amount of the test reagent of the same type is collected, and the corresponding test reagent is taken in order from large to small;

[0018] After the test reagent is used up, the used portion is collected in real time, and the remaining portion is recorded as the remaining amount of the test reagent.

[0019] In a preferred embodiment, the step of determining the effective charging rate of the test reagent according to the charging information includes:

[0020] Establishing a monitoring period, collecting the number of charge reports of the test reagents within the monitoring period, and calibrating the number of charge reports as a first reference parameter;

[0021] Obtaining the number of chargeable reports of the test reagent during the monitoring period, and calibrating it as a second reference parameter;

[0022] A standard function is obtained, and the first reference parameter and the second reference parameter are input into the standard function together, and the output result is calibrated as the effective rate of the test reagent.

[0023] In a preferred embodiment, the step of outputting the usage status of the test reagent based on the effective charging rate includes:

[0024] Obtaining an evaluation interval, and comparing the effective charging rate with the evaluation interval;

[0025] If the effective charging rate is within the evaluation interval, outputting the usage status of the test reagent as a normal status;

[0026] If the effective charging rate is outside the evaluation interval, outputting the usage status of the test reagent as an abnormal status;

[0027] Among them, the abnormal state includes upper limit abnormality and lower limit abnormality. When the effective charging rate exceeds the upper limit value of the evaluation interval, it is an upper limit abnormality, and when the effective charging rate is lower than the lower limit value of the evaluation interval, it is a lower limit abnormality.

[0028] In a preferred embodiment, the step of tracing the fluctuation of the test reagent at different application time nodes and marking it as a condition parameter includes:

[0029] Constructing a timeline, and mapping the charging information and usage information of the test reagent based on the timeline;

[0030] On the time axis after the mapping process, a number of time nodes are divided according to preset time intervals;

[0031] The fluctuations of the charging information and the usage information of the test reagents at adjacent time nodes are calculated and recorded as conditional parameters.

[0032] In a preferred solution, after the condition parameter is output, the allowable fluctuation threshold is obtained, and the allowable fluctuation threshold is subtracted from the condition parameter to obtain a fluctuation deviation, wherein the fluctuation deviation includes a positive deviation and a negative deviation;

[0033] Obtaining the time node under the fluctuation deviation and marking it as a risk node, and collecting the equipment execution parameters under the risk node and marking them as parameters to be evaluated;

[0034] Calculating the correlation coefficients between the parameter to be evaluated and the corresponding positive deviation and negative deviation, respectively, and recording them as the parameter to be evaluated;

[0035] Obtaining an evaluation interval, and comparing the evaluation interval with the parameter to be evaluated;

[0036] If the parameter to be evaluated belongs to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is related to the parameter to be evaluated, and a monitoring interval is constructed based on the parameter to be evaluated with the largest value and the parameter to be evaluated with the smallest value. Next time when the equipment execution parameter is within the monitoring interval, a correction report is generated based on the positive deviation and the negative deviation;

[0037] If the parameter to be evaluated does not belong to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is not related to the parameter to be evaluated, and a warning signal of non-device execution abnormality is simultaneously issued.

[0038] In a preferred embodiment, the step of tracing the conversion rate of the test reagent comprises:

[0039] Obtaining the actual consumption of the test reagents during the monitoring period, and the number of test subjects corresponding to the test items performed;

[0040] Get the conversion rate calculation function;

[0041] The reagent consumption of the test reagent, the number of test persons and the number of charging reports corresponding to the test items are input into the conversion rate calculation function, and the output result is calibrated as the conversion rate of the test reagent.

[0042] In a preferred embodiment, the step of tracing the abnormal period includes:

[0043] Obtaining all time nodes within the monitoring period, and dividing the conversion rate of the test reagent according to the time nodes to form a plurality of conversion rate intervals;

[0044] Calculate the mean of each conversion rate interval and use the mean as the benchmark conversion rate;

[0045] The base conversion rate is compared with a preset conversion rate threshold, and a conversion rate interval in which the base conversion rate is lower than the conversion rate threshold is marked as an abnormal period.

[0046] The present invention also provides an electronic device, the electronic device comprising:

[0047] at least one processor;

[0048] and a memory communicatively coupled to the at least one processor;

[0049] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned application of artificial intelligence technology in the conversion of test reagent benefits.

[0050] The technical effects achieved by the present invention are:

[0051] The present invention can monitor the usage status of the test reagents in real time by assigning consumable codes to the test reagents, thereby achieving effective management and traceability of the reagent information. During the use of the test reagents, the fluctuation of the reagents can be monitored and analyzed in real time, thereby tracing back to the cause of the abnormal fluctuation of the test reagents, providing corresponding data support for subsequent correction and optimization. At the same time, for the abnormal state that occurs, corresponding traceability processing can be performed and refined to upper limit abnormalities and lower limit abnormalities, thereby clarifying the direction of the abnormality and facilitating the management personnel to conduct investigations. The abnormal causes during the abnormal period can also be counted and an abnormality report can be generated, which helps the management personnel to promptly discover and solve problems that occur during the inspection process and improve the inspection quality of subsequent test reagents. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is an application flow chart of the test reagent benefit conversion in the present invention;

[0053] Figure 2 It is a structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] See also Figure 1 As shown, the present invention provides an application of artificial intelligence technology in the conversion of test reagent benefits, comprising the following steps:

[0058] S1. Add consumable codes to the test reagents, wherein the consumable codes corresponding to each test reagent are unique, and scan the consumable codes to record the storage information of the test reagents;

[0059] S2. A statistical report catalog of the charge for testing reagents and their corresponding test items, where each test item corresponds to a number of testing reagents;

[0060] S3. When the test item is executed, the consumables code corresponding to the test reagent is scanned to obtain the reagent, and the number of testers and charging information of the test reagent taken are recorded;

[0061] S4, determining the effective charging rate of the test reagent according to the charging information, and outputting the usage status of the test reagent based on the effective charging rate, wherein the usage status includes a normal state and an abnormal state;

[0062] Under normal conditions, the fluctuation of test reagents can be traced back to different application time nodes, and calibrated as conditional parameters, and the calibration report can be output based on the conditional parameters;

[0063] Under abnormal conditions, the conversion rate of the test reagents can be traced back, and based on the conversion rate, the abnormal period can be traced back, and then the abnormal causes during the abnormal period can be counted, and an abnormal report can be generated.

[0064] As described in the above steps S1-S4, in the medical industry, the usage status of test reagents is crucial for the diagnosis and treatment of diseases. In the management process of test reagents, the application of artificial intelligence makes it more and more convenient. In this embodiment, consumable codes are first added to the test reagents, and the consumable codes corresponding to each test reagent are unique. By scanning the consumable codes, the storage information of the test reagents can be recorded. In this way, the usage of the test reagents can be accurately tracked and managed. Next, it is necessary to count the charge report directories of the test reagents and the test items corresponding to these directories. In each test item, there are several test reagents corresponding. The purpose is to understand the usage and benefits of the test reagents in each project. When the test project is executed, the consumable codes corresponding to the test reagents can be scanned to obtain them. At the same time, it is also necessary to record the number of testers and charging information of the test reagents used. This information will provide a corresponding basis for subsequent benefit analysis. On this basis, it is necessary to determine the effective charging rate of the test reagents based on the charging information. According to the effective charging rate, the usage status of the test reagents can be output, including normal status and abnormal status. Under normal conditions, the fluctuations of the test reagents at different application time nodes can be traced and calibrated as conditional parameters. Based on these conditional parameters, a calibration report is output to optimize the use of the test reagents. Under abnormal conditions, the conversion rate of the test reagents can be traced, and the abnormal time period can be found out accordingly. Furthermore, the abnormal causes during the abnormal time period can be counted and an abnormal report can be generated. This helps managers to promptly discover and solve problems that arise during the inspection process and improve the inspection quality of subsequent test reagents.

[0065] In a preferred embodiment, the step of adding consumable codes to the test reagents includes:

[0066] S101, obtaining the types of all test reagents, and classifying the test reagents according to their types;

[0067] S102, recording basic information of each test reagent, wherein the basic information includes the number of testers, test fees, specifications, and storage time, and generating a unique consumables code based on the basic information;

[0068] S103. Add consumable codes to the classified test reagents one by one.

[0069] As described in the above steps S101-S103, before the test reagents are put into storage, the types of all test reagents are obtained and classified, and the basic information of each test reagent is recorded. This basic information includes the number of testers, test fees, specifications, and storage time, etc. The recording of this data helps to fully understand the use of the reagents, so as to better control the inventory and frequency of use of the reagents. At the same time, based on this basic information, a unique consumable code is generated for each reagent. The consumable code will become the "identity card" of the reagent, which is convenient for identifying the uniqueness of the reagent during subsequent use, query and tracking. Next, the consumable codes are added to the classified test reagents one by one. This step is to integrate the previous classification and basic information into the actual use of the reagents. By adding consumable codes to each reagent, it can ensure that the circulation and use of the reagents in the laboratory are more orderly, reducing the risk of incorrect use. Finally, the management of consumable codes needs to be checked and updated regularly to ensure the accuracy of the codes, thereby improving the effectiveness of laboratory test reagent management.

[0070] In a preferred embodiment, when taking the test reagent, the remaining amount of the same type of test reagent is collected, and the corresponding test reagent is taken in order from large to small;

[0071] After the test reagent is used up, the used doses are collected in real time, and the remaining doses are recorded as the remaining amount of the test reagent.

[0072] In this embodiment, when taking the test reagent, it is first necessary to collect the remaining amount of the same type of test reagent, so that the current inventory situation can be understood and data support can be provided for subsequent use. When collecting the remaining amount, the accuracy of the data should be ensured so as to more accurately reflect the actual situation. In addition, during the taking process, the reagents should be taken in order from large to small to ensure fair distribution of the reagents and avoid waste of resources. For example, if the remaining amount of a certain type of test reagent A is 5 and 3 respectively, reagent A should be used first when taking it to make full use of the existing resources. During the use of the test reagent, the used portions of the test reagent will also be collected in real time, and the remaining portions will be recorded as the remaining amount of the reagent for reference in the next round of use.

[0073] In a preferred embodiment, the step of determining the effective charging rate of the test reagent according to the charging information includes:

[0074] S401, constructing a monitoring period, collecting the number of charge reports of the test reagents within the monitoring period, and calibrating them as the first reference parameter;

[0075] S402, obtaining the number of chargeable reports of the test reagents during the monitoring period, and calibrating it as the second reference parameter;

[0076] S403, obtaining a standard function, and inputting the first reference parameter and the second reference parameter into the standard function, and calibrating the output result as the effective rate of the test reagent.

[0077] As described in the above steps S401-S403, in the practical use of test reagents, it is necessary to construct a monitoring period to monitor the charging information of the test reagents. The selection of the monitoring period should be based on the actual situation, which can be one day, one week, one month or longer. During this monitoring period, it is necessary to collect the number of charge reports of the test reagents, and after the collection is completed, these charge report numbers are calibrated as the first benchmark parameter, and then the number of chargeable reports of the test reagents during the monitoring period is obtained. These chargeable report numbers refer to all the charge records that should be generated in theory, including the actual charged and uncharged parts, and they are recorded as the second benchmark parameter, and then the standard function is obtained, where the expression of the standard function is: k = (S1 / S2) × 100%, where S1 represents the first benchmark parameter, and S2 represents the second benchmark parameter. The corresponding output result is calculated based on the input first benchmark parameter and second benchmark parameter, and this output result is calibrated as the effective rate of the test reagent.

[0078] In a preferred embodiment, the step of outputting the usage status of the test reagent based on the effective charging rate includes:

[0079] S404, obtaining an evaluation interval, and comparing the effective charging rate with the evaluation interval;

[0080] S405, if the effective charging rate is within the evaluation range, outputting the usage status of the test reagent as a normal status;

[0081] S406, if the effective charging rate is outside the evaluation range, outputting the usage status of the test reagent as an abnormal status;

[0082] Among them, abnormal conditions include upper limit abnormality and lower limit abnormality. When the effective charging rate exceeds the upper limit value of the evaluation interval, it is an upper limit abnormality; when the effective charging rate is lower than the lower limit value of the evaluation interval, it is a lower limit abnormality.

[0083] As described in the above steps S404-S406, after the effective charging rate of the test reagent is determined, it is necessary to call the corresponding evaluation interval, and then compare the effective charging rate with the evaluation interval to determine whether the effective charging rate is within a reasonable range. If the effective charging rate is within the evaluation interval, it means that the use status of the test reagent is normal, otherwise, the use status is abnormal. When the effective charging rate is outside the evaluation interval, it is necessary to further determine whether it is an upper limit abnormality or a lower limit abnormality. The upper limit abnormality means that the effective charging rate exceeds the upper limit value of the evaluation interval, and the lower limit abnormality means that the effective charging rate is lower than the lower limit value of the evaluation interval. Both abnormal states indicate that there are problems in the use of the test reagent, and the cause of the abnormality needs to be further analyzed;

[0084] In this implementation, the evaluation interval is preferably 50% to 100%. When the effective charging rate is greater than 100%, it is an upper limit abnormality and the cause must be found in time. It may be an erroneous detection report, or the free quality control solution is used as a normal reagent, etc., which causes the charges to be far greater than the consumption. A charging rate between 50% and 100% is normal, and long-term traceability can be carried out to find the cause and increase the charging rate. For cases where the charging rate is lower than 50%, the cause can be traced back to increase the charging rate and reduce losses.

[0085] In addition, it should be noted that for the analysis of the use of test reagents based on each person, the more statistical reports there are, the more accurate the actual number used. If only 1 to 2 bottles of reagents are used, there is no need to pay too much attention to the situation where the charging rate is lower than 50%, as it may be that the opened bottles of reagents have not been used up.

[0086] Based on the above analysis, the usage status of the test reagents is divided into normal status and abnormal status. When the effective charging rate is within the evaluation range, the usage status is normal status. When the effective charging rate exceeds the upper limit of the evaluation range or is lower than the lower limit of the evaluation range, the usage status is abnormal status.

[0087] In a preferred embodiment, the step of tracing the fluctuation of the test reagent at different application time nodes and marking it as a condition parameter includes:

[0088] Step 1: Build a timeline, and based on the timeline, map the charging information and usage information of the test reagents;

[0089] Step 2: On the time axis after mapping, divide several time nodes according to the preset time intervals;

[0090] Step 3: Calculate the fluctuation of the charging information and usage information of the test reagents at adjacent time nodes, and record them as conditional parameters.

[0091] As described in the above steps Step 1-Step 3, when tracing the fluctuation of the test reagent at different application time nodes, first construct a time axis, which can cover the entire time period from the start of use of the test reagent to the current time point, and then map the charging information and usage information of the test reagent based on this time axis. On the time axis after the mapping process is completed, it is necessary to divide the entire time period into several time nodes according to the preset time interval, and make a detailed characterization of the usage of the test reagent to facilitate the observation and analysis of its fluctuation at different time nodes. After the time nodes are divided, it is necessary to calculate the fluctuation of the charging information and usage information of the test reagent at adjacent time nodes, quantify the fluctuation of the test reagent at different time nodes, and record the calculated fluctuation as the conditional parameter.

[0092] In a preferred embodiment, after the conditional parameters are output, the allowable fluctuation threshold is obtained, and the allowable fluctuation threshold is subtracted from the conditional parameters to obtain a fluctuation deviation, wherein the fluctuation deviation includes a positive deviation and a negative deviation;

[0093] Obtain the time node under the fluctuation deviation and mark it as the risk node, collect the equipment execution parameters under the risk node and mark it as the parameters to be evaluated;

[0094] Calculate the correlation coefficients between the parameters to be evaluated and the corresponding positive deviations and negative deviations respectively, and record them as the parameters to be evaluated;

[0095] Obtain an evaluation interval, and compare the evaluation interval with the parameter to be evaluated;

[0096] If the parameter to be evaluated belongs to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is related to the parameter to be evaluated, and the monitoring interval is constructed based on the parameter to be evaluated with the largest value and the parameter to be evaluated with the smallest value. Next time when the equipment execution parameter is within the monitoring interval, a correction report is generated based on the positive deviation and the negative deviation.

[0097] If the parameter to be evaluated does not belong to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is not related to the parameter to be evaluated, and an alarm signal of non-device execution abnormality is simultaneously issued.

[0098] As mentioned above, after the conditional parameters are determined, an allowable fluctuation threshold value needs to be determined. By performing a difference process between the allowable fluctuation threshold value and the conditional parameters, the fluctuation deviation can be obtained. Here, the fluctuation deviation includes positive deviation and negative deviation. Next, the time nodes corresponding to the fluctuation deviation need to be determined, and these time nodes are marked as risk nodes. At the same time, the equipment execution parameters under these risk nodes are collected and marked as parameters to be evaluated. The correlation between the parameters to be evaluated and the positive deviation and negative deviation is the key to evaluating the operating status of the equipment. The correlation coefficient between them needs to be calculated and the results recorded. The calculation formula of the correlation coefficient is: In the formula, m represents the amount of fluctuation deviation, f j represents the parameter to be evaluated, u j Represents the fluctuation deviation (positive deviation or negative deviation), r represents the correlation coefficient, and then an evaluation interval is preset, and the parameter to be evaluated is compared with the evaluation interval. If the parameter to be evaluated falls within the evaluation interval, it means that its corresponding positive deviation or negative deviation is related to the equipment operation status. At this time, a monitoring interval can be constructed based on the parameter to be evaluated with the largest value and the parameter to be evaluated with the smallest value. When the equipment execution parameter is in this interval, a correction report can be generated based on the positive deviation and the negative deviation, and the equipment operation status can be adjusted in time to ensure the effective use of the test reagent. If the parameter to be evaluated does not fall within the evaluation interval, it means that its corresponding positive deviation or negative deviation is not related to the equipment operation status. At this time, an alarm signal of non-equipment execution abnormality needs to be issued to remind relevant personnel to pay attention to environmental factors and human operation factors, so as to improve the effective charging rate of test reagents.

[0099] In a preferred embodiment, the step of tracing the conversion rate of the reagent comprises:

[0100] Obtain the actual consumption of test reagents during the monitoring period, as well as the number of test subjects corresponding to the test items performed;

[0101] Get the conversion rate calculation function;

[0102] The reagent consumption of the test reagent, the number of test persons and the number of charging reports corresponding to the test items are input into the conversion rate calculation function, and the output result is calibrated as the conversion rate of the test reagent.

[0103] In this embodiment, when tracing the conversion rate of the test reagent, it is first necessary to obtain the actual consumption of the test reagent during the monitoring period and the number of test persons corresponding to the executed test items, which are specifically obtained through the application form and execution record of the test item. Then, it is necessary to obtain the conversion rate calculation function to reflect the utilization rate and economic benefit of the reagent, wherein the expression of the conversion rate calculation function is: In the formula, T represents the conversion rate of the test reagent, X represents the actual consumption of the test reagent, and R i Indicates the number of fee reports generated by the test project, C i represents the corresponding test portion of the test reagent, n represents the number of test items, and the reagent consumption of the test reagent, the corresponding test portions of the test items and the number of charging reports are input into the conversion rate calculation function to obtain the conversion rate of the test reagent.

[0104] In a preferred embodiment, the step of tracing the abnormal period includes:

[0105] Obtain all time nodes within the monitoring period, and divide the conversion rate of the test reagent according to the time nodes to form several conversion rate intervals;

[0106] Calculate the mean of each conversion rate range and use the mean as the benchmark conversion rate;

[0107] The benchmark conversion rate is compared with a preset conversion rate threshold, and a conversion rate interval in which the benchmark conversion rate is lower than the conversion rate threshold is marked as an abnormal period.

[0108] In this embodiment, under abnormal conditions, the conversion rate of the test reagent must not conform to the preset conversion rate range. First, all time nodes within the monitoring period are obtained and divided into several conversion rate intervals. Then, the mean of each conversion rate interval is calculated. This mean will be used as the benchmark conversion rate. The benchmark conversion rate is then compared with the preset conversion rate threshold to identify abnormal time periods where the conversion rate is lower than the threshold. Finally, these abnormal time periods can be marked out.

[0109] See also Figure 2 , an electronic device, the electronic device comprising:

[0110] at least one processor;

[0111] and a memory communicatively coupled to the at least one processor;

[0112] Among them, the memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned application of artificial intelligence technology in the conversion of test reagent benefits.

[0113] In the above electronic equipment,

[0114] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and other components in the electronic device may be controlled to perform desired functions, and the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include read-only memory (ROM), hard disk, flash memory, etc. Various data and / or programs may be stored in the above-mentioned memory for use by the processor to realize various functions required in the application process of artificial intelligence technology in the conversion of test reagent benefits.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0116] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for applying artificial intelligence technology to the efficiency conversion of test reagents, characterized in that: The following steps are involved: Add consumable codes to the test reagents, wherein the consumable codes corresponding to each test reagent are unique, and scan the consumable codes to record the storage information of the test reagents; Counting the charge report catalog of the test reagents and their corresponding test items, wherein each of the test items corresponds to a number of test reagents; When the test item is executed, the consumables code corresponding to the test reagent is scanned to obtain the test reagent, and the number of testers and charging information of the test reagent taken are recorded; Determine an effective charging rate of the test reagent according to the charging information, and output a usage status of the test reagent based on the effective charging rate, wherein the usage status includes a normal state and an abnormal state; Under the normal state, the fluctuation of the test reagent at different application time nodes is traced and calibrated as condition parameters, and a calibration report is output according to the condition parameters; Under the abnormal state, the conversion rate of the test reagent is traced back, and based on the conversion rate, the abnormal period is traced back, and then the abnormal causes in the abnormal period are counted, and an abnormal report is generated; Wherein, the step of outputting the usage status of the test reagent based on the effective charging rate includes: Obtaining an evaluation interval, and comparing the effective charging rate with the evaluation interval; If the effective charging rate is within the evaluation interval, outputting the usage status of the test reagent as a normal status; If the effective charging rate is outside the evaluation interval, outputting the usage status of the test reagent as an abnormal status; The abnormal state includes upper limit abnormality and lower limit abnormality. When the effective charging rate exceeds the upper limit value of the evaluation interval, it is an upper limit abnormality; when the effective charging rate is lower than the lower limit value of the evaluation interval, it is a lower limit abnormality. The step of tracing the fluctuation of the test reagent at different application time nodes and calibrating it as a condition parameter includes: Constructing a timeline, and mapping the charging information and usage information of the test reagent based on the timeline; On the time axis after the mapping process, a number of time nodes are divided according to preset time intervals; Calculate the fluctuation of the charging information and usage information of the test reagent at adjacent time nodes, and record them as condition parameters; After the condition parameter is output, the allowable fluctuation threshold is obtained, and the allowable fluctuation threshold is subtracted from the condition parameter to obtain a fluctuation deviation, wherein the fluctuation deviation includes a positive deviation and a negative deviation; Obtaining the time node under the fluctuation deviation and marking it as a risk node, and collecting the equipment execution parameters under the risk node and marking them as parameters to be evaluated; Calculating the correlation coefficients between the parameter to be evaluated and the corresponding positive deviation and negative deviation, respectively, and recording them as the parameter to be evaluated; Obtaining an evaluation interval, and comparing the evaluation interval with the parameter to be evaluated; If the parameter to be evaluated belongs to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is related to the parameter to be evaluated, and a monitoring interval is constructed based on the parameter to be evaluated with the largest value and the parameter to be evaluated with the smallest value. Next time when the equipment execution parameter is within the monitoring interval, a correction report is generated based on the positive deviation and the negative deviation; If the parameter to be evaluated does not belong to the evaluation interval, it indicates that the corresponding positive deviation or negative deviation is not related to the parameter to be evaluated, and a warning signal of non-device execution abnormality is simultaneously issued; The step of tracing the conversion rate of the test reagent comprises: Obtain the actual consumption of test reagents during the monitoring period, as well as the number of test subjects corresponding to the test items performed; Get the conversion rate calculation function; The reagent consumption of the test reagent, the number of test persons and the number of charging reports corresponding to the test items are input into the conversion rate calculation function, and the output result is calibrated as the conversion rate of the test reagent.

2. The method for applying artificial intelligence technology to the efficiency conversion of test reagents according to claim 1, characterized in that: The step of adding consumables coding to the test reagents comprises: Obtaining the types of all the test reagents, and classifying and processing the test reagents according to the types of the test reagents; Record the basic information of each test reagent, wherein the basic information includes the number of testers, test fees, specifications, and storage time, and generate a unique consumables code based on the basic information; The consumable codes are added to the classified test reagents one by one.

3. The method for applying artificial intelligence technology to the efficiency conversion of test reagents according to claim 1, characterized in that: When taking the test reagent, collect the remaining amount of the test reagent of the same type, and take the corresponding test reagent in order from large to small; After the test reagent is used up, the used portion is collected in real time, and the remaining portion is recorded as the remaining amount of the test reagent.

4. The method for applying artificial intelligence technology to the efficiency conversion of test reagents according to claim 1, characterized in that: The step of determining the effective charging rate of the test reagent according to the charging information comprises: Establishing a monitoring period, collecting the number of charge reports of the test reagents within the monitoring period, and calibrating the number of charge reports as a first reference parameter; Obtaining the number of chargeable reports of the test reagent during the monitoring period, and calibrating it as a second reference parameter; A standard function is obtained, and the first reference parameter and the second reference parameter are input into the standard function together, and the output result is calibrated as the effective rate of the test reagent.

5. The method for applying artificial intelligence technology to the efficiency conversion of test reagents according to claim 1, characterized in that: The step of tracing the abnormal period includes: Obtaining all time nodes within the monitoring period, and dividing the conversion rate of the test reagent according to the time nodes to form a plurality of conversion rate intervals; Calculate the mean of each conversion rate interval and use the mean as the benchmark conversion rate; The base conversion rate is compared with a preset conversion rate threshold, and a conversion rate interval in which the base conversion rate is lower than the conversion rate threshold is marked as an abnormal period.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for applying the artificial intelligence technology in the conversion of test reagent benefits as described in any one of claims 1 to 5.

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