Blood detection sample storage management method and system and storage medium

Abstract

The invention belongs to the technical field of blood management, and discloses a blood detection sample storage management method and system and a storage medium, and the method comprises the steps: reading sample information, extracting storage environment information and storage equipment information of a blood detection sample based on sample use information, and when the environment information of the storage equipment accords with the storage environment information, storing the blood detection sample; a blood detection sample is placed in a storage device, an operation plan is generated, whether the storage device is abnormal or not is periodically judged, an early warning signal is sent when the storage device is abnormal, sample operation information is generated based on sample use information, an operation instruction is sent to a user terminal, and after response information is obtained, the sample operation information is sent to the user terminal. And when the second operation process information in the user operation record is consistent with the first operation process information, outputting a detection report. According to the technical scheme, the accuracy, the convenience and the intelligent level of sample management can be improved, and the safety of the sample and the reliability of a detection result can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of blood management, and in particular relates to a blood test sample storage management method, system and storage medium. Background Art

[0002] With the continuous development of medical technology, blood testing has become one of the important means of clinical diagnosis. The storage and management of blood samples involves multiple links such as sample reception, storage, operation, and testing. Errors in any link may cause damage to the sample or errors in the test results. Traditional methods of storing and managing blood test samples often rely on manual operations, and there are problems such as incomplete information records, inaccurate storage environment control, and non-standard operation procedures. With the development of information technology, especially the widespread application of the Internet of Things and big data technology, new technical means have been provided for the storage and management of blood test samples.

[0003] Similar prior art includes a Chinese patent application with publication number CN118651532A, which discloses a method, device, equipment management terminal and blood storage equipment for storing blood, obtains blood entry and exit records and blood temperature information in the blood storage equipment; determines the safety status of the blood based on the entry and exit records and temperature information. There is also a Chinese patent application with publication number CN117190622A, which discloses a blood sample storage device and method, which places a blood sample on a sample storage position; times the storage time of the waste liquid sample on the sample storage position, and determines whether the blood sample is still within the validity period; if not, triggers a waste liquid transfer operation, and transfers the blood sample that has exceeded the validity period to the waste sample bin.

[0004] The above-mentioned prior art manages blood samples based on entry and exit records, temperature information and storage time, which has limitations and low intelligence. Therefore, it is an urgent problem to provide a blood test sample storage management method, system and storage medium to improve the accuracy, convenience and intelligence level of sample management. Summary of the invention

[0005] In response to the above-mentioned technical problems, the present invention provides a blood test sample storage management method, system and storage medium.

[0006] In a first aspect, the present invention provides a method for storing and managing blood test samples, the method comprising the following steps:

[0007] Step 1: Read sample information from the electronic tag of the blood test sample, and use the time of reading the sample information as the receiving time, wherein the sample information includes sample identification, patient information and sample use information;

[0008] Step 2: extracting storage environment information and storage device information of the blood test sample based on the sample usage information;

[0009] Step 3, obtaining the first environment information of the storage device, determining whether the first environment information conforms to the storage environment information, if not, returning to step 2, if yes, placing the blood test sample in the storage device, and setting the sample operation time based on the sample usage information and the receiving time, and storing the sample information, the receiving time, the storage environment information, the storage device information and the sample operation time in the sample test database correspondingly;

[0010] Step 4: periodically obtain the status data of the storage device, and determine whether the storage device is abnormal based on the status data. If so, issue a warning signal;

[0011] Step 5: extract the first operation process information corresponding to the sample usage information, and store the sample operation information in a sample operation database, wherein the sample operation information includes sample information, storage device information, sample operation time and the first operation process information;

[0012] Step 6: Generate an operation instruction based on the sample information and the sample operation time, and send the operation instruction to the user terminal. After obtaining the response information, send the sample operation information to the user terminal;

[0013] Step 7, obtaining the operation record sent by the user, extracting the second operation process information from the operation record, and determining whether the second operation process information is consistent with the first operation process information. If so, outputting a test report; if not, proceeding to step 8;

[0014] Step 8: extract the difference information between the second operation process information and the first operation process information, determine whether the difference information has an impact on the test result, if not, output a test report, if yes, instruct the user to perform the operation again, and return to step 7.

[0015] Specifically, the state data is collected by a data collection device, and before step 4, the following steps are included:

[0016] Step 411: When the data acquisition device acquires the status value of the storage device, the data acquisition device sends the status value to the data aggregation device. When the data aggregation device acquires the status value, the data acquisition time is added to the status value, and the first identifier of the data acquisition device, the status value, and the data acquisition time are stored correspondingly.

[0017] Step 412: after a preset time period, the data aggregation device generates first transmission data based on the data collection period, the first identifier, the state value and the data collection time of all the data collection devices within the preset time period, and sends the first transmission data to the data forwarding device, wherein the first transmission data also includes the second identifier of the data aggregation device and the data transmission time when the first transmission data is sent;

[0018] Step 413: After acquiring the first transmitted data, the data forwarding device acquires the network time, and adds the network time and the third identifier of the data forwarding device to the first transmitted data to generate the second transmitted data, and then sends the second transmitted data to the data management platform;

[0019] Step 414: After receiving the second sent data, the data management platform calculates the time difference between the data sending time and the network time, and determines whether the time difference is greater than or equal to the time threshold. If not, the state data is generated based on the first identifier, the state value and the data collection time. If so, the process proceeds to step 415.

[0020] Step 415: adjust the data collection time based on the time difference, and then generate status data based on the first identifier, the status value and the adjusted data collection time.

[0021] Specifically, in step 412, generating the first transmission data based on the data collection period, the first identifier, the state value and the data collection time of all data collection devices within the preset time period includes:

[0022] Step 4121: extract any data acquisition device, obtain the data acquisition period, the first identifier, and the state value and data acquisition time within a preset time period corresponding to any data acquisition device;

[0023] Step 4122, defining the data collection time of the first state value collected by any data collection device within the preset time period as the first collection time;

[0024] Step 4123, calculating the amount of data collected by any data collection device within a preset time period, and then generating a portion of the sent data corresponding to any data collection device based on the first identifier, the data collection period, the amount of data, the first collection time, and all state values ​​corresponding to any data collection device within the preset time period;

[0025] Step 4124: After traversing all data acquisition devices, generate first transmission data based on all partial transmission data.

[0026] Specifically, the status data is collected by the data collection device, and different data collection frequencies are set for different data collection devices before step 4, including:

[0027] Step 421: extract any data acquisition device, obtain N measurement data of any data acquisition device, assign a number to each measurement data, and assign a status identifier to each measurement data;

[0028] Step 422: after traversing all data acquisition devices, generate a training data set based on all measurement data and numbers, obtain a first state identifier of any training data in the training data set, then perform numerical processing on the first state identifier, and generate teacher data after traversing all training data;

[0029] Step 423: Perform machine learning on the teacher data to generate a first fault detection model, and obtain a weight coefficient for each training data;

[0030] Step 424, determining whether all weight coefficients corresponding to any data acquisition device are 0, if yes, defining any data acquisition device as a first-category data acquisition device, and reducing the data acquisition frequency of any data acquisition device according to a preset rule, if no, defining any data acquisition device as a second-category data acquisition device;

[0031] Step 425: store the first fault detection model, the device identification of the first type of data acquisition device, and the device identification of the second type of data acquisition device in the first storage module.

[0032] Specifically, the state identifier includes a first type identifier and a second type identifier, and step 422 includes:

[0033] Step 4221, extract the i-th measurement data of any data acquisition device, combine the i-th measurement data with the j-th measurement data, calculate the statistical value between the i-th measurement data and the j-th measurement data, if j<N, set j=j+1, and then return to step 4221, if j=N, generate a statistical value sequence corresponding to the i-th measurement data, and use the state identifier of the i-th measurement data as the sequence state identifier of the statistical value sequence, and use the number of the i-th measurement data as the sequence number of the statistical value sequence, wherein, i is a positive integer from 1 to N, and j is a positive integer from 1 to N;

[0034] Step 4222: after traversing all the measurement data corresponding to any data acquisition device, generate a statistical value matrix corresponding to any data acquisition device;

[0035] Step 4223: After traversing all data acquisition devices, all statistical value matrices are combined based on the sequence numbers to generate a training data set;

[0036] Step 4224: define a row of data in the training data set as one training data, extract any training data, and if all sequence state identifiers corresponding to any training data include the second type identifier, then set the first state identifier of any training data to the second type identifier.

[0037] Specifically, when the preset conditions are met, a second fault detection model is generated, and then the accuracies of the first fault detection model and the second fault detection model are calculated respectively. The fault detection model corresponding to the maximum accuracy is used as the fault detection model to be analyzed, and the first type of data acquisition device and the second type of data acquisition device are reset based on the fault detection model to be analyzed.

[0038] Specifically, after the user obtains the sample operation information, the blood test sample is extracted from the storage device and tested. Before the test, the following steps are included:

[0039] Step 61, reading sample information from the electronic tag of the extracted blood test sample, and defining the time of reading the sample information as the first time;

[0040] Step 62: Obtaining the effective time of the blood test sample based on the sample information;

[0041] Step 63, calculate the sum of the receiving time and the effective time, define it as the second time, and determine whether the second time is less than or equal to the first time. If so, send the instruction information that can be verified; if not, send the instruction information that has exceeded the validity period.

[0042] In a second aspect, the present invention further provides a blood test sample storage management system, the system comprising: a data management platform, a storage device and a user terminal, the data management platform comprising a data acquisition module, an information extraction module, a first judgment module, an equipment monitoring module, an operation guidance module, an instruction generation module, a second judgment module and a third judgment module;

[0043] A data acquisition module is used to read sample information from the electronic tag of the blood test sample and use the time of reading the sample information as the receiving time, wherein the sample information includes the sample identification, patient information and sample use information;

[0044] An information extraction module, used to extract storage environment information and storage device information of the blood test sample according to the sample usage information;

[0045] A first judgment module is used to obtain first environmental information of a storage device, judge whether the first environmental information conforms to the storage environmental information, and if not, return to the information extraction module, and if so, place the blood test sample in the storage device, and set the sample operation time based on the sample use information and the receiving time, and store the sample information, the receiving time, the storage environment information, the storage device information and the sample operation time in the sample test database correspondingly;

[0046] The device monitoring module is used to periodically obtain the status data of the storage device and determine whether the storage device is abnormal based on the status data. If so, an early warning signal is issued;

[0047] An operation guidance module, used to extract first operation process information corresponding to the sample usage information, and store the sample operation information in a sample operation database, wherein the sample operation information includes sample information, storage device information, sample operation time and first operation process information;

[0048] An instruction generation module is used to generate an operation instruction according to the sample information and the sample operation time, and send the operation instruction to the user terminal, and after obtaining the response information, send the sample operation information to the user terminal;

[0049] A second judgment module is used to obtain the operation record sent by the user, extract the second operation process information from the operation record, and judge whether the second operation process information is consistent with the first operation process information. If so, output a detection report; if not, enter the third judgment module;

[0050] The third judgment module is used to extract the difference information between the second operation process information and the first operation process information, and judge whether the difference information has an impact on the detection result. If not, the detection report is output; if so, the user is instructed to perform the operation again and return to the second judgment module.

[0051] In a third aspect, the present invention provides a computer storage medium storing program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute any of the above-mentioned blood test sample storage management methods.

[0052] The present invention discloses a blood test sample storage management method, system and storage medium, and the beneficial effects are at least as follows:

[0053] 1. Read sample information through electronic tags and automatically extract storage environment information. By accurately obtaining the environmental information of the storage device and comparing it with the preset storage environment information, ensure that the sample is stored under appropriate storage conditions. Set the sample operation time according to the purpose and receipt time of the sample to ensure that the sample is tested at the best time, thereby improving the reliability of shelf life management.

[0054] 2. By extracting the first operation process information corresponding to the sample usage information, and sending the sample information, storage device information, sample operation time and the first operation process information to the user terminal before the user starts the operation, the operation process of blood samples is standardized, the management intelligence level and detection accuracy are improved, and human errors can be reduced.

[0055] 3. Periodically obtain the status data of the storage device, and determine whether there is any abnormality in the storage device based on this status data. When the status data of the storage device is abnormal, the system can issue an early warning signal in time to reduce sample loss, enhance the monitoring capability of the storage environment, and realize the automation of blood test sample storage management.

[0056] 4. By using technical means such as machine learning, the data collection frequency can be automatically adjusted according to the status data of the data collection equipment, which not only saves resources but also ensures the adequacy of data, further improving the level of intelligent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0058] Figure 1 It is a flow chart of the blood test sample storage management method of the present invention;

[0059] Figure 2 It is a modular schematic diagram of the blood test sample storage management system of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0061] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0062] Figure 1 FIG. 1 is a flow chart of an embodiment of a method for storing and managing blood test samples provided by the present invention, and the flow chart specifically includes the following steps:

[0063] Step 1: Read sample information from the electronic tag of the blood test sample, and use the time of reading the sample information as the receiving time, wherein the sample information includes sample identification, patient information and sample use information.

[0064] When receiving the blood test sample, the electronic label is attached to the blood test sample, and the automatic labeling equipment is selected according to the size, shape and material of the sample container.

[0065] Blood test samples include whole blood, plasma, serum, etc. Sample usage information includes test purposes (blood routine, biochemical testing, infectious disease screening, etc.), research purposes (epidemiological research, genetic research, etc.), etc.

[0066] Step 2: Extract the storage environment information and storage device information of the blood test sample based on the sample usage information.

[0067] Storage environment information includes temperature, humidity, etc. Different blood test samples have different storage environment requirements, which correspond to different storage devices. You need to select storage devices based on the storage environment requirements of the blood test samples. For example, whole blood needs to be stored at a temperature of 4°C ± 2°C; plasma needs to be stored in a low-temperature cold storage, and the temperature is generally ≤-18°C; serum should be stored at a low temperature of minus 20°C, and can be stored using refrigerators, dry freezers, liquid nitrogen tanks and other equipment.

[0068] There may be multiple storage devices suitable for storing blood test samples. When storing blood test samples, the system can automatically push the storage device, or the storage device can be specified by the administrator.

[0069] Step 3, obtain the first environmental information of the storage device, and determine whether the first environmental information is consistent with the storage environment information. If not, return to step 2. If so, place the blood test sample in the storage device, and set the sample operation time based on the sample usage information and the receiving time, and store the sample information, receiving time, storage environment information, storage device information and sample operation time in the sample detection database accordingly.

[0070] When obtaining the storage device information, the first environment information of the storage device can be obtained. If the first environment information does not meet the storage environment information, return to the previous step to re-acquire the storage device information that can store the blood test sample. By ensuring that the environmental conditions of the storage device meet the storage requirements of the sample, the quality of the sample before testing can be guaranteed, and damage or deterioration of the sample caused by improper environment can be avoided.

[0071] The sample operation time can be a time period of optimal operation time or the earliest time when the inspection work can start. The sample operation time is set according to the order, priority and receiving time of the inspection task corresponding to the sample usage information. By automatically judging and setting the operation time, manual intervention is reduced and the efficiency of sample processing is improved.

[0072] Step 4: Periodically obtain status data of the storage device, and determine whether the storage device is abnormal based on the status data. If so, issue a warning signal.

[0073] After the blood test samples are stored in the storage device, the operating status of the storage device is monitored periodically. The parameters corresponding to the storage environment information can be monitored, and when the environmental information of the storage device exceeds the preset range, an early warning message is issued; the overall performance of the storage device can also be monitored, and the collected status data can be analyzed to determine whether the storage device is operating normally. If the storage device is abnormal, an early warning message is issued. By monitoring the status of the storage device, environmental changes that may cause sample damage can be discovered in a timely manner, thereby protecting the sample from loss. At the same time, it helps to discover potential failures of the storage device in advance, avoid sample loss due to equipment failure, and improve the safety and reliability of blood test sample storage management.

[0074] Specifically, the state data is collected by a data collection device, and before step 4, the following steps are included:

[0075] Step 411, when the data acquisition device obtains the status value of the storage device, it sends the status value to the data aggregation device. When the data aggregation device obtains the status value, it adds the data acquisition time to the status value and stores the first identifier of the data acquisition device, the status value, and the data acquisition time correspondingly.

[0076] Step 412: After a preset time period, the data aggregation device generates first transmission data based on the data collection period, the first identifier, the status value and the data collection time of all data collection devices within the preset time period, and sends the first transmission data to the data forwarding device, wherein the first transmission data also includes the second identifier of the data aggregation device and the data transmission time when the first transmission data is sent.

[0077] Step 413: After acquiring the first transmitted data, the data forwarding device acquires the network time, and adds the network time and the third identifier of the data forwarding device to the first transmitted data to generate the second transmitted data, and then sends the second transmitted data to the data management platform.

[0078] Step 414: After receiving the second sent data, the data management platform calculates the time difference between the data sending time and the network time, and determines whether the time difference is greater than or equal to the time threshold. If not, status data is generated based on the first identifier, status value and data collection time. If so, proceed to step 415.

[0079] Step 415: adjust the data collection time based on the time difference, and then generate status data based on the first identifier, the status value and the adjusted data collection time.

[0080] The preset time period and time threshold are set according to the experience of technical personnel in this field or according to the actual application scenario, and the embodiments of the present application are not limited to this.

[0081] A data aggregation device can be connected to multiple data acquisition devices. For example, all data acquisition devices on a storage device can be connected to the same data aggregation device or to different data aggregation devices. The data acquisition device can be a separate device or a data acquisition module integrated in the above-mentioned data aggregation device.

[0082] When the data aggregation device obtains the state value measured by any data acquisition device, it adds the data acquisition time to the state value, and the data acquisition time is generated by the internal electronic clock of the data aggregation device.

[0083] After the data forwarding device obtains the first transmission data sent by the data aggregation device, it obtains the accurate network time from the external time server, generates the second transmission data based on the network time and the first transmission data, and then sends the second transmission data to the outside. The data aggregation device and the data forwarding device are located in the same local network, and the communication between the two is real-time. The data transmission is almost instantaneous. Under normal circumstances, the data transmission time and network time should be consistent, or there is a very small time difference.

[0084] The internal electronic clock of the data aggregation device uses a crystal oscillator to keep time. Although the crystal oscillator is relatively stable, it will have frequency drift due to factors such as temperature changes, aging, and manufacturing tolerances, resulting in cumulative errors in time measurement, causing the data collection time to gradually deviate from the actual time. Different data aggregation devices will generate different data collection times for the state values ​​collected by different data collection devices at the same time. To ensure the accuracy and consistency of time data, the data collection time needs to be adjusted based on the time difference between the data sending time and the network time.

[0085] Specifically, in step 412, generating the first transmission data based on the data collection period, the first identifier, the state value and the data collection time of all data collection devices within the preset time period includes:

[0086] Step 4121: extract any data acquisition device, obtain the data acquisition period, the first identifier, and the state value and data acquisition time within a preset time period corresponding to any data acquisition device.

[0087] Step 4122: define the data collection time of the first state value collected by any data collection device within a preset time period as the first collection time.

[0088] Step 4123, calculate the amount of data collected by any data acquisition device within the preset time period, and then generate partial sending data corresponding to any data acquisition device based on the first identifier, data acquisition period, data amount, first acquisition time and all status values ​​corresponding to any data acquisition device within the preset time period.

[0089] Step 4124: After traversing all data acquisition devices, generate first transmission data based on all partial transmission data.

[0090] When the data collection cycle and the first collection time are known, the data collection time of other status data can be calculated based on the data collection cycle and the first collection time. Therefore, when sending the status data of any data collection device, only the first identifier, data collection cycle, data volume, first collection time and all status values ​​within the preset time period are sent, which can reduce the amount of data transmission and alleviate communication pressure.

[0091] After receiving the second transmitted data, the data management platform calculates the data collection time corresponding to each state value, and when the time difference is greater than or equal to the time threshold, adjusts the data collection time corresponding to each state value based on the time difference.

[0092] Specifically, the status data is collected by the data collection device, and different data collection frequencies are set for different data collection devices before step 4, including:

[0093] Step 421: extract any data acquisition device, obtain N measurement data of any data acquisition device, assign a number to each measurement data, and assign a status identifier to each measurement data.

[0094] Step 422: After traversing all data acquisition devices, generate a training data set based on all measurement data and numbers, obtain the first state identifier of any training data in the training data set, and then digitize the first state identifier, and generate teacher data after traversing all training data.

[0095] Step 423: Perform machine learning on the teacher data to generate a first fault detection model, and obtain the weight coefficient of each training data.

[0096] Step 424, determine whether all weight coefficients corresponding to any data acquisition device are 0. If so, define any data acquisition device as a first-category data acquisition device, and reduce the data acquisition frequency of any data acquisition device according to preset rules. If not, define any data acquisition device as a second-category data acquisition device.

[0097] Step 425: store the first fault detection model, the device identification of the first type of data acquisition device, and the device identification of the second type of data acquisition device in the first storage module.

[0098] Preferably, the measurement data with the same number from different data acquisition devices have the same acquisition time. When assigning a status identifier to each measurement data, the status identifier of the measurement data is manually determined.

[0099] Exemplarily, the above preset rule is to set the data collection frequency of the first type of data collection equipment to half of that of the second type of data collection equipment, or to set the data collection frequency of the first type of data collection equipment to 0.

[0100] Specifically, the state identifier includes a first type identifier and a second type identifier, and step 422 includes:

[0101] Step 4221, extract the i-th measurement data of any data acquisition device, combine the i-th measurement data with the j-th measurement data, calculate the statistical value between the i-th measurement data and the j-th measurement data, if j<N, then make j=j+1, and then return to step 4221, if j=N, then generate a statistical value sequence corresponding to the i-th measurement data, and use the state identifier of the i-th measurement data as the sequence state identifier of the statistical value sequence, and use the number of the i-th measurement data as the sequence number of the statistical value sequence, wherein, i is a positive integer from 1 to N, and j is a positive integer from 1 to N.

[0102] Step 4222: After traversing all measurement data corresponding to any data acquisition device, generate a statistical value matrix corresponding to any data acquisition device.

[0103] Step 4223: After traversing all data acquisition devices, all statistical value matrices are combined based on the sequence numbers to generate a training data set.

[0104] Step 4224: define a row of data in the training data set as one training data, extract any training data, and if all sequence state identifiers corresponding to any training data include the second type identifier, then set the first state identifier of any training data to the second type identifier.

[0105] Preferably, the first category is marked as normal, and the second category is marked as abnormal. The first category is numerically converted to 1, and the second category is numerically converted to 0.

[0106] Exemplarily, the above statistical value is the sum, difference or average value of two measurement data, and when i and j are equal, the statistical value of the two measurement data is 0.

[0107] For example, there are two data acquisition devices (data acquisition device A and data acquisition device B), and each data acquisition device acquires three measurement data. The measurement data of data acquisition device A are combined in pairs to generate a 3×3 statistical value matrix. The value at the first row and the first column is the statistical value between the first measurement data and the first measurement data, the value at the first row and the second column is the statistical value between the first measurement data and the second measurement data, ..., the value at the third row and the second column is the statistical value between the third measurement data and the second measurement data, and the value at the third row and the third column is the statistical value between the third measurement data and the third measurement data. By combining the statistical value matrices of data acquisition device A and data acquisition device B based on the serial number, a 3×6 matrix can be generated, which is the training data set, and each row corresponds to a training data, wherein the value at the 1st row and the 1st column is the data corresponding to data acquisition device A, ..., the value at the 1st row and the 4th column is the data corresponding to data acquisition device B, the value at the 1st row and the 6th column is the data corresponding to data acquisition device B, ..., the value at the 3rd row and the 1st column is the data corresponding to data acquisition device A, ..., the value at the 3rd row and the 4th column is the data corresponding to data acquisition device B, and the value at the 3rd row and the 6th column is the data corresponding to data acquisition device B.

[0108] The training data and the first state identifier corresponding to each training data are subjected to machine learning to generate a first fault detection model, and the model output result is that the storage device is normal or the storage device is abnormal. The machine learning algorithm includes logistic regression, decision tree, random forest, support vector machine, neural network, etc.

[0109] By using statistical values ​​to generate training data sets, it is possible to achieve dimensionality reduction of measurement data, reduce model complexity and computational requirements, reduce the impact of noise on measurement data, improve model accuracy and robustness, and promote the fusion of different measurement data, provide a more comprehensive description of storage device status, and improve the performance of fault detection models.

[0110] All weight coefficients corresponding to any data acquisition device are 0, indicating that the data acquisition device does not contribute to the fault detection model, and the data of the data acquisition device is not needed, so the data acquisition frequency of the data acquisition device can be reduced.

[0111] According to the technical solution of the present invention, the workload of manually setting up the operation of data acquisition equipment can be reduced. It is not necessary for all data acquisition equipment to be in an active state. Only necessary data acquisition equipment is operated, which reduces energy consumption and communication costs and improves the overall performance of the system.

[0112] Specifically, when the preset conditions are met, a second fault detection model is generated, and then the accuracies of the first fault detection model and the second fault detection model are calculated respectively. The fault detection model corresponding to the maximum accuracy is used as the fault detection model to be analyzed, and the first type of data acquisition device and the second type of data acquisition device are reset based on the fault detection model to be analyzed.

[0113] Exemplarily, the preset conditions are that after a preset time interval, the number of data acquisition devices corresponding to the storage device decreases or increases, the accuracy of the first fault detection model decreases, and the like.

[0114] According to steps 421 to 423 , a second fault detection model is generated, and according to step 424 , the first type of data acquisition device and the second type of data acquisition device are reset.

[0115] Step 5: extract the first operation process information corresponding to the sample usage information, and store the sample operation information in a sample operation database, wherein the sample operation information includes sample information, storage device information, sample operation time and the first operation process information.

[0116] The first operation process information is extracted at a preset time before the sample operation time. The first operation process information is the specific steps and process of the sample detection, and is the standard operation process information when testing the blood sample.

[0117] Step 6: Generate an operation instruction based on the sample information and the sample operation time, and send the operation instruction to the user terminal. After obtaining the response information, send the sample operation information to the user terminal.

[0118] Through the operation instructions generated by the system, users can be reminded to test blood samples in a timely manner.

[0119] The user responds to the operation instruction. Before conducting a blood test sample test, the sample operation information is sent to the user, indicating the blood test sample to be tested, sample usage information, and the location where the blood test sample is stored, etc., which can reduce errors caused by human factors; the first operation process information is sent to the user to ensure the standardization and consistency of the sample testing operation, ensure that the sample is processed according to the correct operation process, reduce human errors, and at the same time, speed up the sample processing and improve the efficiency, accuracy and safety of blood test sample processing.

[0120] Specifically, after the user obtains the sample operation information, the blood test sample is extracted from the storage device and tested. Before the test, the following steps are included:

[0121] Step 61: Read sample information from the electronic tag of the extracted blood test sample, and define the time of reading the sample information as the first time.

[0122] Step 62: Obtain the effective time of the blood test sample based on the sample information.

[0123] Step 63, calculate the sum of the receiving time and the effective time, define it as the second time, and determine whether the second time is less than or equal to the first time. If so, send the instruction information that can be verified; if not, send the instruction information that has exceeded the validity period.

[0124] Each blood test sample has a different effective storage period. Testing beyond the effective period will result in inaccurate test results. Testing the validity of the sample before testing can improve the reliability of the test results and the efficiency of the testing process, help optimize the management of blood test samples, reduce errors and waste of resources, and improve the quality of medical services.

[0125] Step 7, obtain the operation record sent by the user, extract the second operation process information from the operation record, and determine whether the second operation process information is consistent with the first operation process information. If so, output the detection report; if not, proceed to step 8.

[0126] After the user obtains the sample operation information, the blood test sample is extracted from the storage device and the test operation is performed. At the same time, the test operation steps and test results are recorded to generate an operation record. The operation record also includes the user identification information and test time of the user performing the test operation.

[0127] By extracting the second operation process information and comparing the consistency of the second operation process information with the first operation process information, it is determined whether the user has performed the sample inspection in accordance with the established operation process, thereby ensuring the compliance and accuracy of the operation and improving the reliability of the test results. It is possible to avoid test result deviations caused by operational errors and reduce the output of erroneous results.

[0128] Preferably, after obtaining the operation record sent by the user, the user identifier is extracted from the operation record to further determine whether the user corresponding to the user identifier is qualified to perform the blood sample test. If not, a reminder is given that the test is invalid and the test needs to be repeated.

[0129] When the user does not have the corresponding ability to perform the inspection operation, he will not be able to perform the operation. Difficult-to-find errors may occur during the operation, resulting in inaccurate test results.

[0130] Step 8: extract the difference information between the second operation process information and the first operation process information, determine whether the difference information has an impact on the test result, if not, output a test report, if yes, instruct the user to perform the operation again, and return to step 7.

[0131] Extract the difference between the second operation process information and the first operation process information, and evaluate the specific impact of these differences on the test results. If the difference information has no impact on the test results, the test report can be output. If it has an impact, the user is instructed to re-operate to ensure that all operations are performed in accordance with the standard process. The impact of the deviation of the operation process on the test results is judged with high flexibility and adaptability, which can reduce unnecessary sample retesting and save resources and time.

[0132] Figure 2 FIG. 1 is a schematic diagram of the structure of an embodiment of the blood test sample storage management system provided by the present invention. Figure 2 As shown, the system includes: a data management platform 20, a storage device 30 and a user terminal 10, and the data management platform 20 includes a data acquisition module 201, an information extraction module 202, a first judgment module 203, an equipment monitoring module 204, an operation guidance module 205, an instruction generation module 206, a second judgment module 207 and a third judgment module 208.

[0133] The data acquisition module 201 is used to read the sample information from the electronic tag of the blood test sample and use the time of reading the sample information as the receiving time, wherein the sample information includes the sample identification, patient information and sample use information.

[0134] The information extraction module 202 is used to extract the storage environment information and storage device information of the blood test sample according to the sample usage information.

[0135] The first judgment module 203 is used to obtain the first environmental information of the storage device 30, and judge whether the first environmental information conforms to the storage environment information. If not, it returns to the information extraction module 202. If so, the blood test sample is placed in the storage device 30, and the sample operation time is set based on the sample usage information and the receiving time, and the sample information, receiving time, storage environment information, storage device information and sample operation time are stored in the sample detection database correspondingly.

[0136] The device monitoring module 204 is used to periodically obtain the status data of the storage device, and determine whether the storage device is abnormal based on the status data. If so, an early warning signal is issued.

[0137] The operation guidance module 205 is used to extract the first operation process information corresponding to the sample usage information, and store the sample operation information in the sample operation database, wherein the sample operation information includes sample information, storage device information, sample operation time and the first operation process information.

[0138] The instruction generation module 206 is used to generate an operation instruction according to the sample information and the sample operation time, and send the operation instruction to the user terminal 10 , and after obtaining the response information, send the sample operation information to the user terminal 10 .

[0139] The second judgment module 207 is used to obtain the operation record sent by the user, extract the second operation process information from the operation record, and determine whether the second operation process information is consistent with the first operation process information. If so, output a detection report; if not, enter the third judgment module 208.

[0140] The third judgment module 208 is used to extract the difference information between the second operation process information and the first operation process information, and judge whether the difference information has an impact on the detection result. If not, a detection report is output. If so, the user is instructed to perform the operation again and the second judgment module 207 is returned.

[0141] According to another aspect of an embodiment of the present invention, a computer storage medium is provided, wherein the computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute any of the above-mentioned blood test sample storage management methods.

[0142] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0144] The above embodiments only express the preferred implementation mode of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for storing and managing blood test samples, characterized in that: The steps include: Step 1: Reading sample information from the electronic tag of the blood test sample, and using the time of reading the sample information as the receiving time, wherein the sample information includes the sample identification, patient information and sample use information; Step 2: extracting storage environment information and storage device information of the blood test sample based on the sample usage information; Step 3, obtaining the first environment information of the storage device, determining whether the first environment information conforms to the storage environment information, if not, returning to step 2, if yes, placing the blood test sample in the storage device, and setting the sample operation time based on the sample usage information and the receiving time, and then storing the sample information, the receiving time, the storage environment information, the storage device information and the sample operation time in the sample test database correspondingly; Step 4: periodically obtain the status data of the storage device, and determine whether the storage device is abnormal based on the status data. If so, issue a warning signal; Step 5: extracting the first operation process information corresponding to the sample usage information, and storing the sample operation information in a sample operation database, wherein the sample operation information includes the sample information, the storage device information, the sample operation time and the first operation process information; Step 6: Generate an operation instruction based on the sample information and the sample operation time, and send the operation instruction to the user terminal. After obtaining response information, send the sample operation information to the user terminal; Step 7, obtaining the operation record sent by the user, extracting the second operation process information from the operation record, and determining whether the second operation process information is consistent with the first operation process information. If so, outputting a test report; if not, proceeding to step 8; Step 8: extract the difference information between the second operation process information and the first operation process information, and determine whether the difference information has an impact on the test result. If not, output the test report. If so, instruct the user to perform the operation again and return to step 7.

2. The method according to claim 1, characterized in that The state data is collected by a data collection device, and before step 4, the following steps are included: Step 411, when the data acquisition device acquires the status value of the storage device, the data acquisition device sends the status value to the data aggregation device, and when the data aggregation device acquires the status value, the data acquisition time is added to the status value, and the first identifier of the data acquisition device, the status value, and the data acquisition time are stored correspondingly; Step 412: after a preset time period, the data aggregation device generates first transmission data based on the data collection period of all data collection devices within the preset time period, the first identifier, the state value and the data collection time, and sends the first transmission data to the data forwarding device, wherein the first transmission data also includes the second identifier of the data aggregation device and the data transmission time when the first transmission data is sent; Step 413: After acquiring the first transmission data, the data forwarding device acquires the network time, and adds the network time and the third identifier of the data forwarding device to the first transmission data to generate second transmission data, and then sends the second transmission data to the data management platform; Step 414: After receiving the second transmitted data, the data management platform calculates the time difference between the data transmission time and the network time, and determines whether the time difference is greater than or equal to the time threshold. If not, the status data is generated based on the first identifier, the status value and the data collection time. If so, proceed to step 415. Step 415: adjust the data collection time based on the time difference, and then generate the status data based on the first identifier, the status value and the adjusted data collection time.

3. The method according to claim 2, characterized in that In step 412, generating the first transmission data based on the data collection period of all data collection devices within the preset time period, the first identifier, the state value and the data collection time includes: Step 4121: extract any data acquisition device, obtain the data acquisition period, the first identifier, and the state value and the data acquisition time within the preset time period of any data acquisition device; Step 4122, defining the data collection time of the first state value collected by any data collection device within the preset time period as a first collection time; Step 4123, calculating the amount of data collected by any of the data collection devices within the preset time period, and then generating partial transmission data corresponding to any of the data collection devices based on the first identifier, the data collection period, the data amount, the first collection time and all state values ​​corresponding to any of the data collection devices within the preset time period; Step 4124: after traversing all the data acquisition devices, generate the first sending data based on all partial sending data.

4. The method according to claim 1, characterized in that: The state data is collected by a data collection device, and different data collection frequencies are set for different data collection devices before step 4, including: Step 421: extract any data acquisition device, obtain N measurement data of any data acquisition device, assign a number to each measurement data, and assign a status identifier to each measurement data; Step 422: after traversing all data acquisition devices, generate a training data set based on all measurement data and the number, obtain a first state identifier of any training data in the training data set, then perform numerical processing on the first state identifier, and generate teacher data after traversing all training data; Step 423: Perform machine learning on the teacher data to generate a first fault detection model, and obtain a weight coefficient for each training data; Step 424, determining whether all weight coefficients corresponding to any of the data acquisition devices are 0, if so, defining any of the data acquisition devices as a first-category data acquisition device, and reducing the data acquisition frequency of any of the data acquisition devices according to a preset rule, if not, defining any of the data acquisition devices as a second-category data acquisition device; Step 425: store the first fault detection model, the device identification of the first type of data acquisition device, and the device identification of the second type of data acquisition device in a first storage module.

5. The method according to claim 4, characterized in that The state identifier includes a first type of identifier and a second type of identifier, and step 422 includes: Step 4221, extract the i-th measurement data of any of the data acquisition devices, combine the i-th measurement data with the j-th measurement data, calculate the statistical value between the i-th measurement data and the j-th measurement data, if j<N, set j=j+1, and then return to the step 4221, if j=N, generate a statistical value sequence corresponding to the i-th measurement data, and use the state identifier of the i-th measurement data as the sequence state identifier of the statistical value sequence, and use the number of the i-th measurement data as the sequence number of the statistical value sequence, wherein i is a positive integer from 1 to N, and j is a positive integer from 1 to N; Step 4222: after traversing all the measurement data corresponding to any of the data acquisition devices, generate a statistical value matrix corresponding to any of the data acquisition devices; Step 4223: after traversing all the data acquisition devices, all statistical value matrices are combined based on the sequence numbers to generate the training data set; Step 4224: define a row of data in the training data set as one training data, extract any training data, and if all sequence state identifiers corresponding to any of the training data include the second type identifier, then set the first state identifier of any of the training data to the second type identifier.

6. The method according to claim 4, characterized in that When the preset conditions are met, a second fault detection model is generated, and then the accuracies of the first fault detection model and the second fault detection model are calculated respectively. The fault detection model corresponding to the maximum accuracy is used as the fault detection model to be analyzed, and the first type of data acquisition device and the second type of data acquisition device are reset based on the fault detection model to be analyzed.

7. The method according to claim 1, characterized in that After the user obtains the sample operation information, the blood test sample is extracted from the storage device and tested. Before the test, the following steps are included: Step 61, reading sample information from the electronic tag of the extracted blood test sample, and defining the time of reading the sample information as the first time; Step 62: acquiring the effective time of the blood test sample based on the sample information; Step 63, calculate the sum of the receiving time and the valid time, define it as the second time, and determine whether the second time is less than or equal to the first time. If so, send the instruction information that can be verified; if not, send the instruction information that has exceeded the validity period.

8. A blood test sample storage management system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: A data management platform, a storage device and a user terminal, wherein the data management platform includes a data acquisition module, an information extraction module, a first judgment module, an equipment monitoring module, an operation guidance module, an instruction generation module, a second judgment module and a third judgment module; The data acquisition module is used to read sample information from the electronic tag of the blood test sample and use the time of reading the sample information as the receiving time, wherein the sample information includes the sample identification, patient information and sample use information; The information extraction module is used to extract the storage environment information and storage device information of the blood test sample according to the sample usage information; The first judgment module is used to obtain the first environment information of the storage device, judge whether the first environment information conforms to the storage environment information, and if not, return to the information extraction module, and if so, place the blood test sample in the storage device, and set the sample operation time based on the sample use information and the receiving time, and then store the sample information, the receiving time, the storage environment information, the storage device information and the sample operation time in the sample detection database correspondingly; The device monitoring module is used to periodically obtain status data of the storage device, determine whether the storage device is abnormal based on the status data, and if so, issue a warning signal; The operation guidance module is used to extract the first operation process information corresponding to the sample usage information, and store the sample operation information in a sample operation database, wherein the sample operation information includes the sample information, the storage device information, the sample operation time and the first operation process information; The instruction generation module is used to generate an operation instruction according to the sample information and the sample operation time, and send the operation instruction to the user terminal, and after obtaining the response information, send the sample operation information to the user terminal; The second judgment module is used to obtain the operation record sent by the user, extract the second operation process information from the operation record, and judge whether the second operation process information is consistent with the first operation process information. If so, output a detection report; if not, enter the third judgment module; The third judgment module is used to extract the difference information between the second operation process information and the first operation process information, and judge whether the difference information has an impact on the detection result. If not, the detection report is output; if so, the user is instructed to perform the operation again and return to the second judgment module.

9. A computer storage medium, characterized in that: The computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

Images