Biological sample storage management decision control system based on multi-parameter monitoring

Abstract

The invention belongs to the technical field of biological sample storage supervision, and particularly relates to a biological sample storage management decision control system based on multi-parameter monitoring, which comprises a storage environment accurate sensing module, a storage state intelligent evaluation module, a self-adaptive adjustment control module, a storage transfer emergency output module and a storage management decision end, a standardized data set is output through a storage environment precise sensing module, and a risk value is quantified based on the standardized data set and through a two-dimensional index to precisely judge a storage state, so that a self-adaptive adjustment control module is driven to automatically eliminate risks; a basis is provided for the storage transfer emergency output module to ensure that high-value and high-risk samples are transferred in time, the overall management quality is controlled through the sample management abnormity analysis module, and the sample access supervision module focuses on the access link, so that the biological sample storage management is promoted to realize refined and intelligent upgrading, and the efficiency is improved. And the storage safety of the biological sample is comprehensively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of biological sample storage supervision technology, specifically a biological sample storage management decision control system based on multi-parameter monitoring. Background Technology

[0002] Biological samples (such as clinical tissues, cells, gene fragments, etc.) are core resources for medical diagnosis, drug development and life science research. The stability of environmental parameters and the maintenance of the sample's own activity during storage directly determine the sample's subsequent use value. With the large-scale development of biobanks, the traditional management model that relies on manual inspection and single-dimensional parameter monitoring is no longer suitable for the differentiated storage needs of different types of samples. Although the industry is gradually promoting intelligent monitoring technology.

[0003] For example, Chinese patent CN206399479U discloses a biological sample preservation environment detection system. This invention focuses on the field of biological sample preservation. It collects key physical parameters of the storage environment through temperature detection module, light detection module, and vibration detection module. Combined with fingerprint detection module, it realizes personnel access control. When the parameters exceed the safe range, it issues an alarm through voice module and indicator light to remind staff to manually adjust the environment.

[0004] While the above-mentioned technical solutions have solved the problem of lag in traditional manual inspections to some extent, in practical applications, they are not only unable to fully reflect the risks of real-time storage of biological samples, but also unable to accurately assess the urgency of sample transfer to ensure that high-value and high-risk samples are dispatched and disposed of in a timely manner. Furthermore, the lack of overall management and storage and retrieval supervision links makes it difficult to make targeted adjustments to the supervision plan, and it is impossible to upgrade from passive alarm to active prevention and control. It is also difficult to meet the long-term and refined management needs of large-scale sample banks. Therefore, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a decision control system for the storage management of biological samples based on multi-parameter monitoring, so as to solve the technical defects mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a biological sample storage management decision control system based on multi-parameter monitoring, comprising a storage environment precision sensing module, a storage status intelligent assessment module, an adaptive adjustment control module, a storage transfer urgency output module, and a storage management decision terminal; The storage environment precision sensing module comprehensively collects the physicochemical parameters of the biological sample storage environment and the sample's own characteristic parameters, forming a standardized monitoring parameter set for the corresponding biological sample and outputting it in real time. The intelligent storage status assessment module analyzes and determines the storage status of corresponding biological samples based on a standardized set of monitoring parameters. When the storage status is determined to be risky, the adaptive adjustment control module adaptively adjusts the storage environment of the corresponding biological samples. The storage transfer urgency output module analyzes the storage transfer urgency of the corresponding biological samples and generates a high-urgency transfer signal or a low-urgency transfer signal accordingly. The high-urgency transfer signal or the low-urgency transfer signal is then sent to the storage management decision-making terminal.

[0007] Furthermore, the specific analysis process of the storage status intelligent assessment module is as follows: A standardized monitoring parameter set is received, and an evaluation index system including environmental stability index E and sample activity decay index A is constructed. The storage state risk value calculation formula is set as follows: R=w1×(1-E)+w2×A; where w1 and w2 are preset weight values, and the sum of the weights satisfies w1+w2=1. The storage state risk value R is calculated by the formula, and R is compared with the preset storage state risk threshold Rmin. If R<Rmin, it is determined to be a safe storage state; if R≥Rmin, it is determined to be a risky storage state.

[0008] Furthermore, the sample activity decay index A = (Ao - At) / Ao, where Ao is the initial activity value of the corresponding biological sample, and At is the current activity value of the corresponding biological sample; the environmental stability index E is calculated as follows: E=[1-(|T-To| / To+|H-Ho| / Ho+|X-Xo| / Xo+|C-Co| / Co+V / Vmax) / 5], where T is the actual temperature of the sample storage environment, To is the optimal storage temperature of the sample, H is the actual humidity of the sample storage environment, and Ho is the optimal storage humidity of the sample. X represents the actual oxygen concentration in the sample storage environment, and Xo represents the optimal oxygen concentration for sample storage; C represents the actual carbon dioxide concentration in the sample storage environment, and Co represents the optimal carbon dioxide concentration for sample storage; V represents the actual vibration data of the sample storage environment, and Vmax represents the maximum allowable vibration value of the sample storage environment.

[0009] Furthermore, the specific analysis process of the storage transfer urgency output module is as follows: The sample priority database is accessed to extract the priority P of the corresponding biological sample. The sample storage feature value is obtained through analysis, and the sample storage feature value is multiplied by the corresponding priority P to obtain the storage transfer urgency coefficient. The storage transfer urgency coefficient is compared with a preset storage transfer urgency coefficient threshold. If the storage transfer urgency coefficient exceeds the preset storage transfer urgency coefficient threshold, a high-urgency transfer signal is generated; if the storage transfer urgency coefficient does not exceed the preset storage transfer urgency coefficient threshold, a low-urgency transfer signal is generated.

[0010] Furthermore, the specific methods for analyzing and obtaining the feature values ​​of the stored samples are as follows: The number of times the storage environment of the corresponding biological sample was identified as a storage risk state during the detection period was obtained and marked as the storage risk frequency. The average of all adjustment times during the detection period when the storage environment of the corresponding biological sample was adjusted from a storage risk state to a storage safe state was calculated to obtain the tuning hazard coefficient. The average of all storage state risk values ​​of the environment of the corresponding biological sample during the detection period was calculated to obtain the state characteristic coefficient. The sample storage characteristic value was obtained by weighted summation of the storage risk frequency, tuning hazard coefficient and state characteristic coefficient.

[0011] Furthermore, the storage management decision-making terminal communicates with the sample management anomaly analysis module. The sample management anomaly analysis module is used to set the management period, analyze the degree of management anomalies for all biological samples during the management period, generate a sample management qualified signal or a sample management early warning signal through analysis, and send the sample management qualified signal or sample management early warning signal to the storage management decision-making terminal. When the storage management decision-making terminal receives the sample management early warning signal, it issues a corresponding early warning.

[0012] Furthermore, the specific analysis process of the sample management anomaly analysis module is as follows: The actual storage time of the corresponding biological sample is obtained. If the actual storage time of the corresponding biological sample does not exceed the corresponding maximum allowed storage time, but is discarded due to storage factors, the corresponding biological sample is marked as an early discard sample. The number of early discard samples within the management period is obtained and marked as the early discard detection value. The early discard detection value is compared with the preset early discard detection threshold. If the early discard detection value exceeds the preset early discard detection threshold, a sample management early warning signal is generated.

[0013] Furthermore, if the early abandonment sample detection value does not exceed the preset early abandonment sample detection threshold, the difference between the maximum allowable storage time and the actual storage time of the corresponding early abandonment sample is calculated, and the ratio of the difference result to the corresponding maximum allowable storage time is calculated to obtain the allowable storage difference ratio coefficient. The allowable storage poor state value is obtained by averaging the allowable storage difference ratio coefficients of all early abandonment samples during the management period, and the total amount of loss involved in all early abandonment samples during the management period is marked as the early abandonment loss amount. The abnormal characteristic value of the sample tube is calculated by weighting and summing the early rejection detection value, early rejection loss value and allowable poor condition value. The abnormal characteristic value of the sample tube is compared with the preset abnormal characteristic threshold of the sample tube. If the abnormal characteristic value of the sample tube exceeds the preset abnormal characteristic threshold, a sample management early warning signal is generated; if the abnormal characteristic value of the sample tube does not exceed the preset abnormal characteristic threshold, a sample management qualified signal is generated.

[0014] Furthermore, the sample management anomaly analysis module is connected to the sample access supervision analysis module. The sample management anomaly analysis module sends the sample management qualified signal to the sample access supervision analysis module. When the sample access supervision analysis module receives the sample management qualified signal, it analyzes the performance of biological sample access supervision during the management period, generates an access supervision early warning signal or an access supervision qualified signal through analysis, and sends the access supervision early warning signal or access supervision qualified signal to the storage management decision end. When the storage management decision end receives the access supervision early warning signal, it issues a corresponding warning.

[0015] Furthermore, the specific analysis process of the sample access regulatory analysis module is as follows: The system obtains the number of storage device failures and repairs caused by access operations during the management period, and calculates the access-induced damage prevalence value by comparing it with the total number of access operations. It also obtains the number of biological sample damages caused by access operations during the management period, and calculates the access-induced abandonment prevalence value by comparing it with the total number of access operations. The access-induced damage prevalence value and the access-induced abandonment prevalence value are compared with the preset access-induced damage prevalence value and the preset access-induced abandonment prevalence value, respectively. If the access-induced damage prevalence value or the access-induced abandonment prevalence value exceeds the corresponding preset threshold, an access monitoring warning signal is generated. If the access failure rate and access abandonment rate do not exceed the corresponding preset thresholds, the proportion of the number of times the request sample identifier and the actual operation sample identifier do not match during the access process in the management period is obtained to the total number of accesses, and it is marked as the identifier matching anomaly rate; and the record information of each access operation is obtained, the ratio of the number of accesses with incomplete record elements in the management period to the total number of accesses is calculated to obtain the access record anomaly rate, and the ratio of the number of accesses with complete traceability in the management period to the total number of accesses is calculated, and the value 1 is subtracted from the ratio result to obtain the traceability non-traceability rate; The abnormal rate of identifier matching, abnormal rate of access record, and untraceable rate of traceability are compared with the preset thresholds of the abnormal rate of identifier matching, the abnormal rate of access record, and the untraceable rate of traceability. If the abnormal rate of identifier matching, the abnormal rate of access record, or the untraceable rate of traceability exceeds the corresponding preset threshold, an access supervision warning signal is generated. If the identification matching anomaly rate, access record anomaly rate, and traceability non-traceability rate do not exceed the corresponding preset threshold, then the ratio of the time consumed in a single access to the corresponding preset time threshold is calculated to obtain the single access time coefficient, and the average of all single access time coefficients within the management period is calculated to obtain the access efficiency value. The sample access control coefficient is calculated by weighting and summing the access loss rate, access abandonment rate, identifier matching anomaly rate, access record anomaly rate, traceability non-traceability rate, and access effectiveness anomaly value. The sample access control coefficient is then compared with a preset sample access control coefficient threshold. If the sample access control coefficient exceeds the preset sample access control coefficient threshold, an access control warning signal is generated; if the sample access control coefficient does not exceed the preset sample access control coefficient threshold, an access control compliance signal is generated.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, a standardized monitoring parameter set is output by the storage environment precision perception module. Based on the standardized dataset, the storage status risk value is quantified to accurately distinguish between safe and risky states. After identifying the risky state, the environment is automatically adjusted to quickly resolve storage hazards. Furthermore, the storage transfer urgency output module accurately classifies and assesses the transfer urgency to ensure that high-value and high-risk samples are scheduled and disposed of in a timely manner, avoiding sample loss due to transfer delays.

[0017] 2. In this invention, the sample management anomaly analysis module extends from single sample control to overall management optimization, promptly identifying potential loopholes in overall storage management and preventing problems from escalating. Furthermore, the sample access supervision analysis module further addresses shortcomings in the access process, focusing on the performance of access operation supervision after confirming overall management compliance, ensuring the safety, accuracy, and traceability of the sample access process, and strengthening access supervision in a timely manner to avoid sample loss or management chaos caused by improper access operations. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1As shown, the biological sample storage management decision control system based on multi-parameter monitoring proposed in this invention includes a storage environment precision sensing module, a storage status intelligent assessment module, an adaptive adjustment control module, a storage transfer urgency output module, and a storage management decision terminal; The storage environment precision sensing module comprehensively collects the physicochemical parameters (temperature, humidity, oxygen concentration, etc.) of the biological sample storage environment and the sample's own characteristic parameters (such as sample activity value, sample preset storage period, remaining storage time, sample priority, etc.), forming a standardized monitoring parameter set for the corresponding biological sample and outputting it in real time. This provides comprehensive and accurate raw data support for all subsequent analysis steps, ensuring the scientific nature and reliability of storage management decisions from the source.

[0021] The intelligent storage status assessment module, based on a standardized set of monitoring parameters, analyzes and determines the storage status of corresponding biological samples. This accurate assessment avoids the limitations of traditional single-parameter judgments, providing a clear basis for subsequent adjustment, control, and transfer decisions. Furthermore, when a storage risk state is identified, the adaptive adjustment control module adjusts the storage environment of the corresponding biological sample to quickly mitigate the risk, maintain suitable storage conditions, effectively reduce manual intervention costs, and ensure the viability of the biological sample. The specific analysis process is as follows: A standardized set of monitoring parameters is received, and an evaluation index system is constructed that includes the environmental stability index E and the sample activity decay index A. It should be noted that a higher value for the environmental stability index E indicates a worse real-time storage environment. The calculation method for the environmental stability index E is as follows: E=[1-(|T-To| / To+|H-Ho| / Ho+|X-Xo| / Xo+|C-Co| / Co+V / Vmax) / 5]; Where T is the actual temperature of the sample storage environment, and To is the optimal storage temperature of the sample; H represents the actual humidity value of the sample storage environment, and Ho represents the optimal humidity value for sample storage. X represents the actual oxygen concentration in the sample storage environment, Xo represents the optimal oxygen concentration for sample storage; C represents the actual carbon dioxide concentration in the sample storage environment, Co represents the optimal carbon dioxide concentration for sample storage; V represents the actual vibration data of the sample storage environment, Vmax represents the maximum allowable vibration value of the sample storage environment. Furthermore, the sample activity decay index is A = (Ao - At) / Ao; Where Ao is the initial activity value of the corresponding biological sample, and At is the current activity value of the corresponding biological sample. It should be noted that the larger the value of the sample activity decay index A, the worse the real-time status of the corresponding biological sample. The formula for calculating the storage status risk value is: R = w1 × (1 - E) + w2 × A; where w1 and w2 are preset weight values, and the sum of the weights satisfies w1 + w2 = 1; the storage status risk value R is obtained by calculating the formula. It should be noted that the larger the value of the storage status risk value R, the greater the real-time storage risk of the corresponding biological sample, and the more timely the storage environment adjustment is required. The storage status risk value R is compared with the preset storage status risk threshold Rmin. If R < Rmin, it indicates that the real-time storage risk of the corresponding biological sample is small and the current environmental condition can be maintained, and it is judged as a safe storage state. If R ≥ Rmin, it indicates that the real-time storage risk of the corresponding biological sample is large and timely storage environment adjustment is required, and it is judged as a storage risk state.

[0022] The storage transfer urgency output module analyzes the storage transfer urgency of corresponding biological samples and generates high-urgency or low-urgency transfer signals accordingly. These signals are then sent to the storage management decision-making end, ensuring that high-value, high-storage-risk biological samples are prioritized for disposal, preventing sample loss due to untimely transfer, and providing a clear scheduling direction for the storage management decision-making end. The specific analysis process of the storage transfer urgency output module is as follows: The system calls the sample priority database to extract the priority P of the corresponding biological sample. The P value ranges from 1 to 5 (1 being the lowest and 5 being the highest). Furthermore, P is determined comprehensively based on three dimensions: "urgent clinical application + scientific research value + sample scarcity". It is confirmed by manual annotation combined with automatic classification algorithm during system entry. For example, clinical emergency samples have P=5, rare scientific research samples have P=4, routine scientific research samples have P=3, and batch reserve samples have P=2. The number of times the storage environment of the corresponding biological sample was identified as a storage risk state during the detection period was obtained and marked as the storage risk frequency. The average of all adjustment times during the detection period when the storage environment of the corresponding biological sample was adjusted from the storage risk state to the storage safety state was calculated to obtain the adjustment risk coefficient. The average of all storage state risk values ​​of the environment of the corresponding biological sample during the detection period was calculated to obtain the state characteristic coefficient. The sample storage characteristic value is obtained by weighted summation of storage risk frequency, tuning risk coefficient, and state characteristic coefficient. Specifically, each of the storage risk frequency, tuning risk coefficient, and state characteristic coefficient is assigned a corresponding preset weight coefficient, and then each of these coefficients is multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the sample storage characteristic value. It should be noted that the larger the sample storage characteristic value, the worse the storage environment control of the corresponding biological sample during the detection period. The storage feature value of the sample is multiplied by the corresponding priority P to obtain the storage transfer urgency coefficient. The storage transfer urgency coefficient is then compared with a preset storage transfer urgency coefficient threshold. If the storage transfer urgency coefficient exceeds the preset threshold, it indicates that the overall storage control risks for the corresponding biological sample are relatively high during the detection period, and a high-urgency transfer signal is generated. If the storage transfer urgency coefficient does not exceed the preset threshold, it indicates that the overall storage control risks for the corresponding biological sample are relatively low during the detection period, and a low-urgency transfer signal is generated.

[0023] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that the storage management decision-making terminal is connected to the sample management anomaly analysis module. The sample management anomaly analysis module is used to set the management period, preferably 25 days; it analyzes the degree of management anomalies for all biological samples during the management period and generates a sample management qualified signal or a sample management early warning signal through analysis. Furthermore, the sample management pass signal or sample management early warning signal is sent to the storage management decision-making terminal. When the storage management decision-making terminal receives the sample management early warning signal, it issues a corresponding warning, which is conducive to comprehensively controlling the overall storage management quality, timely discovering potential management loopholes to avoid damage to batch samples, and realizing the extension from single sample control to overall management optimization. The specific analysis process of the sample management anomaly analysis module is as follows: The actual storage time of the corresponding biological sample is obtained. If the actual storage time of the corresponding biological sample does not exceed the maximum allowed storage time, but it is discarded due to storage factors (mainly due to poor control of the storage environment), the corresponding biological sample is marked as an early discard sample. The number of early discard samples during the management period is obtained and marked as the early discard detection value. The early discard detection value is compared with the preset early discard detection threshold. If the early discard detection value exceeds the preset early discard detection threshold, it indicates that the management performance of the biological sample storage process during the management period is poor, and a sample management early warning signal is generated.

[0024] Furthermore, if the early abandonment sample detection value does not exceed the preset early abandonment sample detection threshold, the difference between the maximum allowable storage time and the actual storage time of the corresponding early abandonment sample is calculated, and the ratio of the difference result to the corresponding maximum allowable storage time is calculated to obtain the allowable storage difference ratio coefficient. The allowable storage poor state value is obtained by averaging the allowable storage difference ratio coefficients of all early abandonment samples during the management period, and the total amount of loss involved in all early abandonment samples during the management period is marked as the early abandonment loss amount. The sample tube anomaly value is calculated by weighting and summing the early rejection detection value, early rejection loss value, and allowable poor condition value. Specifically, each of the three values ​​is assigned a corresponding preset weight coefficient, and then multiplied by its respective preset weight coefficient. The sum of these three products is then labeled as the sample tube anomaly value. It should be noted that the larger the sample tube anomaly value, the worse the overall management performance of the biological sample storage process during the management period. The abnormal characteristic value of the sample tube is compared with the preset abnormal characteristic threshold of the sample tube. If the abnormal characteristic value of the sample tube exceeds the preset abnormal characteristic threshold, it indicates that the overall management performance of the biological sample storage process during the management period is poor, and a sample management warning signal is generated. If the abnormal characteristic value of the sample tube does not exceed the preset abnormal characteristic threshold, it indicates that the overall management performance of the biological sample storage process during the management period is good, and a sample management qualified signal is generated.

[0025] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the sample management anomaly analysis module is connected to the sample access supervision analysis module. The sample management anomaly analysis module sends the sample management qualified signal to the sample access supervision analysis module. When the sample access supervision analysis module receives the sample management qualified signal, it analyzes the performance of biological sample access supervision during the management period and generates an access supervision early warning signal or an access supervision qualified signal through analysis. Furthermore, the system sends access control warning signals or access control compliance signals to the storage management decision-making terminal. Upon receiving the access control warning signal, the storage management decision-making terminal issues a corresponding warning. This allows for a focus on the access control process after confirming overall management compliance, facilitating timely addressing of access control shortcomings, ensuring the safety, accuracy, and traceability of the biological sample access process, avoiding sample loss or management chaos caused by improper access operations, and reducing the difficulty of biological sample management. The specific analysis process of the sample access control analysis module is as follows: The system obtains the number of storage device failures and repairs caused by access operations during the management period, and calculates the access-induced damage prevalence value by comparing it with the total number of access operations. It also obtains the number of biological samples damaged by access operations during the management period, and calculates the access-induced abandonment prevalence value by comparing it with the total number of access operations. The access-induced damage prevalence value and the access-induced abandonment prevalence value are compared with the preset access-induced damage prevalence value and the preset access-induced abandonment prevalence value, respectively. If the access-induced damage prevalence value or the access-induced abandonment prevalence value exceeds the corresponding preset threshold, it indicates that the access management performance for biological samples is poor during the management period, and an access monitoring warning signal is generated. If the access failure rate and access abandonment rate do not exceed the corresponding preset thresholds, the proportion of the number of times the request sample identifier and the actual operation sample identifier do not match during the access process in the management period is obtained to the total number of accesses, and it is marked as the identifier matching anomaly rate; and the record information of each access operation is obtained, the ratio of the number of accesses with incomplete record elements in the management period to the total number of accesses is calculated to obtain the access record anomaly rate, and the ratio of the number of accesses with complete traceability in the management period to the total number of accesses is calculated, and the value 1 is subtracted from the ratio result to obtain the traceability non-traceability rate; The abnormal rate of identifier matching, abnormal rate of access record, and untraceable rate of traceability are compared with the preset thresholds for the abnormal rate of identifier matching, abnormal rate of access record, and untraceable rate of traceability, respectively. If the abnormal rate of identifier matching, abnormal rate of access record, or untraceable rate of traceability exceeds the corresponding preset threshold, it indicates that the performance of access management for biological samples is poor during the management period, and an access supervision warning signal is generated.

[0026] Furthermore, if the identification matching anomaly rate, access record anomaly rate, and traceability non-traceability rate do not exceed the corresponding preset thresholds, then the ratio of the time consumed by a single access to the corresponding preset time threshold is used to calculate the single access time coefficient, and the average of all single access time coefficients within the management period is used to calculate the access efficiency value. The sample access control coefficient is obtained by weighting and summing the access loss rate, access abandonment rate, identifier matching anomaly rate, access record anomaly rate, traceability non-traceability rate, and access effectiveness anomaly. Specifically, each of these factors is assigned a corresponding preset weight coefficient, and the following six products are multiplied by their respective preset weight coefficients. The sum of these six products is then marked as the sample access control coefficient. It should be noted that the higher the value of the sample access supervision coefficient, the worse the overall performance of biological sample access management during the management period. The sample access supervision coefficient is compared with the preset sample access supervision coefficient threshold. If the sample access supervision coefficient exceeds the preset sample access supervision coefficient threshold, it indicates that the overall performance of biological sample access management during the management period is poor, and an access supervision warning signal is generated. If the sample access supervision coefficient does not exceed the preset sample access supervision coefficient threshold, it indicates that the overall performance of biological sample access management during the management period is good, and an access supervision qualified signal is generated.

[0027] The working principle of this invention is as follows: During use, the storage environment precision sensing module collects the physicochemical parameters of the storage environment and the sample's own characteristic parameters to form a standardized monitoring parameter set. Based on the standardized dataset, it accurately distinguishes between safe and risky states. After identifying a risky state, it automatically adjusts the environment, which helps to quickly resolve storage hazards, reduce manual intervention costs, and ensure that the sample's activity does not continuously deteriorate. The storage transfer urgency output module reasonably judges the urgency of storage transfer, ensuring that high-value and high-urgency samples are dispatched and disposed of in a timely manner, avoiding sample loss due to transfer delays. Furthermore, through the sample management anomaly analysis module, it extends from single sample control to overall management optimization, which can promptly discover potential loopholes in the overall storage management. After confirming that the overall management is qualified, it focuses on the supervision of storage and retrieval operations, ensuring the safety, accuracy, and traceability of the sample storage and retrieval process. This is conducive to promoting the refined and intelligent upgrade of biological sample storage management and comprehensively ensuring the storage safety of biological samples.

[0028] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0029] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A biological sample storage management decision control system based on multi-parameter monitoring, characterized in that, It includes a storage environment precision perception module, a storage status intelligent assessment module, an adaptive adjustment and control module, a storage transfer urgency output module, and a storage management decision terminal; The storage environment precision sensing module comprehensively collects the physicochemical parameters of the biological sample storage environment and the sample's own characteristic parameters, forming a standardized monitoring parameter set for the corresponding biological sample and outputting it in real time. The intelligent storage status assessment module analyzes and judges the storage status of the corresponding biological samples based on a standardized monitoring parameter set. When the storage status is judged to be at risk, the adaptive adjustment control module adaptively adjusts the storage environment of the corresponding biological samples. The storage transfer urgency output module analyzes the storage transfer urgency of the corresponding biological sample, generates a high-urgency transfer signal or a low-urgency transfer signal based on this, and sends the high-urgency transfer signal or the low-urgency transfer signal to the storage management decision-making terminal.

2. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 1, characterized in that, The specific analysis process of the storage status intelligent assessment module is as follows: Receive a standardized set of monitoring parameters, construct an evaluation index system that includes environmental stability index E and sample activity decay index A, set a formula for calculating the storage status risk value, and calculate the storage status risk value R through the formula. If R < Rmin, the storage is determined to be in a safe state. Otherwise, it is judged as a storage risk state.

3. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 2, characterized in that, The sample activity decay index A = (Ao - At) / Ao; the environmental stability index E is calculated as follows: E = [1 - (|T - To| / To + |H - Ho| / Ho + |X - Xo| / Xo + |C - Co| / Co + V / Vmax) / 5].

4. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 1, characterized in that, The detailed analysis process of the storage transfer urgency output module is as follows: The sample priority database is called to extract the priority P of the corresponding biological sample. The sample storage feature value is obtained by analysis, and the sample storage feature value is multiplied by the corresponding priority P to obtain the storage transfer urgency coefficient. If the storage transfer urgency coefficient exceeds the preset storage transfer urgency coefficient threshold, a high transfer urgency signal is generated; otherwise, a low transfer urgency signal is generated.

5. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 4, characterized in that, The specific methods for analyzing and obtaining the feature values ​​of the stored samples are as follows: The number of times the storage environment of the corresponding biological sample was identified as a storage risk state during the detection period was obtained and marked as the storage risk frequency. The average value of all storage state risk values ​​of the environment of the corresponding biological sample during the detection period was calculated to obtain the state characteristic coefficient. The sample storage feature value is obtained by weighting and summing the storage risk frequency, the efficiency hazard coefficient, and the state characteristic coefficient.

6. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 1, characterized in that, The storage management decision-making terminal communicates with the sample management anomaly analysis module. The sample management anomaly analysis module analyzes the degree of management anomalies for all biological samples during the management period, generates a sample management qualified signal or a sample management early warning signal through analysis, and sends the sample management qualified signal or sample management early warning signal to the storage management decision-making terminal.

7. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 6, characterized in that, The specific analysis process of the sample management anomaly analysis module is as follows: The number of early discarded samples during the management period is obtained and marked as the early discard detection value. If the early discard detection value exceeds the preset early discard detection threshold, a sample management early warning signal is generated.

8. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 7, characterized in that, If the early rejection test value does not exceed the preset early rejection test threshold, the sample tube abnormality value is calculated by weighted summation of the early rejection test value, the early rejection loss value, and the allowable poor condition value. If the sample tube abnormality value exceeds the preset sample tube abnormality threshold, a sample management early warning signal is generated; otherwise, a sample management qualified signal is generated.

9. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 6, characterized in that, The sample management anomaly analysis module communicates with the sample access supervision analysis module. When the sample access supervision analysis module receives a qualified sample management signal, it analyzes the performance of biological sample access supervision during the management period and sends the access supervision early warning signal or access supervision qualified signal to the storage management decision-making terminal.

10. The biological sample storage management decision control system based on multi-parameter monitoring according to claim 9, characterized in that, The specific analysis process of the sample access and regulatory analysis module is as follows: If the access-related loss prevalence or access-related abandonment prevalence exceeds the corresponding preset threshold, an access supervision warning signal is generated; if neither the access-related loss prevalence nor the access-related abandonment prevalence exceeds the corresponding preset threshold, the identifier matching anomaly rate, access record anomaly rate, and traceability non-traceability rate are compared with the preset identifier matching anomaly rate threshold, the preset access record anomaly rate threshold, and the preset traceability non-traceability rate threshold, respectively. If the identifier matching anomaly rate, access record anomaly rate, or traceability non-traceability rate exceeds the corresponding preset threshold, an access supervision warning signal is generated. If the anomaly rate of identifier matching, the anomaly rate of access record, and the untraceability rate of traceability do not exceed the corresponding preset thresholds, the sample access supervision coefficient is calculated by weighted summation of the access loss prevalence value, access abandonment prevalence value, identifier matching anomaly rate, access record anomaly rate, untraceability rate of traceability, and access effectiveness value. If the sample access supervision coefficient exceeds the preset sample access supervision coefficient threshold, an access supervision early warning signal is generated; otherwise, an access supervision qualified signal is generated.

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