Block chain-based laboratory instrument resource sharing management method and system

Abstract

The invention belongs to the technical field of resource management, and discloses a laboratory instrument resource sharing management method and system based on a block chain. Comprising the following steps: acquiring and cleaning instrument data to obtain accurate operation data and attribute data; carrying out loss refinement, and outputting an instrument operation behavior data set; carrying out combination instrument identification, and outputting a coupling resource binding field; fusing the data to form an instrument use record data set; determining an instrument cleaning time period, and outputting an instrument cleaning label; performing behavior analysis, constructing a user behavior portrait, and outputting a user behavior tag; performing privacy degree regulation and control, and outputting privacy field data; and constructing a resource sharing data set and sending the resource sharing data set to the block chain. According to the method, efficient, fair and credible management of laboratory instrument resources is realized through quantifying instrument loss, intelligently identifying instrument combination, dynamically planning a maintenance period, constructing a user credit portrait and implementing hierarchical privacy protection, and the resource utilization efficiency and the management level are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and more specifically, to a blockchain-based method and system for sharing and managing laboratory instrument resources. Background Technology

[0002] With the increasing intensity of scientific research experiments, the need for sharing laboratory equipment resources, especially high-precision instruments, among multiple teams or platforms is becoming increasingly urgent. Particularly in experimental bases such as universities and research institutes, there is a pressing need for an efficient, fair, and reliable unified management system to achieve refined control over the reservation, operation, and recording of instrument resources. However, existing resource management methods mostly focus on basic management functions and still face various bottlenecks in complex sharing scenarios in practical applications, restricting the accuracy of scheduling and execution and management efficiency.

[0003] 1. Current resource management methods only record events when instruments are reserved or used, lacking the ability to model the actual wear and tear during operation. This leads to inaccurate identification of equipment responsibility and difficulty in tracing user behavior. For example, in the case of multiple users sharing high-frequency equipment, if the specific usage of vulnerable parts is not identified, a user may frequently perform high-power tasks without being recorded. As a result, all the wear and tear on the instrument is transferred to other users, causing unfairness in the sharing rules.

[0004] 2. In actual experiments, there are situations where multiple instruments are used in combination, leading to chaotic instrument scheduling. However, the existing resource management method uses separate reservations based on instrument numbers, resulting in conflicts where the main instrument is reserved but the sub-instruments are occupied by other users, thus causing task interruption.

[0005] 3. After using laboratory instruments, different levels of cleaning or maintenance are often required. Existing resource management methods lack an assessment process for the time range occupied after use. For example, user A can reuse the instrument after only a short cooling time when using a small amount of sample, while user B needs a longer period of deep cleaning after adding chemical reagents. Existing resource management methods cannot distinguish between these, which can easily lead to mis-booking and confusion in instrument scheduling.

[0006] 4. Existing resource management methods lack a profiling mechanism for abnormal user behavior, making it difficult to assess user credit and indirectly leading to a lack of security in the use of experimental instruments.

[0007] 5. The blockchain does not perform fine-grained judgment on the privacy status of fields, resulting in the complete disclosure of information such as user identity, experimental purpose, and experimental parameter details. For some sensitive experiments, this seriously affects the long-term sharing of data and project compliance.

[0008] In view of this, existing technological systems have significant shortcomings in terms of refined management, intelligent scheduling, fairness of use, and data security. This invention proposes a blockchain-based method and system for sharing and managing laboratory instrument resources to address these problems. Summary of the Invention

[0009] To overcome the aforementioned shortcomings of the existing technology and to achieve the above objectives, the present invention provides the following technical solution: a blockchain-based method for sharing and managing laboratory instrument resources, comprising: S1. Collect laboratory instrument operation data and instrument attribute data, and perform data cleaning to obtain accurate instrument operation data and high-quality instrument attribute data; S2. Refine the loss data of the precise instrument operation data and output the instrument operation behavior dataset; S3. Based on high-quality instrument attribute data, perform combined instrument identification and output coupled resource binding fields; combine coupled resource binding fields and instrument operation behavior datasets to obtain instrument usage record datasets; S4. Determine the instrument cleaning period based on the instrument usage record dataset and output the instrument cleaning label; S5. Receive user cancellation request information, perform behavioral analysis based on instrument usage record dataset, and output user behavior labels; S6. Combine user behavior tags and instrument usage record datasets to adjust the level of privacy and output privacy field data; S7. Construct a resource-sharing dataset and send the resource-sharing dataset to the blockchain.

[0010] Furthermore, the methods for refining the loss include: Extract operation command trigger records from the precise instrument operation data. Traverse the precise instrument operation data in sequence based on the operation command trigger records, and integrate each operation command trigger record with the corresponding timestamp, instrument number, and instrument-related parameters into an operation data set. Identify a group of operation data sets with the same instrument number and a timestamp time interval less than a preset interval threshold as an operation segment. Count the number of instrument calls in the operation segment, calculate the average operating power, and calculate the operating duration of a single instrument operation. Construct a loss function based on the average operating power and operating duration to calculate the basic loss value. Extract the instrument number corresponding to the operation segment, match the instrument number with the preset consumable component parameter table to determine if there is a consumable component in the operation segment. If so, add a consumable tag to the operation segment. Obtain the operator user ID for each operation segment. Combine the operator user ID, parameter changes, and instrument number for operation segments without consumable tags into a standard loss record. Match the basic loss value of the operation segment with the preset loss level table and output the loss level field. Further refine the operation segments with consumable tags, output consumable records, bind the standard loss records with the corresponding loss level fields, and integrate them with the consumable records into an instrument operation behavior dataset.

[0011] Furthermore, the further refined methods include: The difference between the peak current and the average current in the operation segment with the added consumable tag is calculated as the current fluctuation value, and the difference between the maximum voltage and the minimum voltage is calculated as the voltage fluctuation value. The voltage fluctuation value and the current fluctuation value are weighted and summed to obtain the fluctuation degree factor. The product of the fluctuation degree factor and the basic loss value is calculated to obtain the refined loss value. The refined loss value is matched with the preset loss level table, and the refined loss level field is output. The operation user ID, parameter changes and instrument number corresponding to the operation segment with the added consumable tag are integrated and bound to the refined loss level field to output the consumable record.

[0012] Furthermore, the method for performing combined instrument identification includes: Extract the control response log of each instrument from the high-quality instrument attribute data, and compare whether continuous responses occur within the operating time of different instruments based on the control response logs; if multiple instruments issue start responses in succession after a single instrument responds, and the response interval is less than the preset response interval threshold, it is determined to be a linkage behavior event. Retrieve historical task records and count the number of times instrument combinations corresponding to linked behavior events in different tasks. If the joint call occurs repeatedly and the number of joint calls exceeds a preset combination judgment threshold, the instrument combination is judged as a combined instrument unit. Identify the response order of the combined instrument unit in the linked behavior event to obtain the instrument loading order. Mark the instrument with the earliest response time in the combined instrument unit as the main function instrument, and the remaining instruments as sub-instruments. Arrange the instrument numbers of the main function instrument and sub-instruments according to the instrument loading order to form an associated instrument group. Use the association identifier to mark the associated instrument group and bind it to the instrument number of each instrument in the associated instrument group, and output the coupling resource binding field.

[0013] Furthermore, the method for determining the instrument cleaning period includes: The instrument usage record dataset is traversed according to instrument number, and the instrument corresponding to each instrument number is designated as the target instrument. If the target instrument belongs to a predefined instrument number that requires mandatory cleaning after each use, a cleaning method is matched based on the instrument type corresponding to that instrument number, and the standard cleaning duration for that target instrument is output. The instrument number, cleaning method, and corresponding standard cleaning duration are combined to form an instrument cleaning label. If the target instrument does not belong to the mandatory cleaning category, the usage frequency of the target instrument is counted, the number of times contamination-related operations occur in the usage record of the target instrument is identified, and the time interval between the current usage record and the last cleaning operation is calculated. The usage frequency and contamination phase are then analyzed. The frequency and time interval of the closing operation are normalized, and the usage intensity value of the target instrument is obtained by weighted summation based on the normalization result. Target instruments with a basic loss value or refined loss value higher than a preset loss threshold are selected as preliminary cleaning screening instruments. A usage intensity threshold is set. If the usage intensity value is lower than the usage intensity threshold, the preliminary cleaning screening instrument can be cleaned according to standard and matched with a normal cleaning time. Otherwise, the preliminary cleaning screening instrument needs to be manually cleaned with a strong cleaning, and a special cleaning time is matched based on the cleaning method. The instrument number, cleaning method, and normal or special cleaning time of the preliminary cleaning screening instrument are combined to form an instrument cleaning label.

[0014] Furthermore, the methods for conducting behavioral analysis include: Identify the triggering reason and the user ID corresponding to the user's cancellation request, and use the triggering reason as the cancellation path for the corresponding user's cancellation request; query the user's historical instrument reservation records based on the user ID, count the number of times the user has cancelled reservations, calculate the time interval between the timestamp corresponding to each cancellation request and the timestamp of the reservation record, and sum and average the results to obtain the average cancellation interval; The information intensity weight of the corresponding cancellation path is obtained by matching the cancellation path with the preset reason weight parameter table; a user behavior profile evaluation function is constructed based on the number of cancellations and the average cancellation interval, and the user behavior evaluation value is calculated using the user behavior profile evaluation function; the user behavior trajectory score is obtained by multiplying the information intensity weight of the corresponding user and the user behavior evaluation value, and the user behavior trajectory score is matched with the preset user profile behavior parameter table to output the behavior risk level of the user; the user ID, behavior risk level and cancellation path are combined to form a user behavior tag.

[0015] Furthermore, the triggering reasons include: user-submitted cancellation request, manual intervention by laboratory management personnel, automatic release of control terminal load, and hardware fault signal response.

[0016] Furthermore, the methods for adjusting the level of privacy include: The system extracts operation records and user IDs corresponding to each instrument number from the instrument usage record dataset, and obtains the behavioral risk level from the user behavior tags. Based on the user ID, it queries the user's identity and affiliated unit to obtain user information. Based on the user information and operation records, it determines the instrument's experimental purpose and whether it constitutes sensitive information. If it is sensitive information, the corresponding operation records and user IDs are masked. If the instrument's experimental purpose is not sensitive information, the experimental purpose field and the parameter change field in the operation records are integrated into a controlled field set. Based on the visibility standards of known information, the on-chain display method of the controlled field set is set. A field mapping table is constructed based on each field in the controlled field set and its display method. This field mapping table is bound to the corresponding controlled field set and output as privacy field data.

[0017] Furthermore, the methods for constructing the resource-sharing dataset include: The instrument usage record dataset is grouped based on the instrument number to obtain instrument group data. The instrument cleaning label corresponding to each instrument number is matched with the instrument group data to output instrument number matching data. User behavior labels, privacy level labels and instrument number matching data are bound to each other to obtain resource sharing dataset.

[0018] A blockchain-based laboratory instrument resource sharing management system, used to implement a blockchain-based laboratory instrument resource sharing management method, characterized by comprising: The data acquisition module is used to collect laboratory instrument operation data and instrument attribute data, and to perform data cleaning to obtain accurate instrument operation data and high-quality instrument attribute data. The loss determination module is used to refine the loss of precise instrument operating data and output a dataset of instrument operating behavior. The combined identification module is used to identify combined instruments based on high-quality instrument attribute data and output coupled resource binding fields; the coupled resource binding fields and instrument operation behavior datasets are combined to obtain the instrument usage record dataset. The cleaning cycle determination module is used to determine the instrument cleaning period based on the instrument usage record dataset and output the instrument cleaning label. The behavior analysis module is used to receive user cancellation request information, combine it with the instrument's recorded dataset to perform behavior analysis, and output user behavior tags. The privacy identification module is used to adjust the level of privacy by combining user behavior tags and instrument usage record datasets, and output privacy field data. The shared data building module is used to construct a resource-sharing dataset and send the resource-sharing dataset to the blockchain; the modules are connected to each other via wired and / or wireless means.

[0019] The technical effects and advantages of the blockchain-based laboratory instrument resource sharing management method and system of this invention are as follows: This paper presents a blockchain-based resource-sharing management method for laboratory instruments. By collecting operational and attribute data of laboratory instruments and quantifying their wear and tear, it identifies instruments used in combination and adds corresponding identification fields to determine cleaning cycles. It also analyzes user behavior and controls privacy exposure, ultimately sending the constructed resource-sharing dataset to the blockchain. Compared to existing methods, this method precisely assigns responsibility for equipment wear and tear by specifying the degree of wear and tear during instrument operation and provides wear and tear references for subsequent users. Identifying combined instruments through response logs solves the problem of splitting and scheduling master and slave instruments. Determining cleaning methods and cycles based on usage intensity and instrument attributes, generating and directly marking instrument cleaning tags improves the rationality of resource scheduling. Analyzing user reservation and cancellation behaviors allows for user profiling, quantifying user creditworthiness, and indirectly improving instrument usage security and compliance. Addressing privacy exposure risks on the blockchain, sensitive information is anonymized to enhance information security. In conclusion, this blockchain-based laboratory instrument resource-sharing management method offers high real-time performance, high security, and high reliability, improving resource utilization efficiency and resource management level. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a blockchain-based laboratory instrument resource sharing management method according to the present invention; Figure 2 This is a schematic diagram of a blockchain-based laboratory instrument resource sharing management system according to the present invention. Detailed Implementation

[0021] 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.

[0022] Example 1 Please see Figure 1 As shown in this embodiment, a blockchain-based laboratory instrument resource sharing management method includes: S1. Collect laboratory instrument operation data and instrument attribute data, and perform data cleaning to obtain accurate instrument operation data and high-quality instrument attribute data; S2. Refine the loss data of the precise instrument operation data and output the instrument operation behavior dataset; S3. Based on high-quality instrument attribute data, perform combined instrument identification and output coupled resource binding fields; combine coupled resource binding fields and instrument operation behavior datasets to obtain instrument usage record datasets; S4. Determine the instrument cleaning period based on the instrument usage record dataset and output the instrument cleaning label; S5. Receive user cancellation request information, perform behavioral analysis based on instrument usage record dataset, and output user behavior labels; S6. Combine user behavior tags and instrument usage record datasets to adjust the level of privacy and output privacy field data; S7. Construct a resource-sharing dataset and send the resource-sharing dataset to the blockchain.

[0023] In this embodiment, the laboratory instrument operation data includes information such as operation command trigger records, instrument number, start and stop events, changes in parameters such as voltage or current during operation, and operation timestamps; the instrument attribute data includes information such as control response logs from the instrument control terminal, instrument model, functional module descriptions, control interface types, and affiliated unit information; data cleaning is achieved through missing value imputation and noise reduction processing to obtain higher quality and more accurate instrument operation data and higher quality instrument attribute data, respectively.

[0024] Methods for refining loss include: The operation command trigger records are extracted from the precise instrument operation data. The precise instrument operation data is traversed according to the order of the operation command trigger records, and each operation command trigger record is integrated with the corresponding timestamp, instrument number, and instrument-related parameters into an operation data set. The operation command trigger record refers to the trigger command issued each time the instrument-related function is started, representing a specific executable instrument action. The precise instrument operation data is traversed according to the trigger order of the operation command trigger records, and the corresponding timestamp and instrument-related operation parameters such as voltage or current are integrated with the operation command trigger record to form an operation data set corresponding to an operation, providing a data foundation for subsequent processing.

[0025] An operation data set is identified as an operation segment if it has the same instrument number and the time interval between timestamps is less than a preset interval threshold. The preset interval threshold is set based on historical operation experience. When two or more consecutive operation data sets with timestamps less than the preset interval threshold and belonging to the same instrument appear in operation data sets with adjacent triggering sequences, this operation data set is considered as an operation segment. This operation segment is used to represent a stable operation process that is continuously executed by the user on the same instrument.

[0026] The number of times the instrument is called in the operation segment is counted, the average operating power is calculated, and the running duration of a single instrument operation is calculated. The frequency of instrument use is quantified by counting the number of times the same instrument is called in the corresponding operation segment, and the average operating power of the instrument used multiple times in the operation segment is calculated to reflect the energy consumption level during the operation. The running duration of each instrument call is calculated to characterize the length of time the instrument is in an active state each time.

[0027] The loss coefficient is matched based on the number of instrument calls. A loss function is constructed based on the loss coefficient, average operating power, and operating duration to calculate the basic loss value. This is achieved by scaling the number of instrument calls to... The loss coefficient is obtained within the specified interval and used to quantify the instrument's call density in the corresponding operation segment, serving as a weight in the loss function calculation formula; the loss function calculation formula is as follows: ;in, This represents the base loss value calculated using the loss function; This represents the average operating power within the operation segment; Indicates the loss coefficient; Indicates the impedance corresponding to the instrument; Indicates the first operation segment The duration of a single run; This indicates the number of times the instrument runs during the operation segment; it should be noted that the above formula is a dimensionless calculation, and there are no cases where unit differences would prevent the calculation.

[0028] Extract the instrument number corresponding to the operation segment, match the instrument number with a preset consumable component parameter table to determine if there is a consumable component in the operation segment. If so, add a consumable tag to the operation segment. This involves obtaining the instrument number of the instrument used in each operation segment, constructing a preset consumable component parameter table by querying known instrument component descriptions in the laboratory. This table records consumable components in all laboratory instruments, such as lamps, electrodes, and filters. The process involves matching the instrument number with the preset consumable component parameter table to determine if there is a consumable component in the corresponding operation segment. If so, add a consumable tag to the operation segment.

[0029] Obtain the user ID for each operation segment. For operation segments without a consumable tag, integrate the user ID, parameter changes, and instrument number into a standard loss record. Match the basic loss value of the operation segment with a preset loss level table and output a loss level field. In this table, the user ID is matched with the instrument number, and parameter changes are included to form the standard loss record. Based on historical loss judgment experience, a preset loss level table is set. Match the calculated basic loss value with the preset loss level table and output the loss level field of the operation segment. This field is used to quantify the loss impact of the corresponding operation segment on the corresponding instrument.

[0030] The operation segments with added wearable tags are further refined to output wearable records. Standard wearable records are bound to corresponding wear level fields and integrated with wearable records to form an instrument operation behavior dataset. The binding of standard wearable records to corresponding wear level fields means that the wear level field is used as the tag for the standard wearable record to reflect the wear status. By integrating the bound standard wearable records with wearable records, an instrument operation behavior dataset that can reflect the instrument wear status is formed.

[0031] Further refinement methods include: The difference between the peak current and the average current in the operation segment with the added wear and tear label is used as the current fluctuation value, and the difference between the maximum voltage value and the minimum voltage value is used as the voltage fluctuation value. The current fluctuation value and the voltage fluctuation value are often related to sudden changes such as sudden operation and instantaneous load, and have a greater impact on instrument wear and tear.

[0032] The fluctuation factor is obtained by weighted summing and averaging the voltage and current fluctuation values. The product of the fluctuation factor and the base loss value is then calculated to obtain the refined loss value. This refined loss value is matched with a preset loss level table, and the refined loss level field is output. The fluctuation factor, obtained by weighted summing and averaging the voltage and current fluctuation values, is used to integrate the impact of voltage and current fluctuations on loss. The weighting values ​​are set based on the instrument's historical observation experience. This factor is then multiplied with the base loss value of the corresponding operation segment with the added vulnerable tag to obtain the refined loss value that integrates the effects of voltage and current fluctuations. This refined loss value replaces the original base loss value and is matched with a preset loss level table to output the corresponding refined loss level field.

[0033] The operation user ID, parameter changes, and instrument number corresponding to the operation segment with the added consumable tag are integrated and bound to the refined loss level field to output consumable records.

[0034] Methods for identifying combined instruments include: Extract the control response log of each instrument from the high-quality instrument attribute data, and compare whether continuous responses occur within the operating time period of different instruments based on the control response log. The control response log refers to the response timestamp information of each instrument after receiving the operation command, and determine whether there are staged continuous responses when multiple instruments are performing the same task.

[0035] If multiple instruments issue start responses consecutively after a single instrument responds, and the response interval is less than a preset response interval threshold, it is determined to be a linkage behavior event. The preset response interval threshold is set based on instrument-related knowledge. If multiple instruments issue responses consecutively after a certain instrument responds, and the response interval is less than the preset response interval threshold, it indicates that there is a passive coordination or associated control relationship between these instruments. This set of consecutive responses is determined to be a linkage behavior event.

[0036] Obtain historical task records and count the number of times the instrument combinations corresponding to the linked behavior events in the historical task records are called together in different tasks. Specifically, by traversing the historical task records, identify whether the instrument combinations consisting of multiple instruments corresponding to the linked behavior events are called together in multiple different tasks, and count the number of times they are called together.

[0037] If the joint call occurs repeatedly and the number of joint calls exceeds the preset combination judgment threshold, the instrument combination is judged as a combined instrument unit. The preset combination judgment threshold is set based on historical judgment experience. If the number of joint calls of the instrument combination exceeds the preset combination judgment threshold, it can be determined that these instruments need to be operated in combination, and the instrument combination is judged as a combined instrument unit.

[0038] The response order of the combined instrument unit in the linked behavioral event is identified to obtain the instrument loading order. By extracting the timestamps of the first response of each of the multiple instruments in the combined instrument unit in the linked behavioral event, the activation order of different instruments in the task is determined to form the instrument loading order.

[0039] The instrument with the earliest response time in the combined instrument unit is designated as the main functional instrument, and the remaining instruments are designated as sub-instruments. The instrument numbers of the main functional instruments and sub-instruments are arranged in the order of instrument loading to form an associated instrument group. The instrument with the earliest response time is designated as the master control instrument, and the remaining instruments are designated as auxiliary modules of the master control instrument for collaborative scheduling and management. Arranging the instrument numbers of these instruments in the order of instrument loading results in an associated instrument number list, which is the associated instrument group.

[0040] The associated instrument group is marked using an association identifier and bound to the instrument number of each instrument in the associated instrument group. The coupled resource binding field is output. The associated instrument group is marked by generating an association identifier that can uniquely identify each associated instrument group, and the association identifier is bound to the instrument number of each instrument in the associated instrument group to obtain the coupled resource binding field. This field is used as an index field for operation users to schedule, reserve or search, which facilitates the overall locking, unified release and shared access control of the equipment combination state.

[0041] Methods for determining the instrument cleaning period include: The instrument usage record dataset is traversed by instrument number, and the instrument corresponding to each instrument number is taken as the target instrument. This is done by traversing the instrument usage record dataset by instrument number as index, and taking the instrument corresponding to each instrument number as the target instrument, which facilitates the subsequent direct processing of the target instrument's related data.

[0042] If the target instrument belongs to a predefined instrument number that requires mandatory cleaning after each use, a cleaning method is matched based on the instrument type corresponding to that instrument number, and the standard cleaning duration for the target instrument is output. The instrument number, cleaning method, and corresponding standard cleaning duration are combined to form an instrument cleaning tag. The instruments that require mandatory cleaning after each use are determined based on the relevant experimental instrument descriptions, and the instrument number of such instruments is identified. The aforementioned instruments refer to high-precision experimental instruments with strict maintenance requirements, such as mass spectrometers or high-sensitivity discharge power supplies. Such instruments must be cleaned after each use to ensure the accuracy of the next use. Based on the instrument number of such high-precision instruments, the known experimental instrument maintenance information is matched to find a suitable cleaning method and a specified cleaning cycle, and an instrument cleaning tag is generated. This tag will serve as an identifier for unschedulable time periods during subsequent scheduling of the corresponding instrument to prevent conflicts between scheduled times and cleaning cycles.

[0043] If the target instrument is not a mandatory cleaning instrument, then the usage frequency of the target instrument is counted, the number of times contamination-related operations occur in the instrument's usage records is identified, and the time interval between the current usage record and the last cleaning operation is calculated. The usage frequency of the target instrument refers to the number of times an operation has been performed on the instrument in all existing usage records. The number of times contamination-related operations occur in all usage records of the corresponding target instrument is identified. Contamination-related operations refer to operations that are prone to contamination as defined in the laboratory instrument's user manual, such as chemical injection or sample processing. The time interval between the latest usage record and the timestamp of the last cleaning operation is calculated and used as one of the indicators for subsequent evaluation of usage intensity.

[0044] The frequency of use, the number of occurrences of pollution-related operations, and the length of time intervals are all normalized. A weighted sum is then performed based on the normalization results to obtain the usage intensity value of the target instrument. In this embodiment, normalization transforms three indicators—frequency of use, the number of occurrences of pollution-related operations, and the length of time intervals between the last cleaning timestamp—which have different dimensions and different value ranges, into values ​​within the same dimension range. The weight of each indicator is determined by consulting the weighting table corresponding to the laboratory instrument, with different weight configurations for different instruments. The usage intensity value of the target instrument is obtained by weighting and summing the three normalized indicators. This value is used to quantify the degree of pollution impact of the target instrument.

[0045] Target instruments with basic or refined loss values ​​higher than a preset loss threshold are selected as initial cleaning screening instruments. The preset loss threshold is set based on historical loss judgment experience. If the target instrument has a wear-prone label, the refined loss value is compared with the preset loss threshold; otherwise, the basic loss value is compared with the preset loss threshold. The degree of loss is used as a supplementary judgment dimension to ensure that instruments with high loss can also be included in the judgment range that need to be cleaned in a timely manner.

[0046] A usage intensity threshold is set. If the usage intensity value is lower than the threshold, the instrument can be cleaned using standard methods with a normal cleaning duration. Otherwise, a strong manual cleaning is required, with a special cleaning duration matched to the cleaning method. The instrument number, cleaning method, and normal or special cleaning duration of the instrument are combined into an instrument cleaning label. The usage intensity threshold is set based on historical usage intensity judgment experience. If the usage intensity value is lower than the preset threshold, it indicates that the contamination level is not serious, and the standard laboratory cleaning method can be used with a corresponding cleaning duration. If the usage intensity value is higher than the threshold, it indicates that the usage intensity has reached the point where cleaning is necessary due to contaminated operation or prolonged lack of cleaning. In this case, a more complex and longer manual process than the standard cleaning method must be adopted, such as replacing consumables, multiple rinsing, and disassembling the sample chamber. A special cleaning duration is matched to the manual strong cleaning method used. The instrument number, the instruction code corresponding to the cleaning method, and the required normal or special cleaning duration of the instrument are integrated into the same instrument cleaning label.

[0047] Methods for conducting behavioral analysis include: Identify the triggering reason and the user ID corresponding to the user's cancellation request information, and use the triggering reason as the cancellation path for the corresponding user's cancellation request information. The cancellation path binds the triggering reason with the corresponding user's cancellation request information for subsequent evaluation of user behavior.

[0048] Based on the user ID, the system queries the user's historical instrument reservation records, counts the number of times the user cancels reservations, calculates the time interval between the timestamp corresponding to each cancellation request and the timestamp of the reservation record, sums them, and takes the average to obtain the average cancellation interval. In this embodiment, the system queries the user's historical instrument reservation records in the database using the user ID as the index, counts the number of times the user cancels reservations based on the historical instrument reservation records, and calculates the average cancellation interval, which serves as the data basis for behavior evaluation.

[0049] The information intensity weight of the corresponding cancellation path is obtained by matching the cancellation path with the preset reason weight parameter table. The preset reason weight parameter table is constructed based on historical behavior analysis experience, and the weight of the triggering reason corresponding to each cancellation path is matched to obtain the information intensity weight of the cancellation path.

[0050] A user behavior profile evaluation function is constructed based on the number of cancellations and the average cancellation interval. The user behavior evaluation value is then calculated using this function. The formula for calculating the user behavior profile evaluation function is as follows: ;in, This represents the user behavior evaluation value calculated by the user behavior profile evaluation function; This indicates the total number of instrument reservations; Indicates the number of times the reservation can be cancelled; Indicates the average cancellation interval; Indicates the maximum valid early cancellation time; and As weights, in this embodiment + =1, representing the degree of influence on cancellation frequency and cancellation suddenness, respectively. Those skilled in the art can adjust the two weight values ​​based on the user type of the corresponding user. For example, different weights can be used for different users such as guest login, research group members and administrators.

[0051] The user behavior trajectory score is obtained by multiplying the information intensity weight of the corresponding user and the user behavior evaluation value. The user behavior trajectory score is then matched with a preset user profile behavior parameter table to output the behavior risk level of the user. The user behavior trajectory score is obtained by multiplying the information intensity weight and the user behavior evaluation value, which takes into account both the reasons for the cancellation behavior itself and the user's behavior style. The user behavior trajectory score is then matched with a user profile behavior parameter table constructed based on a large amount of historically collected user information to determine the behavior risk level of the corresponding user, which is then used as the user profile output.

[0052] The user ID, behavior risk level, and cancellation path are combined into a user behavior tag. Specifically, the user ID is matched with the behavior risk level and cancellation path to form the user behavior tag corresponding to that user ID.

[0053] Triggering reasons include: user-initiated cancellation requests, manual intervention by laboratory management personnel, automatic release of control terminal load, and hardware fault signal response. In this embodiment, different triggering reasons correspond to different responsibility paths and operational intentions. User-initiated cancellation requests represent personal usage intentions; manual intervention by laboratory management personnel indicates a change in scheduling instructions; automatic release of control terminal load indicates the execution of the corresponding task to protect the user's behavior; and hardware fault signal response is an unpredictable accident cause. It should be noted that different triggering reasons correspond to different information intensity weights. For example, user-initiated cancellation requests are high-risk triggering reasons because they are human-initiated factors. If cancellation requests occur multiple times, it can be considered that the user's operational intention is impure, thus having a high information intensity weight. On the other hand, hardware fault signal response is an unpredictable system fault problem, with a lower information intensity weight.

[0054] Methods for regulating privacy levels include: The operation records and user IDs corresponding to each instrument number in the instrument usage record dataset are extracted, and the behavior risk level in the user behavior tag is obtained. The operation records, user IDs, and behavior risk levels corresponding to each instrument number are used as input information for privacy judgment.

[0055] User information is obtained by querying the user's identity and affiliated unit based on the user ID. The purpose of the instrument's experiment is then determined based on the user information and operation records. It is then assessed whether the purpose of the experiment constitutes sensitive information. If it is, the corresponding operation records and user ID are masked. User identity includes information such as visitor login, laboratory member, and administrator. The affiliated unit information is bound to the user's identity and user ID to obtain the user information. Based on the user information, the experiment purpose of each instrument in the operation records is queried. If certain instruments are used for highly confidential experiments, their experiment purpose is considered sensitive information. If sensitive information is found, the relevant fields in the corresponding operation records and user information are directly hidden to prevent excessive exposure of this sensitive information on the blockchain.

[0056] If the experimental purpose of the instrument is not considered sensitive information, then the experimental purpose field and the parameter change field in the operation record are integrated into a controlled field set. If some fields still need to be controlled in a non-sensitive scenario, in this embodiment, the experimental purpose field and the parameter change field are set as a controlled field set.

[0057] The on-chain display method of the controlled field set is set based on the known information visibility standard. The known information visibility standard refers to the visibility standard for data transmission on the blockchain. It is set based on the importance that different owners or users attach to the controlled field set. Based on the known information visibility standard, the display method of the controlled field set on the blockchain is set to public or encrypted.

[0058] A field mapping table is constructed based on each field in the controlled field set and its display method. The field mapping table is bound to the corresponding controlled field set and output as privacy field data. In this embodiment, each field in the controlled field set is encoded as a unique identifier corresponding to that field, and a mapping is established with the encoding of its display method to construct a field mapping table with a one-to-one correspondence between fields and display methods. The field mapping table is used to control the display method before writing data on the chain. The field mapping table and the controlled field set are bound to the controlled field set and output as privacy field data.

[0059] Methods for constructing resource-sharing datasets include: The instrument usage record dataset is grouped based on the instrument number to obtain instrument group data. The instrument cleaning label corresponding to each instrument number is matched with the instrument group data to output instrument number matching data. Specifically, the instrument usage record dataset is grouped by the instrument number as the index, and the instrument group data is marked with the instrument cleaning label corresponding to the instrument number to obtain instrument number matching data.

[0060] By binding user behavior tags, privacy level tags, and instrument number matching data, a resource-sharing dataset is obtained. This binding process achieves data matching accurate to each user, and finally, the data from all users is integrated to obtain a resource-sharing dataset that can be written to the blockchain.

[0061] This embodiment collects operational and attribute data of laboratory instruments and quantifies their wear and tear. It also identifies instruments used in combination and adds corresponding identification fields to determine cleaning cycles. Furthermore, it analyzes user behavior and controls privacy exposure. Finally, the constructed resource-sharing dataset is sent to the blockchain, realizing a blockchain-based resource-sharing management method for laboratory instruments. Compared to existing experience, by specifying the degree of wear and tear during instrument operation, it achieves precise attribution of equipment responsibility and provides wear and tear references for subsequent users. Identifying combined instruments through instrument response logs solves the problem of splitting and scheduling master and slave instruments. Based on usage intensity and instrument attributes, it determines cleaning methods and cycles, generates instrument cleaning tags, and directly marks them, improving the rationality of resource scheduling. Analyzing user reservation and cancellation behaviors allows for user profiling, quantifying user creditworthiness, and indirectly improving instrument usage security and compliance. Addressing privacy exposure risks on the blockchain, sensitive information is anonymized to enhance information security. In summary, the blockchain-based laboratory instrument resource-sharing management method boasts high real-time performance, high security, and high reliability, improving resource utilization efficiency and resource management level.

[0062] Example 2 Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. A blockchain-based laboratory instrument resource sharing management system is provided, including: The data acquisition module is used to collect laboratory instrument operation data and instrument attribute data, and to perform data cleaning to obtain accurate instrument operation data and high-quality instrument attribute data. The loss determination module is used to refine the loss of precise instrument operating data and output a dataset of instrument operating behavior. The combined identification module is used to identify combined instruments based on high-quality instrument attribute data and output coupled resource binding fields; the coupled resource binding fields and instrument operation behavior datasets are combined to obtain the instrument usage record dataset. The cleaning cycle determination module is used to determine the instrument cleaning period based on the instrument usage record dataset and output the instrument cleaning label. The behavior analysis module is used to receive user cancellation request information, combine it with the instrument's recorded dataset to perform behavior analysis, and output user behavior tags. The privacy identification module is used to adjust the level of privacy by combining user behavior tags and instrument usage record datasets, and output privacy field data. The shared data building module is used to construct a resource-sharing dataset and send the resource-sharing dataset to the blockchain; the modules are connected to each other via wired and / or wireless means.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0064] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0065] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A blockchain-based laboratory instrument resource sharing management method, characterized in that, The method comprises the following steps: S1. Collecting laboratory instrument running data and instrument attribute data, and performing data cleaning to obtain accurate instrument running data and high-quality instrument attribute data; S2. Loss refinement is performed on the accurate instrument running data, and an instrument operation behavior data set is output; S3. Combined instrument identification is performed based on the high-quality instrument attribute data, and a coupling resource binding field is output; The coupling resource binding field and the instrument operation behavior data set are combined to obtain an instrument use record data set; S4. Based on the instrument use record data set, the instrument cleaning period is determined, and an instrument cleaning label is output; S5. Receiving user cancellation request information, combining the instrument use record data set to perform behavior analysis, and outputting a user behavior label; S6. Combining the user behavior label and the instrument use record data set to perform privacy degree regulation, and outputting a privacy field data; S7. Constructing a resource sharing data set and sending the resource sharing data set to a blockchain.

2. The blockchain-based laboratory instrument resource sharing management method of claim 1, wherein, The loss refinement method comprises: Extracting the operation instruction trigger record in the accurate instrument running data, traversing the accurate instrument running data based on the order of the operation instruction trigger record, and integrating each operation instruction trigger record with the corresponding timestamp, instrument number and instrument related parameters into an operation data set; identifying a group of operation data sets in adjacent operation data sets with the same instrument number and a time interval of the timestamp less than a preset interval threshold as an operation segment; counting the number of instrument calls in the operation segment, calculating the average running power, and calculating the running duration of a single instrument running; based on the average running power and the running duration, a loss function is constructed to calculate the basic loss value; Extracting the instrument number corresponding to the operation segment, matching the instrument number with the preset loss-prone component parameter table, judging whether there is a loss-prone component in the operation segment, if there is, adding a loss-prone label to the operation segment; obtaining the operation user ID of each operation segment, integrating the operation user ID, parameter change and instrument number of the operation segment without the loss-prone label into a standard loss record, and matching the basic loss value of the operation segment with the preset loss level table to output a loss level field; further refining the operation segment with the loss-prone label to output a loss-prone record, binding the standard loss record with the corresponding loss level field, and integrating it with the loss-prone record into an instrument operation behavior data set.

3. The blockchain-based laboratory instrument resource sharing management method of claim 2, wherein, The further refinement method comprises: The difference between the current peak value and the average current value in the operation segment with the loss-prone label is calculated as the current fluctuation value, and the difference between the maximum voltage value and the minimum voltage value is calculated as the voltage fluctuation value; the weighted sum of the voltage fluctuation value and the current fluctuation value is averaged to obtain a fluctuation degree factor, and the product of the fluctuation degree factor and the basic loss value is calculated to obtain a refined loss value; the refined loss value is matched with the preset loss level table to output a refined loss level field; the operation user ID, parameter change and instrument number of the operation segment with the loss-prone label are integrated, and the refined loss level field is bound to output a loss-prone record.

4. The blockchain-based laboratory instrument resource sharing management method of claim 3, wherein, The combined instrument identification method comprises: Extracting a control response log of each instrument in high-quality instrument attribute data, comparing whether continuous responses occur in different instrument running time periods based on the control response log; if a single instrument response is followed by multiple instrument continuous start responses, and the response interval is less than a preset response interval threshold, it is determined as a linkage behavior event; Obtaining historical task records, and counting the number of common calls of the instrument combination corresponding to the linkage behavior event in different tasks in the historical task records; if the common call situation repeatedly occurs, and the number of common calls is higher than a preset combination determination threshold, the instrument combination is determined as a combined instrument unit; the response order of the combined instrument unit in the linkage behavior event is identified to obtain an instrument loading order; the instrument with the earliest response time in the combined instrument unit is marked as a main function instrument, and the remaining instruments are used as sub-instruments, and the instrument numbers of the main function instrument and the sub-instruments are arranged into an associated instrument group according to the instrument loading order; the associated instrument group is marked by using an associated identifier, and is bound with the instrument numbers of each instrument in the associated instrument group, and a coupling resource binding field is output.

5. The blockchain-based laboratory instrument resource sharing management method of claim 4, wherein, The instrument cleaning period determination manner comprises: According to the instrument number, the instrument usage record data set is traversed, and each instrument number corresponding instrument is taken as a target instrument; if the target instrument belongs to a predefined instrument number that needs to be forcedly cleaned after each use, a cleaning method is matched based on the instrument type corresponding to the instrument number, and a standard cleaning time length of the target instrument is output, the instrument number, the cleaning method and the corresponding standard cleaning time length are combined into an instrument cleaning label; if the target instrument does not belong to the forced cleaning type instrument, the usage frequency of the target instrument is counted, the number of pollution related operations in the usage record of the target instrument is identified, and the time interval length between the current usage record and the last cleaning operation of the target instrument is calculated; the usage frequency, the number of pollution related operations and the time interval length are all normalized, and a weighted sum is obtained based on the normalization result to obtain the usage intensity value of the target instrument; the target instrument with a basic loss value or a refined loss value higher than a preset loss threshold is selected as a preliminary cleaning screening instrument; a usage intensity threshold is set, if the usage intensity value is lower than the usage intensity threshold, the preliminary cleaning screening instrument can be cleaned and an ordinary cleaning time length is matched, otherwise, the preliminary cleaning screening instrument needs to be manually cleaned, and a special cleaning time length is matched based on the cleaning method, and the instrument number, the cleaning method and the ordinary cleaning time length or the special cleaning time length of the preliminary cleaning screening instrument are integrated into an instrument cleaning label.

6. The blockchain-based laboratory instrument resource sharing management method of claim 5, wherein, The behavior analysis manner comprises: Identifying the trigger cause and the operation user ID corresponding to the user cancellation request information, and taking the trigger cause as the cancellation path of the corresponding user cancellation request information; querying the historical instrument reservation record of the user based on the operation user ID, counting the number of cancellation reservations of the user, calculating the time interval between the time stamp corresponding to each cancellation request and the reservation record time stamp and performing sum averaging to obtain the average cancellation interval; The information intensity weight of the corresponding cancellation path is obtained based on matching the cancellation path with a preset reason weight parameter table; a user behavior portrait evaluation function is constructed based on the number of cancellation reservations and the average cancellation interval, and a user behavior evaluation value is calculated using the user behavior portrait evaluation function; a product of the information intensity weight of the corresponding operating user and the user behavior evaluation value is calculated to obtain a user behavior trajectory score, and the user behavior trajectory score is matched with a preset user portrait behavior parameter table to output a behavior risk level of the operating user; and the operating user ID, the behavior risk level and the cancellation path are combined to obtain a user behavior label.

7. The blockchain-based laboratory instrument resource sharing management method of claim 6, wherein, The triggering reasons include: user-initiated cancellation request, laboratory manager manual intervention, control terminal load automatic release and hardware failure signal response.

8. The blockchain-based laboratory instrument resource sharing management method of claim 7, wherein, The privacy level regulation mode includes: The operation record and the operation user ID corresponding to each instrument number in the instrument use record data set are extracted, and the behavior risk level in the user behavior label is obtained; the user information is obtained by querying the user identity and the belonging unit based on the operation user ID, and the instrument experimental purpose is determined based on the user information and the operation record; it is judged whether the instrument experimental purpose belongs to sensitive information, if it belongs to sensitive information, the corresponding operation record and operation user ID are shielded; if the instrument experimental purpose does not belong to sensitive information, the instrument experimental purpose field and the parameter change field in the operation record are integrated into a controlled field set; the on-chain display mode of the controlled field set is set based on the known information visibility standard; a field mapping table is constructed based on each field in the controlled field set and its display mode, and the field mapping table is bound to the corresponding controlled field set to output as privacy field data.

9. The blockchain-based laboratory instrument resource sharing management method of claim 8, wherein, The resource sharing data set is constructed in the following way: Each instrument related data in the instrument use record data set is grouped based on the instrument number to obtain instrument grouping data, and the instrument cleaning label corresponding to each instrument number is matched with the instrument grouping data to output instrument number matching data; the user behavior label, the privacy level label and the instrument number matching data are bound correspondingly to obtain the resource sharing data set.

10. A blockchain-based laboratory instrument resource sharing management system for implementing the blockchain-based laboratory instrument resource sharing management method of any one of claims 1-9, characterized in that, It includes: A data acquisition module is configured to acquire laboratory instrument running data and instrument attribute data, and to clean the data to obtain accurate instrument running data and high-quality instrument attribute data; A loss determination module is configured to refine the accurate instrument running data to output an instrument operation behavior data set; A combination identification module is configured to identify combined instruments based on the high-quality instrument attribute data to output coupled resource binding fields; The instrument use record data set is obtained by combining the coupled resource binding fields and the instrument operation behavior data set; A cleaning cycle determination module is configured to determine an instrument cleaning period based on the instrument use record data set to output an instrument cleaning label; A behavior analysis module is configured to receive user cancellation request information, and to analyze the behavior based on the instrument use record data set to output a user behavior label; A privacy identification module is configured to regulate the privacy level based on the user behavior label and the instrument use record data set to output privacy field data. A shared data construction module is configured to construct a resource sharing dataset and send the resource sharing dataset to a blockchain; each module is connected through wired and / or wireless means.

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