A laboratory intelligent management system based on big data

Abstract

The present invention relates to the field of laboratory instrument management, and specifically discloses a laboratory intelligent management system based on big data. The present invention analyzes the importance of each competing appointment by acquiring the number of instrument users, the appointment usage time, and the name of the instrument application experiment of each competing appointment; acquires the historical number of late arrivals, the number of missed appointments, the number of timeouts, and the related maintenance information of each instrument user in each competing appointment, and analyzes the historical credit of each competing appointment; analyzes the priority ranking of each competing appointment according to the importance and historical credit of each competing appointment, and judges whether each competing appointment is successfully booked in combination with the number of successful appointment places, which can improve the evaluation system of appointment priority, further acquire alternative appointment plans for each unsuccessful competing appointment, and can improve the intelligence and flexibility of laboratory instrument appointment management, and enhance the experience of appointment users.

Description

Technical Field

[0001] The present invention relates to the field of laboratory management, and more particularly to a laboratory intelligent management system based on big data. Background Art

[0002] Laboratory instrument management is not only the basis of daily laboratory operations, but also a key factor in achieving scientific research goals, ensuring safety and promoting academic progress. It plays a vital role in scientific research, education and industrial production.

[0003] Among them, appointment management is one of the important aspects of laboratory instrument management. Laboratories usually have multiple researchers or experimental teams sharing limited instrument resources. In order to optimize resource utilization, reasonably arrange experimental time, improve management efficiency, avoid conflicts and disputes, enable the laboratory to better meet the needs of researchers and improve the efficiency and quality of research work, instrument appointment management is very necessary.

[0004] However, the existing laboratory instrument reservation management still has some limitations and shortcomings in practical applications.

[0005] For example, the existing Chinese patent with publication number CN104809239A discloses a method for instrument reservation registration and its management platform, which first sends a request to the server to use a certain instrument within the reservation time. The server determines whether the reservation is successful. If the reservation is successful, when using it, first scan and obtain the instrument identification code information and send the identification code information to the server. The server determines whether the instrument can be used. If the use is allowed, after the use is over, scan again to obtain the instrument identification code information and send it to the server. The server registers the end time to complete the entire reservation and use process of the instrument, thereby managing the use of the instrument, which has the characteristics of reasonable use allocation and high utilization rate.

[0006] However, the above patent has the following deficiencies: 1. The above patent queries whether there is an appointment record for the appointment instrument within the appointment time. If there is no appointment record, the appointment registration is successful. If there is an appointment record, the appointment registration is unsuccessful. This shows that the above patent determines whether the appointment is successful based on the time of the appointment record, that is, the order of the appointment time. The system for approving whether the appointment is successful or evaluating the appointment priority has too single analysis indicators and does not take into account other factors that affect the appointment priority, such as the importance of the application experiment of the appointment instrument and the user's historical credit. There may be a situation where an experiment that is very important but submitted a reservation application a little later cannot successfully reserve an instrument, which makes the reliability and scientificity of laboratory instrument reservation management insufficient, which is not conducive to the reasonable allocation and maximum utilization of laboratory instruments.

[0007] 2. The above patent does not provide a solution for unsuccessful appointment registration, such as providing alternative appointment plans, recommending users to change the usage time or choose other instruments with similar functions, which makes the flexibility of laboratory instrument appointment management low and reduces the experience of appointment users. Summary of the invention

[0008] In view of the above problems, the present invention proposes a laboratory intelligent management system based on big data to realize the function of laboratory instrument management.

[0009] The technical solution adopted by the present invention to solve its technical problem is: the present invention provides a laboratory intelligent management system based on big data, including: an instrument reservation basic information acquisition module: used to obtain the basic information of each reservation on the laboratory instrument online reservation platform of the target laboratory, wherein the basic information includes the application submission time, the applicant, the instrument users, the reserved instrument, the reserved usage time period and the reservation purpose.

[0010] Competing appointment screening and summary module: used to obtain appointments that use the same instrument, have overlapping time periods, and whose application submission interval is less than the set interval, and record them as competing appointments.

[0011] Competitive appointment importance analysis module: used to obtain the number of people using the instrument, the duration of the appointment, the name of the instrument application experiment for each competitive appointment, and analyze the importance of each competitive appointment.

[0012] Competitive appointment historical credit analysis module: used to obtain the historical number of lateness, historical number of missed appointments and historical number of timeouts of each instrument user in each competitive appointment, and obtain the fault type and fault severity level of each related maintenance of each instrument user, and analyze the historical credit of each competitive appointment.

[0013] Competing appointment priority ranking acquisition module: used to analyze the appointment priority coefficient of each competing appointment according to the importance and historical credit of each competing appointment, and further obtain the priority ranking of each competing appointment.

[0014] Competitive appointment approval module: used to obtain the number of successful appointments, and combined with the priority ranking of each competing appointment, determine whether each competing appointment is successful. If the appointment is successful, send a successful appointment reminder. If the appointment is unsuccessful, count the unsuccessful competing appointments.

[0015] Unsuccessful reservation processing module: used to obtain the alternative reservation plans for each time replacement and each instrument replacement for each competing reservation that failed to be booked, and provide feedback.

[0016] Database: used to store detailed information of each instrument in the target laboratory.

[0017] Compared with the prior art, the laboratory intelligent management system based on big data described in the present invention has the following beneficial effects: 1. The present invention obtains the importance and historical credit of each competing appointment, analyzes the appointment priority coefficient of each competing appointment, obtains the priority ranking of each competing appointment, and obtains the number of successful appointments, and determines whether each competing appointment is successful. It can improve the appointment priority evaluation system, improve the reliability and scientificity of evaluating appointment priorities, and thus facilitate the rational allocation and full utilization of laboratory instruments.

[0018] 2. The present invention can improve the intelligence and flexibility of laboratory instrument reservation management and thus enhance the experience of reservation users by obtaining alternative reservation plans for each time replacement and alternative reservation plans for each instrument replacement for each competing reservation that failed to be reserved and providing feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 This is a system module connection diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the present invention provides a laboratory intelligent management system based on big data, including an instrument reservation basic information acquisition module, a competitive reservation screening and summary module, a competitive reservation importance analysis module, a competitive reservation history credit analysis module, a competitive reservation priority ranking acquisition module, a competitive reservation approval module, an unsuccessful reservation processing module, and a database.

[0023] The competitive appointment screening and summary module is respectively connected to the instrument appointment basic information acquisition module, the competitive appointment importance analysis module and the competitive appointment history credit analysis module; the competitive appointment priority ranking acquisition module is respectively connected to the competitive appointment importance analysis module, the competitive appointment history credit analysis module and the competitive appointment approval module; the unsuccessful appointment processing module is connected to the competitive appointment approval module; and the database is respectively connected to the competitive appointment history credit analysis module and the competitive appointment approval module.

[0024] The instrument reservation basic information acquisition module is used to obtain the basic information of each reservation on the laboratory instrument online reservation platform of the target laboratory, wherein the basic information includes the application submission time, the applicant, the instrument users, the reserved instrument, the reserved usage time period and the reservation purpose.

[0025] As a preferred solution, when users make an appointment on the online reservation platform for laboratory instruments of the target laboratory, they need to fill out an application form online and retrieve basic reservation information through the data terminal of the online reservation platform for laboratory instruments.

[0026] As a preferred option, the target laboratories include but are not limited to: university laboratories, scientific research institute laboratories and enterprise laboratories.

[0027] As a preferred solution, the appointment applicant refers to a person who submits an appointment application on the laboratory instrument online appointment platform.

[0028] As a preferred solution, the instrument user refers to the personnel who use the instrument including the person who applies for an appointment. There may be multiple persons, and the person who applies for an appointment is the representative of the personnel who use the instrument.

[0029] As a preferred solution, the scheduled purpose refers to the name of the scheduled instrument application experiment.

[0030] The competing appointment screening and summarizing module is used to obtain appointments that use the same instrument, have overlapping appointment time periods, and whose application submission interval is less than the set interval, and record them as competing appointments.

[0031] The competition reservation importance analysis module is used to obtain the number of personnel using the instrument, the reservation usage time, and the name of the instrument application experiment for each competition reservation, and analyze the importance of each competition reservation.

[0032] Furthermore, the specific working process of the competitive appointment importance analysis module is: according to the personnel using the instruments, the appointment usage time period and the appointment purpose of each competitive appointment, the number of personnel using the instruments, the appointment usage time and the name of the instrument application experiment of each competitive appointment are obtained.

[0033] Set the weight factor corresponding to each experimental category, filter out the weight factor of the experimental category to which each competing reservation corresponds, and record it as , Indicates The number of the competing reservation, .

[0034] By analyzing the formula Get the importance of each competing appointment ,in A correction factor indicating the importance of the preset, Respectively represent The number of people using the equipment and the duration of the reservations, Indicates the number of competing reservations.

[0035] The competitive appointment historical credit analysis module is used to obtain the historical number of lateness, historical number of missed appointments and historical number of timeouts of each instrument user in each competitive appointment, and to obtain the fault type and fault severity level of each related maintenance of each instrument user, and analyze the historical credit of each competitive appointment.

[0036] Furthermore, the specific working process of the competitive appointment history credit analysis module includes: obtaining the historical number of late arrivals, historical number of missed appointments and historical number of timeouts of each instrument user of each competitive appointment, and recording them as , Indicates The number of the person using the instrument, .

[0037] As a preferred solution, the basic historical appointment information of each instrument user in each competing appointment is obtained through the laboratory instrument online appointment platform of the target laboratory, and the historical number of lateness, historical number of missed appointments and historical number of timeouts of each instrument user in each competing appointment is obtained in combination with the usage records of each instrument in the target laboratory stored in the database.

[0038] By analyzing the formula Get the first credit factor of each competing reservation ,in They respectively represent the impact factors corresponding to the preset number of unit lateness, number of unit no-shows and number of unit overtimes.

[0039] Furthermore, the specific working process of the competitive appointment history credit analysis module also includes: extracting the fault type, fault severity level and fault-associated user of each maintenance of each instrument in the target laboratory stored in the database; if a certain instrument user in a competitive appointment belongs to the fault-associated user of a certain maintenance of a certain instrument in the target laboratory, then the maintenance of the instrument is recorded as the associated maintenance of the instrument user, and the associated maintenance of each instrument user in each competitive appointment is counted, and the fault type and fault severity level of each associated maintenance of each instrument user in each competitive appointment are further obtained.

[0040] Set the influencing factors corresponding to each fault type and each fault severity level respectively, and screen out the influencing factors corresponding to the fault type and fault severity level of each associated maintenance of each instrument user in each competitive appointment, and record them as , Indicates The number of the associated repair, .

[0041] As a preferred solution, the fault types of each instrument repaired in the target laboratory are divided in the same way.

[0042] By analyzing the formula Get the second credit factor of each competing reservation ,in Represents a natural constant.

[0043] Furthermore, the specific working process of the competitive reservation history credit analysis module also includes: Get the historical credit of each competing reservation ,in Respectively represent the weights of the preset first credit coefficient and the second credit coefficient, .

[0044] The competing reservation priority ranking acquisition module is used to analyze the reservation priority coefficient of each competing reservation according to the importance and historical credit of each competing reservation, and further obtain the priority ranking of each competing reservation.

[0045] Furthermore, the specific working process of the competitive reservation priority ranking acquisition module is: by analyzing the formula Get the reservation priority coefficient of each competing reservation ,in Respectively represent the weights of preset importance and historical credit, .

[0046] Sort each competing reservation according to its reservation priority coefficient from large to small to obtain the priority ranking of each competing reservation.

[0047] The competitive reservation approval module is used to obtain the number of successful reservation places, and determine whether each competitive reservation is successful in combination with the priority ranking of each competitive reservation. If the reservation is successful, a successful reservation reminder is sent; if the reservation is unsuccessful, statistics are collected for each unsuccessful competitive reservation.

[0048] Furthermore, the specific working process of the competition reservation approval module is: recording the reserved instrument of each competition reservation as the designated instrument, and recording the time period obtained by integrating the reserved time periods of each competition reservation as the designated time period.

[0049] According to the instruments in the target laboratory and the available time periods of each instrument stored in the database, the total number of designated instruments in the target laboratory and the available time periods of each designated instrument are obtained, and the number of designated instruments that are idle in the target laboratory within the specified time period is further obtained, which is recorded as the number of successful reservations.

[0050] The successful reservation places will be allocated according to the order of priority ranking of each competing reservation. The competing reservations that have obtained successful reservation places will be recorded as successful competing reservations, and a successful reservation reminder will be sent. The competing reservations that have not obtained successful reservation places will be recorded as unsuccessful competing reservations, and statistics will be kept on each unsuccessful competing reservation.

[0051] As a preferred solution, if the competing appointment is successful, the laboratory instrument online appointment platform sends a successful appointment reminder to the appointment applicant of the competing appointment. The successful appointment reminder can be sent in the form of email, text message or message notification within the laboratory instrument online appointment platform.

[0052] In another specific embodiment, after the competitive appointment is successfully made, the laboratory instrument online appointment platform will also send an appointment confirmation reminder to the appointment applicant of the competitive appointment to confirm the reserved instrument and usage time period.

[0053] It should be noted that the present invention obtains the importance and historical credit of each competing appointment, analyzes the appointment priority coefficient of each competing appointment, obtains the priority ranking of each competing appointment, and obtains the number of successful appointments, and determines whether each competing appointment is successful. It can improve the appointment priority evaluation system and improve the reliability and scientificity of evaluating appointment priorities, which is conducive to the rational allocation and full utilization of laboratory instruments.

[0054] The unsuccessful reservation processing module is used to obtain the time replacement alternative reservation plans and the instrument replacement alternative reservation plans of each competing reservation that failed to be reserved, and provide feedback.

[0055] Furthermore, the specific working process of the unsuccessful reservation processing module includes: according to the total number of designated instruments in the target laboratory and the available time periods of each designated instrument, obtaining the available time periods of each designated instrument in the target laboratory that do not overlap with the designated time periods, and obtaining the time replacement alternative reservation plans for each competing reservation that failed to be reserved.

[0056] As a preferred solution, the time replacement alternative reservation solution means that the reservation uses the same instrument as the original reservation, but the reservation usage time period is different from the original reservation.

[0057] Furthermore, the specific working process of the unsuccessful reservation processing module also includes: obtaining the reservation use instrument demand function of each competing reservation for which the reservation is unsuccessful.

[0058] Obtain the instruments in the target laboratory that have the demand function for the use of the instruments in the competing appointments with unsuccessful reservations and whose available time periods match the reservation usage time periods, obtain alternative reservation plans for the replacement of the instruments in the competing appointments with unsuccessful reservations, and feed back to the reservation applicants for the competing appointments with unsuccessful reservations.

[0059] As a preferred solution, if the competing reservations that failed to make reservations have the same alternative reservation plans, the allocation of the alternative reservation plans is determined based on the priority ranking.

[0060] As a preferred solution, the demand functions of the reserved instruments for each unsuccessful competing appointment are obtained. The specific method is: based on the reserved instruments and the reservation purposes of each unsuccessful competing appointment, the names of the application experiments of the reserved instruments for each unsuccessful competing appointment are obtained, and the functions of the reserved instruments for each unsuccessful competing appointment in the experiments are obtained, and the functions are recorded as the demand functions of the reserved instruments for each unsuccessful competing appointment.

[0061] As a preferred option, the instrument replacement alternative reservation plan means that the reservation time period is the same as the original reservation, but the reservation instrument is different from the original reservation.

[0062] It should be noted that the present invention can improve the intelligence and flexibility of laboratory instrument reservation management and thus enhance the experience of reservation users by obtaining alternative reservation plans for time replacement and alternative reservation plans for instrument replacement for each competing reservation that failed to be reserved and providing feedback.

[0063] The database is used to store detailed information of each instrument in the target laboratory.

[0064] Furthermore, the detailed information of each instrument in the target laboratory stored in the database includes name, model, specification, available time periods, usage records, and maintenance records, wherein the usage records include the time period of each usage, user and purpose, and the maintenance records include the fault type, fault severity level, fault-related purpose and fault-related user of each repair.

[0065] As a preferred solution, detailed information of each instrument in the target laboratory is updated in real time to reflect the actual availability of the instruments in the target laboratory.

[0066] As a preferred solution, the purpose of the instrument in the target laboratory refers to the name of the instrument application experiment.

[0067] As a preferred solution, the fault severity level of the instruments in the target laboratory is obtained by comprehensive evaluation based on the maintenance time and maintenance cost.

[0068] As a preferred solution, the failure-associated use of the instrument in the target laboratory refers to the name of the experiment applied when the instrument fails.

[0069] As a preferred solution, the fault-associated user of the instrument in the target laboratory refers to the participant in the application experiment when the instrument fails, and there may be multiple fault-associated users.

[0070] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A laboratory intelligent management system based on big data, characterized in that: include: Instrument reservation basic information acquisition module: used to obtain the basic information of each reservation on the target laboratory's laboratory instrument online reservation platform, where the basic information includes application submission time, applicant, instrument user, scheduled instrument, scheduled usage time period and reservation purpose; Competitive appointment screening and summary module: used to obtain appointments with the same instrument, overlapping appointment time periods, and application submission intervals shorter than the set interval, and record them as competing appointments; Competitive appointment importance analysis module: used to obtain the number of people using the instrument, appointment usage time, and name of the instrument application experiment for each competitive appointment, and analyze the importance of each competitive appointment; Competitive appointment history credit analysis module: used to obtain the historical number of lateness, historical number of missed appointments and historical number of timeouts of each instrument user in each competitive appointment, and obtain the fault type and fault severity level of each related maintenance of each instrument user, and analyze the historical credit of each competitive appointment; Competitive appointment priority ranking acquisition module: used to analyze the appointment priority coefficient of each competing appointment according to the importance and historical credit of each competing appointment, and further obtain the priority ranking of each competing appointment; Competitive appointment approval module: used to obtain the number of successful appointments, and determine whether each competing appointment is successful based on the priority ranking of each competing appointment. If the appointment is successful, a successful appointment reminder is sent; if the appointment is unsuccessful, the unsuccessful competing appointments are counted; Unsuccessful reservation processing module: used to obtain alternative reservation plans for each time and each instrument for each competing reservation that failed to be booked, and provide feedback; Database: used to store detailed information of each instrument in the target laboratory; The specific working process of the competition reservation importance analysis module is as follows: According to the instrument users, scheduled use time periods and reservation purposes of each competing reservation, the number of instrument users, scheduled use time and the name of the instrument application experiment of each competing reservation are obtained; Set the weight factor corresponding to each experimental category, filter out the weight factor of the experimental category to which each competing reservation corresponds, and record it as , Indicates The number of the competing reservation, ; By analyzing the formula Get the importance of each competing appointment ,in A correction factor indicating the importance of the preset, Respectively represent The number of people using the equipment and the duration of the reservations, represents the number of competing appointments; The specific working process of the competitive reservation history credit analysis module includes: Get the historical number of late arrivals, missed appointments, and timeouts of each instrument user for each competing appointment, and record them as , Indicates The number of the person using the instrument, ; By analyzing the formula Get the first credit factor of each competing reservation ,in Respectively represent the impact factors corresponding to the preset number of unit lateness, number of unit no-shows and number of unit overtimes; The specific working process of the competitive reservation history credit analysis module also includes: Extract the fault type, fault severity level and fault-associated user of each maintenance of each instrument in the target laboratory stored in the database; if a certain instrument user in a competing appointment belongs to the fault-associated user of a certain maintenance of a certain instrument in the target laboratory, then record the maintenance of the instrument as the associated maintenance of the instrument user; count the associated maintenance of each instrument user in each competing appointment, and further obtain the fault type and fault severity level of each associated maintenance of each instrument user in each competing appointment; Set the influencing factors corresponding to each fault type and each fault severity level respectively, and screen out the influencing factors corresponding to the fault type and fault severity level of each associated maintenance of each instrument user in each competitive appointment, and record them as , Indicates The number of the associated repair, ; By analyzing the formula Get the second credit factor of each competing reservation ,in represents a natural constant; The specific working process of the competitive reservation history credit analysis module also includes: By analyzing the formula Get the historical credit of each competing reservation ,in Respectively represent the weights of the preset first credit coefficient and the second credit coefficient, ; The specific working process of the competitive reservation priority ranking acquisition module is as follows: By analyzing the formula Get the reservation priority coefficient of each competing reservation ,in Respectively represent the weights of preset importance and historical credit, ; Sort each competing reservation according to its reservation priority coefficient from large to small to obtain the priority ranking of each competing reservation.

2. According to claim 1, a laboratory intelligent management system based on big data is characterized by: The detailed information of each instrument in the target laboratory stored in the database includes the name, model, specification, each available time period, usage record, and maintenance record, wherein the usage record includes the time period of each usage, the user and the purpose, and the maintenance record includes the fault type of each repair, the fault severity level, the fault-related purpose and the fault-related user.

3. According to claim 2, a laboratory intelligent management system based on big data is characterized in that: The specific working process of the competition appointment approval module is as follows: Record the reserved instrument of each competing reservation as the designated instrument, and record the time period obtained by integrating the reserved time periods of each competing reservation as the designated time period; According to the instruments in the target laboratory and the available time periods of the instruments stored in the database, the total number of designated instruments in the target laboratory and the available time periods of the designated instruments are obtained, and the number of designated instruments that are idle in the target laboratory within the specified time period is further obtained, which is recorded as the number of successful reservations; The successful reservation places will be allocated according to the order of priority ranking of each competing reservation. The competing reservations that have obtained successful reservation places will be recorded as successful competing reservations, and a successful reservation reminder will be sent. The competing reservations that have not obtained successful reservation places will be recorded as unsuccessful competing reservations, and statistics will be kept on each unsuccessful competing reservation.

4. According to claim 3, a laboratory intelligent management system based on big data is characterized in that: The specific working process of the unsuccessful reservation processing module includes: According to the total number of designated instruments in the target laboratory and the available time periods of the designated instruments, the available time periods of the designated instruments in the target laboratory that do not overlap with the designated time periods are obtained, and alternative time replacement reservation plans for the competing reservations that failed to be reserved are obtained.

5. The laboratory intelligent management system based on big data according to claim 4 is characterized by: The specific working process of the unsuccessful reservation processing module also includes: The function of obtaining the demand function of the reservation use instrument for each competing reservation that failed to be reserved; Obtain the instruments in the target laboratory that have the demand function for the use of the instruments in the competing appointments with unsuccessful reservations and whose available time periods match the reservation usage time periods, obtain alternative reservation plans for the replacement of the instruments in the competing appointments with unsuccessful reservations, and feed back to the reservation applicants for the competing appointments with unsuccessful reservations.

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