A cloud platform-based laboratory resource monitoring system

Abstract

This invention relates to the field of cloud computing technology, and more particularly to a cloud-based laboratory resource monitoring system, comprising a data acquisition module, a data storage module, a cloud computing module, and a user interface module. The data acquisition module collects relevant information about various resources within each laboratory. The data storage module stores the data collected by the data acquisition module. The user interface module receives user commands and displays the calculation results from the cloud computing module. The cloud computing module extracts corresponding data from the data storage module and performs corresponding calculations. This solution monitors not only physical resources such as idle equipment and surplus materials, but also human resources by setting human resource indicators, enabling users to gain a more comprehensive understanding of the laboratory's resource situation.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, and in particular to a cloud-based laboratory resource monitoring system. Background Technology

[0002] Cloud platforms provide computing, networking, and storage capabilities. Users can obtain the resources they need directly from the cloud simply by sending a request over the network, without needing to purchase servers or build a data center. Cloud computing is now widely used in resource monitoring.

[0003] For example, the prior art disclosed in CN105761011A is a cloud-based laboratory resource management system, including a management platform and an interactive cloud platform. The management platform includes a user management module and a laboratory management module. The user management module filters and verifies user information and stores the user information in a user database. The laboratory management module filters and verifies laboratory information and stores the laboratory information in a laboratory database. Users access the interactive cloud platform through a terminal and call data in the laboratory database.

[0004] Another typical example is the prior art disclosed in CN109951548B, which discloses a method for managing resources on a cloud platform, including: setting a region under a cloud account as a cloud environment on the cloud management platform, and using the cloud environment to manage the resources of the cloud platform; wherein, the cloud account is the account of the cloud platform.

[0005] Let's look at an existing technology, such as CN108769207B, which discloses a cloud platform resource monitoring method and system. The method includes: dividing the cloud platform resource monitoring process into a basic resource layer, a data acquisition layer, a data processing layer, and a function display layer according to different business logics, with each layer only responsible for the business logic of its own layer; wherein, the basic resource layer includes basic resources in the cloud platform; the data acquisition layer collects and obtains monitoring data and performance data of the basic resources and transmits them to the data processing layer; the data processing layer processes the monitoring data and performance data respectively; different sub-modules of the function display layer receive the corresponding processed data and present the monitoring data in different forms.

[0006] Currently, existing technologies that use cloud platforms to monitor resources only consider the quantity of materials and equipment, resulting in limited information available to users. This invention was developed to address this common problem in the field. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of current systems by proposing a cloud-based laboratory resource monitoring system.

[0008] To overcome the shortcomings of existing technologies, the present invention adopts the following technical solution: a cloud platform-based laboratory resource monitoring system, characterized in that it includes a data acquisition module, a data storage module, a cloud computing module, and a user interface module; the data acquisition module is used to collect relevant information of various resources in each laboratory; the data storage module is used to store the data collected by the data acquisition module; the user interface module is used to receive user instructions and display the calculation results of the cloud computing module; the cloud computing module is used to obtain the corresponding data in the data storage module according to the user instructions and perform corresponding calculations; the user instructions include the user's personal identity authentication instructions and the experimental file instructions related to the experiment that the user wants to execute.

[0009] The data storage module includes a scheduling unit, which is used to schedule corresponding data from the database according to the index information sent by the user interface module and send it to the cloud computing module. The cloud computing module includes an import unit and an execution unit. The import unit is used to receive the corresponding data sent by the scheduling unit according to user instructions. The execution unit is used to calculate the completion time index and human resource index of the target experiment according to the data imported by the import unit. The human resource index is used to characterize the efficiency of the corresponding laboratory in completing the target experiment, and the completion time index is used to characterize the duration of the corresponding laboratory in completing the target experiment.

[0010] Furthermore, the data acquisition module includes a storage unit, a registration unit, and a communication unit. The storage unit is used to store various equipment and materials in the laboratory. The storage unit is equipped with a scanning device, which is used to scan the materials or equipment stored in the storage unit. The data acquisition module obtains the number of available equipment and the number of remaining materials in the laboratory through the storage unit and the scanning device. The registration unit is used to register the number of people in the laboratory and the experimental progress. The communication unit is used to communicate with the communicable devices in the laboratory and collect relevant information of each device.

[0011] Furthermore, the data storage module also includes an index information analysis unit, a classification unit, and an encryption unit. The index information analysis unit is used to analyze the index information sent by the user interface module. The classification unit is used to classify the data collected by the data acquisition module and save it to different databases. The encryption unit is used to encrypt the information stored in the database.

[0012] Furthermore, the user interface module includes an input unit, a display unit, and an index information generation unit. The input unit is used to receive user input and generate user instructions. The display unit is used to display system functions and the calculation results of the cloud computing module. The index information generation unit is used to generate corresponding index information according to user instructions and send it to the data storage module.

[0013] Furthermore, the workflow of the cloud-based laboratory resource monitoring system includes the following steps:

[0014] S1, the user interface module receives user input and generates user instructions, which are then sent to the cloud computing module;

[0015] S2, The index information generation unit generates index information to the data storage module according to user instructions;

[0016] S3, The data storage module extracts data from the database based on the index information and sends it to the cloud computing module;

[0017] S4, the cloud computing module calculates the completion time and human resource indicators for the target experiment.

[0018] S5, the user interface module displays the calculation results of the cloud computing module.

[0019] Furthermore, the data storage module extracts data from the database through the following steps:

[0020] S31, The index information analysis unit identifies the type of index information. If it is the index information corresponding to a personal identity authentication instruction, then S32 is executed; otherwise, S33 is executed.

[0021] S32, the index information is forwarded to the encryption unit. The encryption unit matches the index information with the stored user information and sends the corresponding feedback information to the user interface module. The user continues to input user instructions based on the feedback information. The index information generation unit generates the corresponding index information based on the input user instructions and returns to S31.

[0022] S33, the index information is forwarded to the scheduling unit, and the scheduling unit extracts the data information collected by the data acquisition module from the corresponding database according to the index information;

[0023] S34, the scheduling module packages the data information and its corresponding tags and sends them to the cloud computing module.

[0024] Furthermore, the cloud computing module calculates the completion time and various resource indicators for the target experiment, including the following steps:

[0025] S41, the execution unit uses a text recognition algorithm to identify experimental information in the user's instructions and obtains experimental steps, materials required for the experiment, and equipment required for the experiment;

[0026] S42, The import unit imports the corresponding data from the data storage module into the execution unit;

[0027] S43, the execution unit calculates the comprehensive waiting time index for each laboratory;

[0028] S44, The execution unit calculates the human resource indicators corresponding to the target experiment in each laboratory based on the experimental steps and the laboratory's situation; the target experiment is the experiment that the user needs to perform.

[0029] S45, calculate the completion time index for each laboratory to complete the target experiment based on the waiting time index and human resource index.

[0030] The beneficial effects of this solution are as follows: 1. Compared with existing technologies, in the process of monitoring laboratory resources, the cloud platform not only acquires material resource data, but also human resource data. This enables the laboratory resource monitoring system to monitor not only material resources such as idle equipment and surplus materials, but also human resources by setting human resource indicators, which helps users to have a more comprehensive understanding of the laboratory's resource situation.

[0031] 2. When a user applies to conduct an experiment, the resource monitoring system refers to the status of various resources in the laboratory and calculates the completion time target. This helps users set the latest completion time based on the completion time target and cost, and helps to reasonably set experimental tasks for laboratory personnel and effectively utilize human resources. Attached Figure Description

[0032] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0033] Figure 1 This is a schematic diagram of the structure of the present invention.

[0034] Figure 2 This is a flowchart of the process of the present invention.

[0035] Figure 3 This is a flowchart illustrating how the data storage module of this invention extracts data from the database.

[0036] Figure 4 This is a flowchart illustrating the completion time and resource metrics of the target experiment calculated by the cloud computing module of this invention. Detailed Implementation

[0037] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0038] Example 1: According to Figure 1 , Figure 2 and Figure 3 This embodiment provides a cloud-based laboratory resource monitoring system, including a data acquisition module, a data storage module, a cloud computing module, and a user interface module. The data acquisition module is used to collect relevant information about various resources in each laboratory. The data storage module is used to store the data collected by the data acquisition module. The user interface module is used to receive user instructions and display the calculation results of the cloud computing module. The cloud computing module is used to obtain the corresponding data in the data storage module according to the user instructions and perform corresponding calculations. The user instructions include the user's personal identity authentication instructions and the experimental file instructions related to the experiment that the user wants to execute.

[0039] The data storage module includes a scheduling unit, which is used to schedule corresponding data from the database according to the index information sent by the user interface module and send it to the cloud computing module. The cloud computing module includes an import unit and an execution unit. The import unit is used to receive the corresponding data sent by the scheduling unit according to user instructions. The execution unit is used to calculate the completion time index and human resource index of the target experiment according to the data imported by the import unit.

[0040] Furthermore, the data acquisition module includes a storage unit, a registration unit, and a communication unit. The storage unit is used to store various equipment and materials in the laboratory. The storage unit is equipped with a scanning device, which is used to scan the materials or equipment stored in the storage unit. The data acquisition module obtains the number of available equipment and the number of remaining materials in the laboratory through the storage unit and the scanning device. The registration unit is used to register the number of people in the laboratory and the experimental progress. The communication unit is used to communicate with communicable devices in the laboratory and collect relevant information of each device.

[0041] Specifically, the experimenters register their own information and the experimental progress in the laboratory through the registration unit, and the data acquisition module obtains the number of available personnel in the laboratory and the number of experimental schedules through the registration information in the registration unit.

[0042] Furthermore, the data storage module also includes an index information analysis unit, a classification unit, and an encryption unit. The index information analysis unit is used to analyze the index information sent by the user interface module. The classification unit is used to classify the data collected by the data acquisition module and save it to different databases. The encryption unit is used to encrypt the information stored in the database.

[0043] Specifically, the index information analysis unit analyzes the index information through a set communication protocol.

[0044] Furthermore, the user interface module includes an input unit, a display unit, and an index information generation unit. The input unit is used to receive user input and generate user instructions. The display unit is used to display system functions and the calculation results of the cloud computing module. The index information generation unit is used to generate corresponding index information according to user instructions and send it to the data storage module.

[0045] Furthermore, the workflow of a cloud-based laboratory resource monitoring system includes the following steps:

[0046] S1, the user interface module receives user input and generates user instructions, which are then sent to the cloud computing module;

[0047] S2, The index information generation unit generates index information to the data storage module according to user instructions;

[0048] S3, The data storage module extracts data from the database based on the index information and sends it to the cloud computing module;

[0049] S4, the cloud computing module calculates the completion time of the target experiment and various resource indicators such as human resources indicators;

[0050] S5, the user interface module displays the calculation results of the cloud computing module.

[0051] Furthermore, the data storage module extracts data from the database through the following steps:

[0052] S31, The index information analysis unit identifies the type of index information. If it is the index information corresponding to a personal identity authentication instruction, then S32 is executed; otherwise, S33 is executed.

[0053] S32, the index information is forwarded to the encryption unit. The encryption unit matches the index information with the saved user information and sends the corresponding feedback information to the user interface module. The user continues to input user instructions based on the feedback information. The index information generation unit generates the corresponding index information based on the input user instructions and returns to S31.

[0054] S33, the index information is forwarded to the scheduling unit, and the scheduling unit extracts the data information collected by the data acquisition module from the corresponding database according to the index information;

[0055] S34, the scheduling module packages the data information and its corresponding tags and sends them to the cloud computing module.

[0056] Furthermore, the cloud computing module calculates the completion time and various resource indicators for the target experiment, including the following steps:

[0057] S41, the execution unit uses a text recognition algorithm to identify experimental information in the user's instructions and obtains experimental steps, materials required for the experiment, and equipment required for the experiment;

[0058] S42, The import unit imports the corresponding data from the data storage module into the execution unit;

[0059] S43, the execution unit calculates the comprehensive waiting time index for each laboratory;

[0060] Specifically, the calculation method for the comprehensive waiting time index is as follows:

[0061] t wait1 =min[wait1, wait2,...wait i ]

[0062] t wait2 =max[Wait1, Wait2,...Wait j ]

[0063] t wait3 =max[t wait2 ,t wait1 ]

[0064] Where wait1 is the waiting time for the first set of idle devices, wait2 is the waiting time for the second set of idle devices, and wait... i t represents the waiting time for the i-th set of non-idle devices, where i is the number of devices in use (registered devices) in the registration unit, and the waiting time is the time remaining until the end of the experiment currently being performed by the device; wait1 The waiting time indicator for the equipment; t wait2Wait1 represents the replenishment time indicator for the first type of material that is out of stock, Wait2 represents the replenishment time indicator for the second type of material that is out of stock, and Wait3 represents the replenishment time indicator for the second type of material that is out of stock. j t represents the replenishment time index for the j-th type of out-of-stock material, where j is the total number of out-of-stock material types. This can be obtained by comparing the number of remaining materials registered in the registration unit with the required material count identified by the text recognition algorithm. The replenishment time index for each out-of-stock material is the average historical replenishment time. The replenishment time is the time from user confirmation of replenishment to material arrival. wait3 The higher the overall waiting time index, the longer the waiting time before the operable experiment is possible; the equipment and materials mentioned above in this plan are all required for the experiment that the user intends to operate.

[0065] Preferably, this solution allows for replenishment by linking laboratory resource monitoring system users with material suppliers through a cloud platform, enabling suppliers to obtain supply information and provide materials immediately when users confirm replenishment in the system.

[0066] S44, the execution unit calculates the human resource indicators corresponding to the target experiment in each laboratory based on the experimental content and the laboratory's situation; the target experiment is the experiment that the user needs to perform.

[0067] Specifically, the human resource indicators corresponding to the target experiment in the laboratory can be calculated using the following formula:

[0068]

[0069] Among them, HU XY Let X be the human resource indicator corresponding to the target experiment in the current laboratory, and Y be the number of available experimental personnel in the laboratory who have performed experiments of the same type as the target experiment. x For the xth idle experimenter, set is the number of times this type of experiment has been performed in the past. xy This refers to the time limit for the xth idle experimenter to perform this type of experiment for the yth time in the past. The time limit is set by the user for the current experiment, and is the difference between the user-set latest completion time and the experiment start time. xy The actual experimental time corresponding to the yth time in the past performed by the xth idle experimenter;

[0070] Specifically, the experiment type can be obtained by identifying the experiment title using a text recognition algorithm and classifying it according to keywords;

[0071] S45. Calculate the completion time index for each laboratory to complete the target experiment based on the comprehensive waiting time index and human resource index.

[0072] Specifically, the completion time target can be calculated using the following formula:

[0073]

[0074] Wherein, GJ is the completion time index for completing the target experiment, which characterizes the estimated time required from the user inputting the user command to the completion of the experiment. The larger the completion time index, the longer the estimated time. M is the number of times this type of experiment has been completed in past records, and T... m HU represents the actual time taken in the past records for executing this type of experiment for the mth time. m This refers to the human resource indicator corresponding to the m-th execution of this type of experiment in the past records;

[0075] A represents the number of experimenters who performed this type of experiment in the m-th past record, B a Let set be the number of times the a-th experimenter, who performed this type of experiment for the m-th time in the past records, had performed this type of experiment before this record. ab This refers to the time limit for the b-th previous execution of this type of experiment by the a-th experimenter. The time limit is set by the user for the current experiment, and is the difference between the user-set latest completion time and the experiment start time. ab This refers to the actual experimental time corresponding to the bth previous execution of this type of experiment by the a-th experimenter;

[0076] Specifically, the user interface module displays the minimum completion time index for each laboratory and its corresponding laboratory. By selecting the laboratory to conduct the experiment, users can achieve experimental results in a shorter time. Users can also use this index to predict how quickly the experiment will end, thus providing a reference for setting the latest completion time. Setting the latest completion time based on this index can improve the work efficiency of experimental personnel when the time is reasonable.

[0077] The beneficial effects of this solution are: 1. Compared with existing technologies, in the process of monitoring laboratory resources, not only are physical resources such as idle equipment and surplus materials monitored, but human resources are also monitored by setting human resource indicators, which helps users to have a more comprehensive understanding of the laboratory's resource situation.

[0078] 2. When a user applies to conduct an experiment, the completion time target is calculated by referring to the resources available in the laboratory. This helps the user set the latest completion time based on the completion time target and cost, and also helps to rationally set experimental tasks for laboratory personnel and effectively utilize human resources.

[0079] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them. It also includes a resource scheduling method based on resource monitoring, comprising the following steps:

[0080] STEP1: The user interface module receives resource scheduling instructions sent by the user.

[0081] STEP2: The data storage module extracts data from the database and sends it to the cloud computing module based on the index information corresponding to the resource scheduling instructions.

[0082] STEP3: The cloud computing module obtains the target laboratory and its resource allocation weight based on the resource scheduling instructions.

[0083] Specifically, the target laboratory is the laboratory that the user selects to conduct the experiment;

[0084] STEP4: The cloud computing module obtains the human resource scheduling parameters of the target laboratory and the human resources to be scheduled based on the data extracted from the database.

[0085] Specifically, the human resource scheduling parameters for the target laboratory and the human resources that need to be scheduled can be obtained through the following formula:

[0086]

[0087] RE and RES satisfy the following equation:

[0088] RES≥RE max1 +……RE maxn

[0089] and

[0090] RES <RE max1 +……RE maxN N = n + 1

[0091] Among them, RE x Here, RES represents the human resource parameter corresponding to the xth available experimental personnel, and RES represents the current laboratory's human resource scheduling parameter. A larger RES indicates a greater weight of resources that the current laboratory needs to allocate from other laboratories. The current laboratory is the laboratory where the experiment is to be conducted. LEVEL represents the resource allocation weight set by the user. This weight is set by the user through resource scheduling commands and represents the degree to which the user wants to allocate resources to the allocated resource target. A larger weight indicates a greater degree of allocation. The resource allocation weight ranges from 1 to 2, with preferred values ​​including 1.2, 1.4, 1.6, 1.8, and 2. The specific value is set by the user. xyThis refers to the time limit for the xth idle experimenter to perform this type of experiment for the yth time in the past. The time limit is set by the user for the current experiment, and is the difference between the user-set latest completion time and the experiment start time. xy The actual experimental time corresponding to the yth time in the past performed by the xth idle experimenter;

[0092] RE max1 RE represents the maximum value of the corresponding human resource parameter among the available laboratory personnel. maxn RE represents the nth largest value of the human resource parameter among available laboratory personnel. maxN This is the Nth largest value of the corresponding human resource parameter among the available laboratory personnel, i.e., RE max1 …RE maxn …RE maxN The parameters are human resources sorted from largest to smallest; n represents the number of personnel that should be transferred from other laboratories, with each RE corresponding to one researcher from another laboratory. max1 To RE maxn This allows us to determine which personnel should be transferred from other laboratories to the current laboratory;

[0093] STEP5: The cloud computing module generates corresponding tilt instructions based on the calculation results and sends the tilt instructions to each person in the human resources that need to be scheduled.

[0094] Specifically, the cloud computing module arranges and combines the human resource parameters corresponding to the idle experimental personnel in other laboratories to obtain a combination that is closest to RES. Through this combination, the identity of the experimental personnel who should be dispatched to the laboratory where the experiment will be conducted can be obtained. Based on the identity information of the experimental personnel, a tilt instruction is generated. After receiving the tilt instruction, the experimental personnel will go to the laboratory where the experiment will be conducted, thereby realizing the scheduling of human resources.

[0095] The beneficial effects of this embodiment are: by setting human resource scheduling parameters and human resource parameters, the optimal choice of human resource scheduling can be obtained, that is, the completion time index of the experiment can be reduced according to the user's needs, while avoiding the waste of human resources caused by randomly selecting experimental personnel for scheduling.

[0096] The above-disclosed content is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the description and drawings of the present invention are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops. The above units are merely examples, and those skilled in the art can adopt corresponding units according to actual needs when implementing this solution.

Claims

1. A cloud-based laboratory resource monitoring system, characterized in that, It includes a data acquisition module, a data storage module, a cloud computing module, and a user interface module. The data acquisition module is used to collect relevant information about various resources in each laboratory. The data storage module is used to store the data collected by the data acquisition module. The user interface module is used to receive user instructions and display the calculation results of the cloud computing module. The cloud computing module is used to obtain the corresponding data in the data storage module according to the user instructions and perform corresponding calculations. The user instructions include the user's personal identity authentication instructions and the experimental file instructions related to the experiment that the user wants to execute. The data storage module includes a scheduling unit, which is used to schedule corresponding data from the database according to the index information sent by the user interface module and send it to the cloud computing module. The cloud computing module includes an import unit and an execution unit. The import unit is used to receive the corresponding data sent by the scheduling unit according to the user instruction. The execution unit is used to calculate the completion time index and human resource index of the target experiment according to the data imported by the import unit. Calculating the completion time and human resource indicators for the target experiment includes the following steps: S41, the execution unit uses a text recognition algorithm to identify experimental information in the user's instructions and obtains experimental steps, materials required for the experiment, and equipment required for the experiment; S42, The import unit imports the corresponding data from the data storage module into the execution unit; S43, the execution unit calculates the comprehensive waiting time index for each laboratory; S44, The execution unit calculates the human resource indicators corresponding to the target experiment in each laboratory based on the experimental steps and the laboratory's situation; the target experiment is the experiment that the user needs to perform. S45, Calculate the completion time index for each laboratory to complete the target experiment based on the comprehensive waiting time index and human resource index; The calculation method for the comprehensive waiting time index is as follows: ； ； ； in, The waiting time for the first set of idle equipment. The waiting time for the second set of idle equipment. The waiting time is the waiting time for the i-th set of idle devices, where i is the number of devices in use in the registration unit, and the waiting time is the time remaining until the end of the experiment currently being performed by the device. The waiting time indicator for the equipment; For material replenishment time indicators, This is the replenishment time indicator for the first type of material that is out of stock. The replenishment time indicator for the second type of out-of-stock material The replenishment time index for the j-th type of out-of-stock material, where j is the total number of out-of-stock material types, the replenishment time index for each out-of-stock material is the average of historical replenishment times, and the replenishment time is the time from user confirmation of replenishment to material arrival. The larger the overall waiting time index, the longer the waiting time is before the operable experiment can be conducted. The human resource indicators corresponding to the target experiment in the laboratory can be calculated using the following formula: ； in, Let X be the human resource indicator corresponding to the target experiment in the current laboratory, and let X be the number of available experimental staff in the laboratory who have performed experiments of the same type as the target experiment. This represents the number of times the xth idle experimenter has performed this type of experiment in the past. This refers to the time limit for the xth idle experimenter to perform this type of experiment in the past yth time. The time limit is set by the user for the current experiment, and it is the difference between the latest completion time set by the user and the start time of the experiment. The actual experimental time corresponding to the yth time in the past performed by the xth idle experimenter; The completion time target can be calculated using the following formula: ； ； Wherein, GJ represents the completion time index for completing the target experiment. This completion time index is used to characterize the estimated time required from the user inputting the user command to the completion of the experiment. The larger the completion time index, the longer the estimated time. This represents the number of times this type of experiment has been completed in the past. This represents the actual time taken to execute this type of experiment in the past, compared to the m-th execution. This refers to the human resource indicator corresponding to the m-th execution of this type of experiment in the past records; A represents the number of experimenters who performed this type of experiment in the m-th past record. This represents the number of times the a-th experimenter, who performed this type of experiment for the m-th time in past records, had performed this type of experiment before this record. This refers to the time limit for the b-th previous execution of this type of experiment by the a-th experimenter. The time limit is set by the user for the current experiment, and is the difference between the user-set latest completion time and the experiment start time. This represents the actual experimental time corresponding to the bth previous execution of this type of experiment by the a-th experimenter.

2. The cloud-based laboratory resource monitoring system according to claim 1, characterized in that, The data acquisition module includes a storage unit, a registration unit, and a communication unit. The storage unit stores various equipment and materials in the laboratory and is equipped with a scanning device. The scanning device scans the materials or equipment stored in the storage unit. The data acquisition module obtains the number of available equipment and the amount of remaining materials in the laboratory through the storage unit and the scanning device. The registration unit registers the number of people in the laboratory and the experimental progress. The communication unit communicates with communicable devices in the laboratory and collects relevant information about each device.

3. The cloud-based laboratory resource monitoring system according to claim 2, characterized in that, The data storage module further includes an index information analysis unit, a classification unit, and an encryption unit. The index information analysis unit is used to analyze the index information sent by the user interface module. The classification unit is used to classify the data collected by the data acquisition module and save it to different databases. The encryption unit is used to encrypt the information stored in the database.

4. The cloud-based laboratory resource monitoring system according to claim 3, characterized in that, The user interface module includes an input unit, a display unit, and an index information generation unit. The input unit is used to receive user input and generate user instructions. The display unit is used to display system functions and the calculation results of the cloud computing module. The index information generation unit is used to generate corresponding index information according to user instructions and send it to the data storage module.

5. A cloud-based laboratory resource monitoring system according to claim 4, characterized in that, The workflow of a cloud-based laboratory resource monitoring system includes the following steps: S1, the user interface module receives user input and generates user instructions, which are then sent to the cloud computing module; S2, The index information generation unit generates index information to the data storage module according to user instructions; S3, The data storage module extracts data from the database based on the index information and sends it to the cloud computing module; S4, the cloud computing module calculates the completion time and human resource indicators for the target experiment. S5, the user interface module displays the calculation results of the cloud computing module.

6. The cloud-based laboratory resource monitoring system according to claim 5, characterized in that, The data storage module extracts data from the database through the following steps: S31, The index information analysis unit identifies the type of index information. If it is the index information corresponding to a personal identity authentication instruction, then S32 is executed; otherwise, S33 is executed. S32, the index information is forwarded to the encryption unit. The encryption unit matches the index information with the stored user information and sends the corresponding feedback information to the user interface module. The user continues to input user instructions based on the feedback information. The index information generation unit generates the corresponding index information based on the input user instructions and returns to S31. S33, the index information is forwarded to the scheduling unit, and the scheduling unit extracts the data information collected by the data acquisition module from the corresponding database according to the index information; S34, the scheduling module packages the data information and its corresponding tags and sends them to the cloud computing module.

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