Methods, devices, equipment, media, and procedures for the allocation of laboratory resources

Abstract

This application relates to a method, apparatus, equipment, medium, and program product for scheduling laboratory resources. The method acquires current and historical resource data for various resources within the laboratory. Then, based on the current resource data and dependencies between resources, it performs a risk assessment on each resource to obtain the current risk assessment result. Next, based on the current risk assessment results and historical resource data, it performs a health status assessment of the laboratory to obtain the current health status assessment result. Finally, based on the current health status assessment result, it schedules the various resources within the laboratory to obtain the scheduling result. This method, by comprehensively integrating data on laboratory equipment, personnel, and materials for dynamic resource allocation, can improve the management efficiency of laboratory resources.

Description

Technical Field

[0001] This application relates to the field of resource management technology, and in particular to a method, apparatus, equipment, medium, and program product for scheduling laboratory resources. Background Technology

[0002] With the increasing demands of scientific research and laboratory management, the efficient utilization and precise management of laboratory resources have become crucial issues for research institutions, corporate laboratories, and educational and scientific research institutions. As the core location for scientific research and technological development, laboratories contain a wealth of resources, such as equipment, personnel, and experimental materials. Therefore, the effective management and rational utilization of laboratory resources directly impacts laboratory efficiency, research outcomes, and resource efficiency.

[0003] However, the laboratory resource management methods described in the relevant technologies suffer from low management efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, medium, and program product for scheduling laboratory resources that can improve resource management efficiency in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for scheduling laboratory resources, the method comprising:

[0006] Acquire current and historical resource data for various resources within the laboratory; resources include experimental equipment, personnel, and materials.

[0007] Based on the current resource data of each resource and the dependency information between each resource, a risk assessment is performed on each resource to obtain the risk assessment result of each resource at the current moment;

[0008] Based on the current risk assessment results and historical resource data of each resource, the health status of the laboratory is assessed to obtain the current health status assessment results of the laboratory.

[0009] Based on the current health status assessment results of the laboratory, various resources within the laboratory are allocated, and the allocation results are obtained.

[0010] In some embodiments, a risk assessment is performed on each resource based on its current resource data and the dependency information between resources, to obtain the risk assessment result of each resource at the current moment, including:

[0011] The current resource data of each resource is non-linearly quantized to obtain the quantized data of each resource;

[0012] For each target resource among all resources, the relative weight information between the target resource and the remaining resources is determined based on the quantitative data of the target resource, the quantitative data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources.

[0013] Based on the quantitative data of the remaining resources and the relative weight information between the target resource and the remaining resources, a risk assessment is conducted on the target resource to obtain the risk assessment result of the target resource at the current moment.

[0014] In some embodiments, the dependency information includes similarity sensitivity and weight sensitivity. Based on the quantized data of the target resource, the quantized data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources, the relative weight information between the target resource and the remaining resources is determined, including:

[0015] The weight sensitivity information is determined based on the weight sensitivity, the quantitative data of the target resource, and the quantitative data of the remaining resources;

[0016] Based on weight sensitivity information and similarity sensitivity, the relative weight information between the target resource and the remaining resources is determined.

[0017] In some embodiments, a risk assessment is performed on the target resource based on the quantified data of the remaining resources and the relative weight information between the target resource and the remaining resources, to obtain the risk assessment result of the target resource at the current moment, including:

[0018] For each of the remaining resources, the relative weight information corresponding to the target resource is multiplied by the quantitative data of the other resources to obtain the risk score of the target resource relative to the other resources;

[0019] The risk assessment result of the target resource is obtained by summing the risk scores of the target resource against all other remaining resources.

[0020] In some embodiments, the laboratory's health status is assessed based on the current risk assessment results and historical resource data for each resource, resulting in the laboratory's current health status assessment results, including:

[0021] Historical risk assessment results for each resource and historical health status assessment results for the laboratory were extracted from historical resource data.

[0022] Based on the current risk assessment results and the historical risk assessment results of each resource, determine the risk change of each resource;

[0023] Based on the risk changes of each resource and the historical health status assessment results of the laboratory, a health status assessment of the laboratory is conducted to obtain the current health status assessment result of the laboratory.

[0024] In some embodiments, resource scheduling is performed on various resources within the laboratory based on the laboratory's current health status assessment results, resulting in scheduling outcomes including:

[0025] Based on the health score in the laboratory's current health status assessment results, the preset health threshold, and the initial weights of each resource, determine the optimal decision value for each resource at the current moment;

[0026] Based on the current optimization decision value, health score, and target health threshold of each resource, determine the next optimization decision value for each resource.

[0027] Based on the optimized decision value of each resource at the next moment and the preset configuration strategy, resource scheduling is performed on various resources in the laboratory to obtain the scheduling results.

[0028] Secondly, this application also provides a laboratory resource scheduling device, the device comprising:

[0029] The acquisition module is used to acquire current and historical resource data of various resources in the laboratory; resources include experimental equipment, experimental personnel, and experimental materials.

[0030] The risk assessment module is used to assess the risk of each resource based on its current resource data and the dependency information between resources, and to obtain the risk assessment result of each resource at the current moment.

[0031] The health assessment module is used to assess the health status of the laboratory based on the current risk assessment results and historical resource data of each resource, and obtain the current health status assessment results of the laboratory.

[0032] The scheduling module is used to schedule various resources within the laboratory based on the laboratory's current health status assessment results, and obtain the scheduling results.

[0033] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0034] Acquire current and historical resource data for various resources within the laboratory; resources include experimental equipment, personnel, and materials.

[0035] Based on the current resource data of each resource and the dependency information between each resource, a risk assessment is performed on each resource to obtain the risk assessment result of each resource at the current moment;

[0036] Based on the current risk assessment results and historical resource data of each resource, the health status of the laboratory is assessed to obtain the current health status assessment results of the laboratory.

[0037] Based on the current health status assessment results of the laboratory, various resources within the laboratory are allocated, and the allocation results are obtained.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0039] Acquire current and historical resource data for various resources within the laboratory; resources include experimental equipment, personnel, and materials.

[0040] Based on the current resource data of each resource and the dependency information between each resource, a risk assessment is performed on each resource to obtain the risk assessment result of each resource at the current moment;

[0041] Based on the current risk assessment results and historical resource data of each resource, the health status of the laboratory is assessed to obtain the current health status assessment results of the laboratory.

[0042] Based on the current health status assessment results of the laboratory, various resources within the laboratory are allocated, and the allocation results are obtained.

[0043] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, performs the following steps:

[0044] Acquire current and historical resource data for various resources within the laboratory; resources include experimental equipment, personnel, and materials.

[0045] Based on the current resource data of each resource and the dependency information between each resource, a risk assessment is performed on each resource to obtain the risk assessment result of each resource at the current moment;

[0046] Based on the current risk assessment results and historical resource data of each resource, the health status of the laboratory is assessed to obtain the current health status assessment results of the laboratory.

[0047] Based on the current health status assessment results of the laboratory, various resources within the laboratory are allocated, and the allocation results are obtained.

[0048] The aforementioned laboratory resource scheduling method, apparatus, equipment, media, and program products involve acquiring current and historical resource data for various resources within the laboratory. Then, based on the current resource data and dependencies between resources, a risk assessment is performed on each resource to obtain its current risk assessment result. Next, based on the current risk assessment results and historical resource data, a health status assessment of the laboratory is conducted to obtain its current health status result. Finally, based on the current health status assessment result, resource scheduling is performed on the various resources within the laboratory to obtain the scheduling result. Resources include experimental equipment, personnel, and materials. This method comprehensively integrates data on laboratory equipment, personnel, and materials, and achieves continuous optimization of resource allocation based on dynamic health status assessment, effectively avoiding resource waste and maximizing resource utilization. This method can automatically generate and dynamically adjust resource allocation schemes, improving the overall operational efficiency of the laboratory. Furthermore, by linking the risk assessment of individual resources with the overall laboratory health status assessment, potential risks such as equipment failure, personnel overload, and material shortages can be identified in advance, allowing for timely adjustments to resource allocation, thereby improving the management efficiency of laboratory resources and ensuring smooth laboratory operation. Attached Figure Description

[0049] Figure 1 These are internal structural diagrams of the computer device in some embodiments;

[0050] Figure 2 This is one of the flowcharts illustrating the scheduling method for laboratory resources in some embodiments;

[0051] Figure 3 This is a second flowchart illustrating the scheduling method for laboratory resources in some embodiments;

[0052] Figure 4 This is the third flowchart illustrating the laboratory resource scheduling method in some embodiments;

[0053] Figure 5 This is a fourth flowchart illustrating the laboratory resource scheduling method in some embodiments;

[0054] Figure 6 This is the fifth flowchart illustrating the laboratory resource scheduling method in some embodiments;

[0055] Figure 7 This is a flowchart illustrating the scheduling method for laboratory resources in some embodiments, number six.

[0056] Figure 8 This is a structural block diagram of a laboratory resource scheduling device in some embodiments. Detailed Implementation

[0057] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0058] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0059] In the embodiments of this application, the term "at least one" means one or more. For example, at least one of A, B and C can represent six situations: A exists alone, B exists alone, C exists alone, A and B exist simultaneously, A and C exist simultaneously, B and C exist simultaneously, and A, B and C exist simultaneously.

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0061] With the increasing demands of scientific research and laboratory management, the efficient utilization and precise management of laboratory resources have become crucial issues for research institutions, corporate laboratories, and educational and scientific research institutions. Laboratories, as core locations for scientific research and technological development, contain a wealth of resources, such as equipment, personnel, and experimental materials. The effective allocation and rational utilization of these resources directly affect laboratory efficiency, research outcomes, and the economic viability of resources. Traditional laboratory resource management typically relies on manual recording and simple management systems, lacking real-time monitoring and intelligent scheduling, leading to resource waste, low efficiency, and potential safety risks. Furthermore, laboratory health assessments often focus on simple resource usage frequency and equipment operating status monitoring, lacking multi-dimensional and dynamic health evaluation models. This results in inaccurate assessments, hindering the provision of scientific and precise optimization decisions. Therefore, there is an urgent need for an innovative method that can integrate various laboratory resource data, accurately assess laboratory health, and optimize resource allocation through intelligent decision-making to improve laboratory efficiency and management levels.

[0062] Existing technologies suffer from at least the following technical problems: They lack real-time performance and automation, leading to data lag and inconsistency, neglecting the dynamic interactions between resources, and failing to accurately reflect the actual operating status of the laboratory; existing technologies often lack optimization mechanisms for resource scheduling and allocation, failing to effectively combine resource health and utilization efficiency for dynamic adjustments, resulting in resource waste or inefficient use; and they struggle to cope with the nonlinear relationships and complexities in the laboratory environment, leading to poor optimization results. Therefore, the aforementioned laboratory resource management methods suffer from low management efficiency.

[0063] In view of this, embodiments of this application propose a method, apparatus, equipment, medium, and program product for scheduling laboratory resources. By comprehensively integrating data on laboratory equipment, personnel, and materials, and dynamically allocating resources, the management efficiency of laboratory resources can be improved.

[0064] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.

[0065] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0066] In some embodiments, the laboratory resource scheduling method provided in this application can be applied to, for example... Figure 1 The computer device shown can be a terminal or a server, and its internal structure diagram can be as follows: Figure 1As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for scheduling laboratory resources. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0067] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0068] In some embodiments, such as Figure 2 As shown, a method for scheduling laboratory resources is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0069] S201, acquire current and historical resource data of various resources in the laboratory.

[0070] Resources include experimental equipment, personnel, and materials. Current resource data refers to the set of data collected in real-time or near real-time at the current evaluation moment (time t), reflecting the immediate status and usage of various resources. Historical resource data refers to similar resource data collected and stored over one or more historical time periods prior to the current moment. Resource data includes experimental equipment data, personnel data, and material data. Experimental equipment data includes at least one of the following: operating status, working hours, fault records, energy consumption, and reservation usage rate; personnel data may include at least one of the following: workload, work efficiency, on-duty status, and skill qualifications; material data may include at least one of the following: inventory level, consumption rate, expiration date, and requisition records.

[0071] In this embodiment, the computer equipment can automatically acquire current and historical resource data through various data acquisition terminals and management systems deployed in the laboratory environment. Specifically, for experimental equipment, the computer equipment can collect its power-on / off status, operating parameters, cumulative working time, and fault alarm logs in real time through IoT sensors, equipment controller interfaces, or dedicated equipment monitoring systems. For laboratory personnel, the computer equipment can obtain their current task list, task progress, attendance status, and historical work efficiency statistics through laboratory information management systems, access control card swipe records, and task assignment and reporting systems. For experimental materials, the computer equipment can track their inbound, outbound, current inventory, and consumption history in real time through intelligent warehouse management systems and material management cabinets with RFID or QR code labels. Optionally, all collected heterogeneous data can be cleaned and formatted, and the processed resource data can be stored in a unified resource database to form a structured dataset for subsequent analysis and processing.

[0072] S202, based on the current resource data of each resource and the dependency information between each resource, perform a risk assessment on each resource and obtain the risk assessment result of each resource at the current moment.

[0073] Dependency information is used to characterize the degree of mutual influence between different resource states, that is, the mutual influence and constraint relationships between different categories of resources in terms of function, efficiency, or state. Risk assessment results are used to represent the potential risk level faced by a single resource at the current moment due to its own state and the states of other related resources (i.e., the probability and degree that it may have a negative impact on the overall operational efficiency, stability, or safety of the laboratory).

[0074] In this embodiment, after obtaining the current resource data of each resource and the dependency information between resources based on the above steps, the computer device can perform risk assessment on each resource by combining the embedding method of graph neural network and resource association graph to obtain the risk assessment result of each resource at the current moment. Specifically, the computer device can abstract the experimental equipment, personnel, and materials in the laboratory as nodes in a graph structure, and construct a resource association graph based on predefined or dynamically learned edges between nodes according to physical connections, usage relationships, or logical affiliations. Then, the current resource data of each resource is encoded and used as the initial feature vector of the corresponding node. Next, the resource association graph and the initial node features are input into a pre-trained graph neural network model; the model updates the feature representation of each node through multiple rounds of message passing and feature aggregation, so that it integrates the state information of itself and its neighboring resources. Finally, the updated feature vector of each node is calculated and normalized by a fully connected layer, and a scalar value is output, which is the risk assessment result of the corresponding resource at the current moment.

[0075] S203. Based on the current risk assessment results and historical resource data of each resource, conduct a health status assessment of the laboratory to obtain the current health status assessment results of the laboratory.

[0076] The health status assessment result is a comprehensive quantitative score used to characterize the overall operational health status, efficiency level, and robustness of the laboratory at the assessment point in time. It not only reflects the static aggregation of risks of various resources at the current moment, but also reflects the dynamic trend of health status by incorporating historical data.

[0077] In this embodiment, after obtaining the current risk assessment results and historical resource data for each resource based on the above steps, the computer device can perform a health status assessment of the laboratory based on a deep assessment method that integrates multi-dimensional risk features, thus obtaining the current health status assessment result of the laboratory. Specifically, the computer device can group the current risk assessment results of each resource according to resource category (equipment, personnel, materials) to form multiple risk feature vectors. Then, it extracts the time series of recent risk assessment results for each resource from historical resource data, calculates their statistical characteristics (such as mean, variance, and trend), and concatenates them with the current risk feature vector to form an enhanced feature vector. Next, the enhanced feature vector is input into a trained deep assessment network, which consists of multiple fully connected layers and nonlinear activation layers, used to learn a nonlinear mapping from complex risk features to overall health status. Finally, the output layer of the deep assessment network outputs the current health status assessment result of the laboratory.

[0078] S204. Based on the current health status assessment results of the laboratory, resource scheduling is performed on various resources within the laboratory to obtain the scheduling results.

[0079] The scheduling results are specific adjustment plans for resources such as experimental equipment, personnel and materials in the laboratory. These plans may take the form of equipment task allocation plans, personnel work task adjustments, and material procurement or allocation suggestions. The goal is to optimize resource allocation to improve the overall health and operational efficiency of the laboratory.

[0080] In this embodiment, the computer device can use the current health status assessment result of the laboratory as one of the core optimization objectives, while defining other optimization objectives, such as minimizing resource usage costs and minimizing task completion time, and setting constraints such as the capacity of various resources, man-hours, and material inventory. Then, using the current state of each resource, risk assessment results, and health status assessment results as input parameters, a mathematical optimization model containing multiple objectives and constraints is constructed. Next, a multi-objective evolutionary algorithm or linear programming solver is used to solve the model, finding a set of objective solutions that achieves comprehensive optimization of multiple objectives while satisfying all constraints. Finally, an optimal solution is selected from the objective solution set according to a preset strategy (such as prioritizing health). This solution contains the suggested configuration amount or task allocation scheme for each resource, which is then converted into executable scheduling instructions to obtain the final scheduling result.

[0081] The laboratory resource scheduling method provided in this application acquires current and historical resource data of various resources within the laboratory. Then, based on the current resource data and dependencies between resources, a risk assessment is performed on each resource to obtain its current risk assessment result. Next, based on the current risk assessment results and historical resource data, a health status assessment of the laboratory is performed to obtain its current health status assessment result. Finally, based on the current health status assessment result, resource scheduling is performed on the various resources within the laboratory to obtain the scheduling result. Resources include experimental equipment, personnel, and materials. This method comprehensively integrates data on laboratory equipment, personnel, and materials, and achieves continuous optimization of resource allocation based on dynamic health status assessment, thereby effectively avoiding resource waste and maximizing resource utilization. This method can automatically generate and dynamically adjust resource allocation schemes, improving the overall operational efficiency of the laboratory. Simultaneously, by linking the risk assessment of individual resources with the overall laboratory health status assessment, potential risks such as equipment failure, personnel overload, and material shortages can be identified in advance, allowing for timely adjustments to resource allocation, thereby improving the management efficiency of laboratory resources and ensuring smooth laboratory operation.

[0082] In some embodiments, a specific implementation method is also provided for performing risk assessments on each resource to obtain the risk assessment results of each resource at the current moment, such as... Figure 3As shown, the phrase "based on the current resource data of each resource and the dependency information between resources, perform a risk assessment on each resource to obtain the risk assessment result of each resource at the current moment" in S202 above includes:

[0083] S301 performs non-linear quantization on the current resource data of each resource to obtain the quantized data of each resource.

[0084] Nonlinear quantization refers to the process of converting the raw, heterogeneous state data (such as working hours, workload, and inventory) of different resources (such as experimental equipment, personnel, and materials) into unified and comparable standardized values ​​through a pre-defined nonlinear mathematical function. The quantified data is the standardized value obtained after this conversion, which eliminates the dimensional differences of the original data and better reflects the essential impact of resource status on laboratory health.

[0085] In this embodiment, to ensure data consistency and comparability, all collected data undergoes preliminary standardization before entering the data processing stage, unifying different data sources and formats to form a unified resource database. The goal of laboratory resource integration is to map various resource data (including equipment, personnel, experimental materials, etc.) into standardized quantitative indicators through unified data collection and integration methods, providing basic data support for health assessment and optimization decisions.

[0086] After obtaining the current resource data of each resource in the laboratory, the computer equipment can perform non-linear quantization on the current resource data of each resource to obtain the quantized data of each resource. Specifically, for each type of resource (e.g., the i-th resource), the raw data of the i-th resource at the current time t can be obtained first. Then, the non-linear mapping function pre-defined for this type of resource is called. After processing, the quantified data of the i-th resource is obtained. This nonlinear mapping function can be expressed by the following relation:

[0087]

[0088] in, Let be the quantized data of the i-th resource at time t. This refers to the current resource data of the i-th resource (e.g., equipment operating time, personnel work efficiency, material consumption, etc.). Let be the weight coefficient of the i-th resource, representing the degree of influence of the i-th resource on the overall evaluation, which is obtained through expert experience. This represents the dynamic variation range of the i-th resource, indicating its fluctuation range over time. It can be calculated based on the variance or standard deviation of the time series data. During the mapping process, the mapping function uses different transformation methods depending on the resource type to adapt to the dynamic variation characteristics of the resource.

[0089] S302, for each target resource among all resources, determine the relative weight information between the target resource and the remaining resources based on the quantitative data of the target resource, the quantitative data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources.

[0090] In this context, the target resource is the single resource currently being evaluated. The remaining resources are all other resources in the laboratory besides the target resource. Dependency information describes the nature and strength of the interaction between the target resource and any of the remaining resources. Relative weight information is a quantifiable value representing the degree to which the target resource is affected by the state of each remaining resource.

[0091] In this embodiment, the computer device can use the resource being evaluated as the target resource, and then evaluate the relative weight information between the target resource and the remaining resources. Specifically,

[0092] First, quantitative data for the target resource and all remaining resources are acquired. Then, matching is performed according to a predefined resource association rule base, which defines the conditions for dependencies between different resource categories (e.g., equipment A and material X, personnel P and equipment B) and their corresponding basic weight coefficients. Next, the quantitative data similarity between the target resource and each remaining resource that meets the association conditions is calculated. This can be achieved by calculating the reciprocal of the difference between their quantitative data or by using a cosine similarity algorithm. Finally, the basic weight coefficient defined in the association rule base is multiplied by the calculated similarity, and the result is used as the final relative weight information between the target resource and the remaining resource.

[0093] Optionally, the aforementioned dependency information includes similarity sensitivity and weight sensitivity, based on which, such as Figure 4 As shown, S302 above includes:

[0094] S3021, determine the weight sensitivity information based on the weight sensitivity, the quantitative data of the target resource, and the quantitative data of the remaining resources.

[0095] S3022, Determine the relative weight information between the target resource and the remaining resources based on weight sensitivity information and similarity sensitivity.

[0096] Weight sensitivity is a parameter in dependency information that controls the strength of the impact of quantitative data differences between two resources on the final weight calculation, reflecting the "amplification" or "reduction" effect of differences. Similarity sensitivity is another parameter in dependency information that controls the overall range or decay rate of the influence of resource similarity (or difference) on the weight value.

[0097] In this embodiment, the computer device can input weight sensitivity, similarity sensitivity, quantified data of the target resource, and quantified data of the remaining resources into a preset relative weight function for calculation to obtain the relative weight information between the target resource and the remaining resources. This preset relative weight function can be expressed by the following formula:

[0098]

[0099] in, This represents the relative weight information between the target resource (the i-th resource) and the remaining resources (the j-th resource) at time t, reflecting the interdependence between the two resources. This represents the sensitivity of the range of influence between the i-th resource and the j-th resource to similarity (i.e., similarity sensitivity), and is used to control the degree of influence of the similarity between resources on the weights. To control the sensitivity of the difference between the i-th and j-th resources to the weight calculation (i.e., weight sensitivity), it reflects the degree of influence of changes in the differences between resources on the final weight. Let be the quantized data of the i-th resource at time t. This represents the quantized data of the j-th resource at time t. This indicates weight-sensitive information.

[0100] S303, based on the quantitative data of the remaining resources and the relative weight information between the target resource and the remaining resources, conduct a risk assessment of the target resource to obtain the risk assessment result of the target resource at the current moment.

[0101] Among them, the risk assessment result is a comprehensive risk score for the target resource, reflecting the level of risk that the target resource may cause not only based on its own state, but also based on its dynamic dependence on all other resources in the laboratory.

[0102] In this embodiment, a computer device can perform risk assessment on a target resource based on a neural network with an attention mechanism, using quantified data of remaining resources and relative weight information between the target resource and the remaining resources, to obtain the current risk assessment result of the target resource. Specifically, firstly, the quantified data of all remaining resources are combined into a feature vector, and the relative weight information between the target resource and each remaining resource is used as the basis for attention scoring. Then, the feature vector and relative weight information of the remaining resources are input into an attention calculation layer. This layer assigns different attention weights to the features of each remaining resource according to the relative weight information and performs weighted fusion of all features to generate a comprehensive feature vector that incorporates contextual dependencies. Next, this comprehensive feature vector is input into a fully connected neural network layer for processing. Finally, the fully connected neural network layer outputs a scalar value, which, after being processed by an activation function, serves as the current risk assessment result of the target resource.

[0103] Optional, such as Figure 5 As shown, S303 above includes:

[0104] S3031, for each of the remaining resources, multiply the relative weight information corresponding to the target resource with the quantitative data of the other resources to obtain the risk score of the target resource relative to the other resources.

[0105] S3032, sum the risk scores of the target resource against all other remaining resources to obtain the risk assessment result of the target resource.

[0106] "Other resources" refers to any one of the remaining resources. The risk score of the target resource relative to other resources represents its unilateral contribution to the risk of the target resource when only the influence of that specific other resource is considered. The risk assessment result of the target resource represents the comprehensive risk borne by that target resource, taking into account the network effects of interdependence among all laboratory resources.

[0107] In this embodiment, the risk score of each resource depends not only on its own state but also on the state of other resources. This is because resources are interdependent; anomalies or high loads in some resources directly affect the efficiency of other resources and the overall health of the laboratory. To reflect the mutual influence between resources, the relationship with other resources needs to be comprehensively considered when calculating the risk assessment result for each resource.

[0108] For each of the remaining resources, the computer device multiplies the relative weight information of the target resource with the quantitative data of the other resources to obtain a risk score for the target resource relative to the other resources. Then, it sums the risk scores of the target resource relative to all other remaining resources to obtain the risk assessment result for the target resource. This step, by weighting the risk scores of all resources, can reflect the efficiency of resource utilization and potential risks. Specifically, it can be expressed by the following formula:

[0109]

[0110] in, The risk assessment result of the target resource (i.e., the i-th resource) at time t. It represents the total number of resources. This represents the quantized data of the j-th resource at time t.

[0111] For example, the computer device implements this step by traversing and calculating. Specifically, when evaluating the target resource (e.g., the device identified as resource i), the computer device can sequentially retrieve each other resource (e.g., the person identified as resource j) from the list of remaining resources. For the other resource j currently being processed, the computer device reads the pre-calculated relative weight information corresponding to the target resource from memory or a database (i.e., ...). , representing the influence weight of resource j on resource i), and read the quantified data of the other resource j (i.e. Subsequently, the computer device performs a floating-point multiplication operation: Risk Score = × The result of this product is the risk score of the target resource relative to other resources. For example, if a precision piece of equipment (target resource) has a higher weight than the operator (other resources), then... (High value), and the quantified value of the operator's current workload ( If the risk score is also high, the calculated risk score will be significantly higher, accurately reflecting the one-sided impact of "high personnel workload leading to increased operational risk of the equipment".

[0112] The computer device performs this step through aggregation calculation. Specifically, after traversing all other resources and obtaining a series of risk scores for the target resource relative to other resources, the computer device initializes an accumulator (usually a floating-point variable) to store the sum. Then, it iterates through these calculated risk score values, adding them sequentially to the accumulator. When the loop ends, the final value in the accumulator is the risk assessment result of the target resource, where the summation has been performed on all other resources j. This process essentially linearly superimposes the individual risk contributions of each associated resource to the target resource, thereby comprehensively assessing the overall risk posture of the target resource in the complex resource network. For example, the risk assessment result of a piece of equipment is its comprehensive risk value under the combined influence of all associated personnel, other equipment, material conditions, etc., and not just a reflection of its own operating status.

[0113] The method described in this application constructs a structured resource risk assessment model through a progressive technical framework of "nonlinear quantification - weight determination - risk assessment". This model first unifies heterogeneous resource data into standardized quantitative values, providing a comparable data foundation for subsequent analysis; secondly, by dynamically calculating the relative weights between resources, it can accurately characterize the real-time dependencies in the resource network; and finally, it completes a comprehensive risk assessment through weighted aggregation. This closed-loop process achieves systematic calculation from data normalization and relationship quantification to risk quantification, ensuring that the risk assessment results reflect both the state of the resources themselves and the mutual influence between resources, resulting in a more comprehensive assessment dimension and significantly improving the accuracy of risk identification.

[0114] When calculating the relative weights between resources, two configurable parameters, "similarity sensitivity" and "weight sensitivity," are introduced and utilized to achieve fine-grained control over the weight formation mechanism. "Similarity sensitivity" adjusts the range of influence of state differences between resources on the rate of weight decay, while "weight sensitivity" controls the amplification or smoothing of state differences themselves during calculation. This design makes the weight calculation model highly flexible and adaptable, enabling precise modeling by adjusting the sensitivity parameters based on the characteristics of the mutual influence relationships between different resource pairs (e.g., some relationships are sensitive to small differences, while others only react to large differences), thus more realistically reflecting complex resource dependencies.

[0115] The risk assessment process is explicitly broken down into two steps: "single-point contribution calculation" and "global summation and aggregation." First, by multiplying, the individual risk contribution of each other resource to the target resource is quantified, clarifying the specific path and magnitude of risk transmission. Then, by summing all single-point contributions, a linear superposition and integration of all external influences on the target resource is achieved. This ensures that the final assessed "comprehensive risk" is the result of the combined effects of the entire resource ecosystem in the laboratory, rather than an isolated indicator, thus providing a direct basis for risk warning and root cause analysis.

[0116] In some embodiments, a specific implementation method is also provided for conducting a health status assessment of the laboratory to obtain the current health status assessment result of the laboratory, such as... Figure 6 As shown, the phrase "based on the current risk assessment results and historical resource data of each resource, conduct a health status assessment of the laboratory to obtain the current health status assessment result of the laboratory" in S203 above includes:

[0117] S401, extract the historical risk assessment results of each resource and the historical health status assessment results of the laboratory from historical resource data.

[0118] Historical resource data is stored in a database or file system, recording resource-related datasets at one or more past assessment points. Historical risk assessment results are risk scores calculated and archived for each resource at a past point in time (e.g., time t-1). Historical laboratory health status assessment results are comprehensive health scores calculated and archived for the overall operational status of the laboratory at a past point in time (e.g., time t-1).

[0119] In this embodiment, when a health status assessment at the current time (time t) is required, the computer device first accesses the database or log file storing historical assessment results. Then, based on a preset time identifier (such as timestamp t-1) or the latest historical record entry, it queries and reads two key data items: one is the historical risk assessment result of all resources in the previous assessment cycle (or a specified historical time), denoted as... Second, the historical health status assessment results of the laboratory at the same historical moment, recorded as... These data were calculated and saved in the previous evaluation process, serving as the benchmark for the current evaluation and the basis for trend analysis.

[0120] S402, based on the current risk assessment results and the historical risk assessment results of each resource, determine the risk change of each resource.

[0121] Among them, the risk change is a difference or increment indicator used to quantify the difference in risk assessment results of a single resource between two consecutive assessment moments (the current moment and the previous historical moment), reflecting the rising and falling trend and fluctuation range of the risk level of the resource.

[0122] In this embodiment of the application, for each resource i, the computer device calculates the historical risk assessment result. and the risk assessment results at the current moment The difference between them is used to determine the risk change of each resource, which can be expressed by the following formula:

[0123]

[0124] in, Let be the risk change of the i-th resource at time t, and let represent the change in the health score of the i-th resource between the current time and the previous time. It is the adjustment factor for the i-th resource, controlling the degree of influence of the error increment on the health assessment. This represents the risk assessment result for the i-th resource at the current moment. This represents the risk assessment result of the i-th resource at the previous time step (t-1).

[0125] Through this calculation, the computer device generates an error increment (i.e., risk change) for each resource that characterizes its risk dynamics.

[0126] S403. Based on the risk changes of each resource and the historical health status assessment results of the laboratory, conduct a health status assessment of the laboratory to obtain the current health status assessment result of the laboratory.

[0127] In this embodiment, health status assessment is a key indicator in the process of laboratory resource integration and optimization, reflecting the overall operational status of the laboratory at the current moment. To accurately assess the laboratory's health status, the health score needs to be dynamically adjusted. Computer equipment can monitor the risk changes of each resource. and the laboratory's historical health status assessment results. The results are then aggregated to obtain the laboratory's current health status assessment. Specifically, this can be achieved by weighted summation, multiplying the risk change of each resource by its importance weight to overall health. The results are then accumulated, and finally, a health status assessment of the laboratory is performed based on the accumulated results to obtain the laboratory's current health status assessment result, which can be expressed by the following formula:

[0128]

[0129] in, This represents the health status assessment result of the laboratory at the current time (time t). This represents the health status assessment results at the laboratory at the previous time point (t-1). Let be the importance weight of the i-th resource to overall health. Let be the risk change of the i-th resource.

[0130] This calculation process implies that the current health status is not calculated independently, but rather is a continuation and correction of the historical status. The correction amount is jointly determined by the real-time changing trends of all resource risks (risk changes), enabling the health score to smoothly and continuously reflect the dynamic evolution of the laboratory's status.

[0131] The method described in this application calculates risk changes by extracting and comparing historical and current risk assessment results. This enables the system to accurately capture the dynamic rise and fall trends of the risk level of each resource, achieving a quantitative perception of resource status evolution. Furthermore, by combining the risk changes of each resource with the laboratory's historical health status assessment results for comprehensive calculation, dynamic updates and trend accumulation of health status assessments are achieved. This method ensures that the assessment results not only reflect the static state of the laboratory "at this moment" but also embed the trajectory of change from "past" to "present." This allows for more sensitive early warning of potential deterioration trends and confirmation of improvement effects, providing a health status assessment with temporal continuity and trend insight, and providing a key basis for preventive resource scheduling and adaptive optimization.

[0132] In some embodiments, a specific implementation method for resource scheduling of various resources within the laboratory is also provided, such as... Figure 7 As shown, the phrase "based on the laboratory's current health status assessment results, resource scheduling is performed on various resources within the laboratory to obtain scheduling results" in S204 above includes:

[0133] S501. Based on the health score in the laboratory's current health status assessment results, the preset health threshold, and the initial weights of each resource, determine the optimal decision value for each resource at the current moment.

[0134] Among them, the health score in the laboratory's current health status assessment is a comprehensive quantitative indicator characterizing the overall operational status of the laboratory, denoted as . The preset health threshold is a pre-defined value representing the desired or maintained level of health, denoted as . The initial weights of each resource are weight coefficients pre-assigned to each type of resource, reflecting their differences in importance to the overall optimization during the initial configuration phase, denoted as... The current optimization decision value for each resource is a quantitative decision output, representing the current health status, used to guide resource scheduling and allocation, and ultimately affecting the configuration and operational efficiency of laboratory resources.

[0135] In this embodiment, during the optimization decision-making stage, the core objective is to determine the optimal configuration of each resource based on the laboratory's health score and the interdependencies between resources, thereby maximizing resource utilization efficiency and reducing potential risks. The optimization decision function generates an optimization plan for each resource based on the health assessment results, converting the difference between the laboratory's health score and the target health value into resource allocation adjustment decisions, and then performing resource scheduling. The computer device can acquire the optimization decision function and then, based on the health score in the laboratory's current health status assessment results, a preset health threshold, and the initial weights of each resource, determine the current optimization decision value for each resource. This optimization decision function can be expressed by the following formula:

[0136]

[0137] in, Let be the optimal decision value for the i-th resource at the current moment. Let be the initial weight of the i-th resource. It is the adjustment factor for the i-th resource, used to control the response speed of health score differences to resource optimization decisions. This is the current health status assessment result of the laboratory. Set a preset health threshold.

[0138] The aforementioned optimization decision function determines the optimal configuration scheme for each resource by inputting the difference between the health score and the health threshold into a nonlinear activation function. This ensures dynamic adjustment of resource allocation, enabling the configuration of laboratory resources to be continuously updated at different points in time based on changes in health and optimization objectives, thereby promoting the improvement of laboratory operating efficiency.

[0139] S502, based on the current optimization decision value, health score and target health threshold of each resource, determine the next optimization decision value of each resource.

[0140] The target health threshold is the desired laboratory health score. Based on the laboratory's management goals and resource availability, an ideal health target is set as a reference for optimization decisions, denoted as [missing information]. The optimized decision value for each resource in the next time step is an updated configuration recommendation value prepared for the next time step, based on the current decision and after feedback correction considering the gap with the long-term goal. This value is denoted as... .

[0141] In this embodiment, the computer device can dynamically adjust resource allocation based on the difference between the health score and the health threshold. To optimize resource allocation towards the target health level, a feedback mechanism is used to adjust the optimization decision value for each resource. By calculating the adjustment amount and adding it to the current optimization decision value, an updated optimization decision value is obtained. This optimization decision process ensures that the allocation of each resource can be adjusted in real time according to changes in laboratory health, optimizing resource allocation and ultimately achieving the goal of improving the overall efficiency of the laboratory. The specific process can be represented by the following relationship:

[0142]

[0143] in, It is the optimized decision value of the i-th resource at the next time step (t+1), that is, the updated optimized decision value; It is the adjustment amount for the optimization decision of the i-th resource; It is the feedback adjustment factor for the i-th resource, used to control the sensitivity of the adjustment amount; It is the target health threshold.

[0144] It should be noted that the above update process ensures that the configuration of each resource can be continuously optimized and adapted to changes in the environment during laboratory operation.

[0145] S503 performs resource scheduling on various resources in the laboratory based on the optimized decision value of each resource at the next moment and the preset configuration strategy, and obtains the scheduling result.

[0146] The preset configuration strategy is a set of predefined rules or mapping relationships used to transform abstract optimization decision values ​​into specific, executable resource configurations or operation instructions. The scheduling result is the final set of specific task instructions that can directly drive the laboratory management system to execute, such as equipment task allocation plans, personnel work adjustment notices, and material purchase orders.

[0147] In this embodiment, after the computer device obtains the next-moment optimization decision value for each resource based on the above steps, it can schedule and allocate resources such as equipment, personnel, and experimental materials in the laboratory by combining preset configuration strategies for different types of resources. Specifically, the usage priority and task allocation of resources can be dynamically adjusted according to the updated optimization decision value to obtain the scheduling result. For example, for experimental equipment, the usage frequency and maintenance cycle of some equipment may be adjusted to improve overall efficiency or reduce the risk of failure. The preset configuration strategy can stipulate that when the optimization decision value exceeds a certain threshold, its usage priority will be automatically reduced or a maintenance task will be triggered in the reservation system. For laboratory personnel, if some personnel have an excessive workload, the optimization decision value can help the system adjust the task allocation of personnel to avoid excessive fatigue and ensure effective resource utilization and efficient work. The preset configuration strategy can map the optimization decision value to specific work task adjustment suggestions or skills training prompts. For experimental materials, based on the usage of experimental materials, the updated optimization decision value can help decision-makers rationally arrange the replenishment of experimental materials or adjust the consumption rate of materials to avoid excessive waste or lack of material support. The preset configuration strategy can trigger inventory replenishment alarms or adjust requisition quotas based on the optimization decision value.

[0148] Computer devices generate specific scheduling results based on these strategies, and then send scheduling instructions to the corresponding laboratory equipment management system, personnel task system or warehouse management system through application programming interface (API) calls, message queues or direct writing to the control instruction database, thereby driving the reallocation of physical world resources according to the optimization scheme.

[0149] The method described in this application combines real-time health assessment with preset thresholds and utilizes nonlinear functions and initial weights to generate preliminary optimization decisions. This transforms the macroscopic health status into quantifiable and actionable configuration adjustment suggestions for each resource, achieving scientific and refined decision-making. By introducing target health thresholds and feedback adjustment mechanisms, optimization decision-making becomes a continuously iterative and self-correcting dynamic process. Resource allocation can continuously track and approach the long-term optimal goal, enhancing the system's adaptability and optimization capabilities. Finally, by converting the updated optimization decision values ​​into specific scheduling instructions according to preset strategies, the key closed loop from "decision calculation" to "physical execution" is completed, ensuring that the optimization plan can be implemented effectively. This dynamically and automatically adjusts laboratory resource allocation, effectively improving overall operational efficiency and health levels.

[0150] In summary, based on all the above embodiments, a method for scheduling laboratory resources is also provided, the method comprising:

[0151] S601 acquires current and historical resource data for various resources within the laboratory. These resources include experimental equipment, personnel, and materials.

[0152] S602 performs non-linear quantization on the current resource data of each resource to obtain the quantized data of each resource.

[0153] S603, for each target resource among all resources, determine the weight sensitivity information based on the weight sensitivity, the quantitative data of the target resource, and the quantitative data of the remaining resources.

[0154] S604. Determine the relative weight information between the target resource and the remaining resources based on weight sensitivity information and similarity sensitivity information.

[0155] S605, for each of the remaining resources, multiply the relative weight information corresponding to the target resource with the quantitative data of the other resources to obtain the risk score of the target resource relative to the other resources.

[0156] S606 sums the risk scores of the target resource against all other remaining resources to obtain the risk assessment result of the target resource.

[0157] S607 extracts the historical risk assessment results of each resource and the historical health status assessment results of the laboratory from historical resource data.

[0158] S608, based on the current risk assessment results and the historical risk assessment results of each resource, determine the risk change of each resource.

[0159] S609. Based on the risk changes of each resource and the historical health status assessment results of the laboratory, a health status assessment of the laboratory is conducted to obtain the current health status assessment result of the laboratory.

[0160] S610, based on the health score in the laboratory's current health status assessment results, the preset health threshold, and the initial weights of each resource, determine the optimal decision value for each resource at the current moment.

[0161] S611, determine the optimization decision value for each resource at the next moment based on the current optimization decision value, health score and target health threshold of each resource.

[0162] S612 performs resource scheduling on various resources in the laboratory based on the optimized decision value of each resource at the next moment and the preset configuration strategy, and obtains the scheduling result.

[0163] The optimization method for laboratory resource integration and health assessment in this application includes the following steps:

[0164] S1. Acquire real-time data of laboratory resources. All resource data are converted into standardized quantitative values ​​through nonlinear mapping, and a weight matrix between resources is established to assess the interdependence between resources. By calculating the error increment of each resource, the fluctuation of the health of laboratory resources is measured.

[0165] In the quantization process, quantization mapping transforms the data of each resource using a nonlinear mapping function to conform to the standards for laboratory health assessment. The original data of each resource at time t is converted into a quantized value through the mapping function, representing the actual use or state of the resource at that time. During the mapping process, different transformation methods are used depending on the resource type to adapt to the dynamic changes in the resources. Using the quantified resource data, a relative weight matrix is ​​constructed to represent the interdependencies between resources, especially their impact on the overall efficiency and health of the laboratory. The relative influence between resources is determined by weighted calculation of the quantization differences between each pair of resources. To accurately reflect the actual operation of laboratory resources, a health assessment model based on incremental error feedback is proposed. This model dynamically reflects the health fluctuations of laboratory resources and corrects health scores through feedback. By calculating the error increment for each resource, the fluctuation of laboratory resource health can be measured. The error increment reflects the difference between the health score at the current time and the previous time, thus revealing the impact of changes in resource status on laboratory health.

[0166] S2. Optimize resource allocation using health scores, generate optimization decision values ​​for each resource, and adjust them through a feedback mechanism to ensure that resource allocation can adapt to changes in laboratory health in real time. The optimization decision results are used to schedule resources and improve the overall efficiency of the laboratory.

[0167] Among these, the health score is a key indicator in the laboratory resource integration and optimization process, reflecting the overall operational status of the laboratory at any given moment. To accurately assess the laboratory's health status, the health score needs to be dynamically adjusted, especially when resource usage changes; the correction process is based on error increments. In the optimization decision-making phase, the core objective is to determine the optimal configuration of each resource based on the laboratory's health score and the interdependencies between resources, maximizing resource utilization efficiency and minimizing potential risks. The optimization decision function generates an optimization plan for each resource based on the health assessment results, converting the difference between the laboratory health score and the target health value into resource allocation adjustment decisions, thereby enabling resource scheduling. During the optimization decision-making process, resource allocation is dynamically adjusted based on the difference between the health assessment results and the health threshold. To optimize resource allocation towards the target health level, a feedback mechanism adjusts the optimization decision value for each resource. By calculating the adjustment amount and adding it to the current optimization decision value, an updated optimization decision value is obtained. This optimization decision-making process ensures that the configuration of each resource can be adjusted in real time according to changes in laboratory health, optimizing resource allocation and ultimately achieving the goal of improving the overall efficiency of the laboratory.

[0168] The method described in this application, by comprehensively integrating data on equipment, personnel, and experimental materials within the laboratory, and through quantitative mapping and health assessment methods, enables dynamic adjustment and optimized allocation of resources. This avoids resource waste, ensures the most effective use of various resources, and maximizes the laboratory's resource utilization rate. The incremental error feedback mechanism dynamically assesses the laboratory's health, reflecting real-time fluctuations in the health of various laboratory resources. Based on the health assessment results and the optimization decision model, an optimized resource allocation scheme can be automatically generated according to the laboratory's health score and the mutual influence between resources. This scheme is dynamically adjusted at different points in time, allowing the laboratory's resource allocation to continuously optimize with changes in the laboratory's health, thereby achieving efficient resource allocation and improving the overall efficiency of the laboratory. Through dynamic scheduling based on health scores and risk assessments, the embodiments can identify potential risks between resources in advance, such as equipment failure, excessive personnel workload, and material shortages, and adjust resource use and allocation in a timely manner to reduce potential risks and ensure smooth and efficient laboratory operation.

[0169] The methods described in each of the above steps have been described in the foregoing embodiments. For details, please refer to the foregoing descriptions. They will not be repeated here.

[0170] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0171] Based on the same inventive concept, this application also provides a laboratory resource scheduling apparatus for implementing the laboratory resource scheduling method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations of one or more laboratory resource scheduling apparatus embodiments provided below can be found in the limitations of the laboratory resource scheduling method described above, and will not be repeated here.

[0172] In some embodiments, such as Figure 8 As shown, a laboratory resource scheduling device is provided, comprising:

[0173] The acquisition module 11 is used to acquire current and historical resource data of various resources in the laboratory; resources include experimental equipment, experimental personnel and experimental materials.

[0174] The risk assessment module 12 is used to assess the risk of each resource based on the current resource data and the dependency information between resources, and to obtain the risk assessment result of each resource at the current moment.

[0175] The health assessment module 13 is used to assess the health status of the laboratory based on the current risk assessment results and historical resource data of each resource, and obtain the current health status assessment results of the laboratory.

[0176] The scheduling module 14 is used to schedule various resources in the laboratory based on the current health status assessment results of the laboratory, and obtain the scheduling results.

[0177] In some embodiments, the risk assessment module described above includes:

[0178] The quantization unit is used to perform non-linear quantization on the current resource data of each resource to obtain the quantized data of each resource.

[0179] The first determining unit is used to determine the relative weight information between the target resource and the remaining resources for each target resource among the resources, based on the quantitative data of the target resource, the quantitative data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources.

[0180] The risk assessment unit is used to assess the risk of the target resource based on the quantitative data of the remaining resources and the relative weight information between the target resource and the remaining resources, and to obtain the risk assessment result of the target resource at the current moment.

[0181] In some embodiments, the first determining unit includes:

[0182] The first determining subunit is used to determine weight sensitivity information based on weight sensitivity, quantitative data of target resources, and quantitative data of remaining resources.

[0183] The second determining subunit is used to determine the relative weight information between the target resource and the remaining resources based on weight sensitivity information and similarity sensitivity information.

[0184] In some embodiments, the risk assessment unit described above includes:

[0185] The first calculation subunit is used to perform a product operation on the relative weight information corresponding to the target resource and the quantitative data of other resources for each of the remaining resources, so as to obtain the risk score of the target resource relative to other resources.

[0186] The second calculation subunit is used to sum the risk scores of the target resource against all other remaining resources to obtain the risk assessment result of the target resource.

[0187] In some embodiments, the above-described health assessment module includes:

[0188] The extraction unit is used to extract the historical risk assessment results of each resource and the historical health status assessment results of the laboratory from historical resource data.

[0189] The second determining unit is used to determine the risk change of each resource based on the current risk assessment results and the historical risk assessment results of each resource.

[0190] The health assessment unit is used to assess the health status of the laboratory based on the risk changes of each resource and the laboratory's historical health status assessment results, and obtain the current health status assessment result of the laboratory.

[0191] In some embodiments, the scheduling module described above includes:

[0192] The third determining unit is used to determine the optimal decision value of each resource at the current moment based on the health score in the laboratory's current health status assessment results, the preset health threshold, and the initial weight of each resource.

[0193] The fourth determining unit is used to determine the optimal decision value of each resource at the next moment based on the current optimal decision value, health score and target health threshold of each resource.

[0194] The scheduling unit is used to schedule various resources in the laboratory according to the optimized decision value of each resource at the next moment and the preset configuration strategy, and obtain the scheduling result.

[0195] Each module in the aforementioned laboratory resource scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0196] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the laboratory resource scheduling method described in any of the above embodiments.

[0197] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the laboratory resource scheduling method described in any of the above embodiments.

[0198] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the laboratory resource scheduling method described in any of the above embodiments.

[0199] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0200] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for scheduling laboratory resources, characterized in that, The method includes: Acquire current and historical resource data for various resources within the laboratory; the resources include experimental equipment, experimental personnel, and experimental materials, and the experimental material data includes at least one of the following: inventory quantity, consumption rate, expiration date, and requisition records; Based on the current resource data of each resource and the dependency information between each resource, a risk assessment is performed on each resource to obtain the risk assessment result of each resource at the current moment. The dependency information represents the mutual influence and constraint relationship between different categories of resources in terms of function, efficiency, or status. The dependency information includes weight sensitivity and similarity sensitivity. The weight sensitivity controls the influence intensity of the quantitative data difference between two resources on the final weight calculation, and the similarity sensitivity controls the overall influence range or decay rate of the similarity between resources on the weight value. The risk assessment result represents the potential risk level faced by a single resource at the current moment due to its own status and the status of other related resources. Based on the current risk assessment results of each resource and the historical risk assessment results and historical health status assessment results of each resource in the historical resource data, the health status assessment of the laboratory is performed to obtain the current health status assessment result of the laboratory. Based on the health score in the current health status assessment results of the laboratory, the preset health threshold, and the initial weight of each resource, determine the optimal decision value of each resource at the current moment; Based on the current optimization decision value, health score, and target health threshold of each resource, determine the next optimization decision value for each resource. Based on the optimized decision value of each resource at the next moment and the preset configuration strategy, resource scheduling is performed on various resources in the laboratory to obtain the scheduling result.

2. The method according to claim 1, characterized in that, The step of performing a risk assessment on each resource based on its current resource data and the dependency information between the resources, to obtain the risk assessment result of each resource at the current moment, includes: The current resource data of each resource is non-linearly quantized to obtain the quantized data of each resource. For each target resource among the resources, relative weight information between the target resource and the remaining resources is determined based on the quantized data of the target resource, the quantized data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources. Based on the quantitative data of the remaining resources and the relative weight information between the target resource and the remaining resources, a risk assessment is performed on the target resource to obtain the risk assessment result of the target resource at the current moment.

3. The method according to claim 2, characterized in that, The step of determining the relative weight information between the target resource and the remaining resources based on the quantified data of the target resource, the quantified data of the remaining resources other than the target resource, and the dependency information between the target resource and the remaining resources includes: The weight sensitivity information is determined based on the weight sensitivity, the quantized data of the target resource, and the quantized data of the remaining resources. Based on the weight sensitivity information and the similarity sensitivity, the relative weight information between the target resource and the remaining resources is determined.

4. The method according to claim 2, characterized in that, The step of performing a risk assessment on the target resource based on the quantified data of the remaining resources and the relative weight information between the target resource and the remaining resources, to obtain the risk assessment result of the target resource at the current moment, includes: For each of the remaining resources, the relative weight information corresponding to the target resource and the quantitative data of the other resources are multiplied to obtain the risk score of the target resource relative to the other resources; The risk assessment result of the target resource is obtained by summing the risk scores of the target resource against all other resources in the remaining resources.

5. The method according to claim 1, characterized in that, The step of assessing the health status of the laboratory based on the current risk assessment results of each resource and the historical resource data, to obtain the current health status assessment result of the laboratory, includes: Extract the historical risk assessment results of each resource and the historical health status assessment results of the laboratory from the historical resource data; Based on the current risk assessment results and the historical risk assessment results of each resource, determine the risk change of each resource; Based on the risk changes of each resource and the historical health status assessment results of the laboratory, a health status assessment is performed on the laboratory to obtain the current health status assessment result of the laboratory.

6. A laboratory resource scheduling device, used to implement the laboratory resource scheduling method as described in claim 1, characterized in that, The device includes: The acquisition module is used to acquire current and historical resource data of various resources in the laboratory; the resources include experimental equipment, experimental personnel, and experimental materials. The risk assessment module is used to assess the risk of each resource based on the current resource data and the dependency information between the resources, and to obtain the risk assessment result of each resource at the current moment. The health assessment module is used to assess the health status of the laboratory based on the current risk assessment results of each resource and the historical resource data, and to obtain the current health status assessment result of the laboratory. The scheduling module is used to schedule various resources within the laboratory based on the current health status assessment results of the laboratory, and obtain the scheduling results.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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