Biosafety laboratory energy efficiency optimization method and system based on simulation

Abstract

The present invention discloses a method and system for optimizing the energy efficiency of a biosafety laboratory based on simulation, including collecting simulation data and related data of a simulated biosafety laboratory, preprocessing the simulation data and the related data; performing comparative clustering on the simulation data according to an energy efficiency threshold to obtain grouped data, performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio; performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and obtaining a variable coefficient according to an associated impact analysis on the related data; constructing a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimizing the biosafety laboratory energy efficiency optimization model, inputting the data to be optimized into the biosafety laboratory energy efficiency optimization model, and outputting an optimization result. The method improves the accuracy of energy efficiency optimization of a biosafety laboratory based on simulation, and has good interpretability, and can be directly applied to an energy efficiency optimization system.

Description

Technical Field

[0001] The present invention relates to the field of simulation optimization, and in particular to a method and system for optimizing energy efficiency of a biosafety laboratory based on simulation. Background Art

[0002] With the rapid development of life science research, biosafety laboratories, as important places for disease prevention and control, drug development and biotechnology research, are receiving increasing attention for their operating efficiency and safety. However, the energy consumption management of biosafety laboratories has long faced many challenges, including but not limited to a wide variety of equipment, complex operating modes, strict environmental control requirements, and uncertainty in experimental operations. These factors together make it difficult to accurately control and optimize energy efficiency.

[0003] Traditional biosafety laboratory energy efficiency management relies on manual experience adjustment and regular equipment maintenance, lacking systematic data analysis and scientific prediction and optimization methods. In addition, the complex relationship between laboratory internal environmental parameters and energy consumption makes it difficult to achieve global energy efficiency optimization through single parameter adjustment. Therefore, developing a biosafety laboratory energy efficiency optimization method based on simulation has become an important way to improve laboratory energy efficiency, reduce operating costs, and ensure experimental safety. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the energy efficiency of a biosafety laboratory based on simulation.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Collecting simulation data and related data of a simulated biosafety laboratory, and preprocessing the simulation data and the related data;

[0008] Comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio;

[0009] Performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient;

[0010] Constructing a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimizing the biosafety laboratory energy efficiency optimization model, inputting the data to be optimized into the biosafety laboratory energy efficiency optimization model, and outputting the optimization result; comprising:

[0011] Construct the objective function based on the correction data:

[0012]

[0013] The control weight is The natural constant is e, and the energy efficiency of the κth simulation is The variable coefficient of the κth simulation is χ κ , the optimization rate φ of the κth simulation κ , the loss coefficient of the κth simulation is ζ κ ;

[0014] The energy efficiency optimization model of the biosafety laboratory includes decision tree algorithm, energy efficiency evaluation, and machine learning algorithm;

[0015] The decision tree algorithm classifies data based on the similarity of input data to obtain classified data;

[0016] Energy efficiency evaluation is carried out based on the classified data to obtain the energy efficiency value;

[0017] The machine learning algorithm analyzes the optimization patterns and relationships of the categorized data according to the differences in energy efficiency values, and makes predictions on the energy efficiency optimization of the biosafety laboratory based on the optimization patterns and relationships.

[0018] Furthermore, the method of obtaining grouped data by comparing and clustering the simulation data according to the energy efficiency threshold includes:

[0019] Calculate the energy efficiency value based on the simulation data:

[0020]

[0021] The energy efficiency value is The unit price of the xth energy is The consumption of the xth energy at the sth moment is E x,s , the amount of energy is M1, and the weight of the vth energy efficiency evaluation index is η v , the value of the vth energy efficiency evaluation index at the sth moment is The upper limit of simulation time is T;

[0022] Cross-validation is used to obtain the energy efficiency threshold according to the energy efficiency value, and the simulation data greater than the energy efficiency threshold is used as the research data, and the non-repeated data in the research data are used to form the initial center set;

[0023] Calculate the similarity between the study data and the initial center set:

[0024]

[0025] The number of research data is P, and the number of research data for the cth simulation is P c , the number of research data in the τth initial center set is P τ , the i-th research data of the c-th simulation is uc,i , the i-th research data of the τ-th initial center set is u τ,i , the control factor is α, and the optimization coefficient is The similarity between the cth simulation research data and the τth initial center set is

[0026] The research data with the greatest similarity is used as the element of the initial center set to obtain the initial classification cluster;

[0027] Calculate the mean distance between the initial classification clusters:

[0028]

[0029] The mean distance between the initial classification clusters is The number of initial classification clusters is W, and the research data of the jth initial classification cluster is u j , the research data of the λth initial classification cluster is u λ , research data λ and research data j The distance is ds(u j ,u λ );

[0030] Compute central compactness:

[0031]

[0032] The center compactness of the jth initial classification cluster is The center point of the jth initial classification cluster is The number of elements in the jth initial classification cluster is W1;

[0033] Update the elements of the initial classification cluster by the center compactness until the center compactness reaches the maximum, and output the clustering result as grouped data, otherwise replace the initial center set.

[0034] Furthermore, the method of performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio includes:

[0035]

[0036] The upper limit of the simulation time is T, and the vth temperature barrier coefficient at the sth time is The number of elements in the grouped data is The energy efficiency value of the kth group data at the sth time is The importance of the vth group data is The contribution of the kth group data is The energy efficiency of the cth simulation is The initial energy efficiency value of the cth group data is The energy efficiency ratio of the cth simulation is φ c .

[0037] Furthermore, the method of obtaining an optimization rate by comparing and analyzing the grouped data according to the energy efficiency ratio includes:

[0038] The comparative analysis method is used to compare the energy efficiency ratio of the grouped data and calculate the optimization rate:

[0039]

[0040] The control factor is The energy efficiency value of the kth group data is The energy efficiency value of the fth group data is The energy efficiency ratio of the kth group data is φ k , the energy efficiency ratio of the fth group data is φ f , the optimization rate of the kth group data and the fth group data is χ(k,f).

[0041] Furthermore, the method for obtaining the variable coefficient by performing correlation influence analysis on the relevant data includes:

[0042] Calculate the influence of relevant data:

[0043]

[0044] The influence of the zth related data is The initial energy efficiency value is H o , the energy efficiency value of the zth related data after the change is H z , the number of relevant data is N, the adjustment factor is γ, and the contribution of the zth relevant data to the energy efficiency change is

[0045] Calculate the correlation:

[0046]

[0047] The relevant data are The number of relevant data y is N1, the correlation coefficient between relevant data y and energy efficiency value change ΔH is ρ(ΔH,y), and the control factor of relevant data y is The correlation between the zth relevant data and the energy efficiency value change ΔH is ε z (ΔH z );

[0048] The relevant data with a correlation degree greater than 0.315 are taken as key data, and the machine learning algorithm is used to analyze the impact of key data according to energy efficiency changes;

[0049] Compute the coefficient of variation for correlated data:

[0050]

[0051] The predicted energy efficiency value of the zth related data is H z , the proportional control coefficient is ξ, and the predicted energy efficiency value is H z The probability of occurrence is Q(H z ), and the predicted energy efficiency value H z and key data z The probability is Q(H z ,y z ), the error coefficient is υ, and the variation coefficient of the zth related data is The first weight is The second weight is

[0052] Furthermore, the method for optimizing the biosafety laboratory energy efficiency optimization model comprises:

[0053] Introduce the search sub-population and perform chaotic mapping on the search sub-population. The expression is:

[0054]

[0055] The chaotic random number from 0 to 1 is b, and the w+1th dimension is X w+1 , the wth dimension is X w ;

[0056] Calculate the fitness of the searcher, take the position of the particle with the largest fitness as the optimal searcher position, and update the position of the searcher. The expression is:

[0057] Z a,w (t+1)=Z a,w (t)+r1·(Z a,w (t)-(Z * +β)·(1+ξ))

[0058] The random number from 1 to 2 is v1, and the position of the search sub-sub in the w-th dimension of the a-th search sub-sub in the t+1-th iteration of the exploration phase is Z a,w (t+1), the position of the searcher in the wth dimension of the ath searcher in the tth iteration is Z a,w (t), the first random number from 0 to 1 is r1, the Cauchy operator is ξ, the perturbation factor is β, and the optimal search subposition is Z * ;

[0059] Update the search sub-position according to the search area to obtain the regional position. The expression is:

[0060]

[0061] The region position of the w-th dimension of the a-th searcher in the t+1-th iteration is The decay factor is μ and the maximum number of iterations is t max , the adaptive inertia weight is l, the current number of iterations is t, the upper bound of the search area is Ub, and the lower bound of the search area is Lb;

[0062] The corrected position is obtained by using the position updated by the standard deviation, and the expression is:

[0063]

[0064] The suppression factor parameter is ω, and the standard deviation of the normal distribution random number κ is θ κ , the gamma function is The region position of the w-th dimension of the a-th searcher in the t-th iteration is The corrected position of the wth dimension of the ath searcher at the t+1th iteration is The lower bound of the w-th dimension is The upper bound of the w-th dimension is

[0065] The updated upper and lower bounds of the search area are expressed as:

[0066]

[0067] The updated upper bound of the search area is Ub * , the updated search area lower bound is Lb * ;

[0068] Iterate continuously until the maximum number of iterations is reached, then stop iterating, otherwise update the adaptive inertia weight.

[0069] The second aspect is the biosafety laboratory energy efficiency optimization system based on simulation, including:

[0070] Data acquisition module: used to collect simulation data and related data of the simulated biosafety laboratory, and pre-process the simulation data and the related data;

[0071] Cluster evaluation module: used for comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio;

[0072] Comparative analysis module: performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient;

[0073] Constructing an optimization module: used to construct a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimize the biosafety laboratory energy efficiency optimization model, input the data to be optimized into the biosafety laboratory energy efficiency optimization model, and output the optimization result.

[0074] The beneficial effects of the present invention are:

[0075] The present invention is a method and system for optimizing the energy efficiency of a biosafety laboratory based on simulation. Compared with the prior art, the present invention has the following technical effects:

[0076] The present invention can improve the accuracy of energy efficiency optimization of simulated biosafety laboratories through preprocessing, comparative clustering, energy efficiency evaluation, comparative analysis, correlation impact analysis, model construction and model optimization steps, thereby improving the precision of energy efficiency optimization of simulated biosafety laboratories. The energy efficiency optimization of simulated biosafety laboratories can be optimized, which can greatly save resources and improve work efficiency. It can realize intelligent optimization of the energy efficiency of biosafety laboratories and perform multi-data fusion on the energy efficiency optimization of simulated biosafety laboratories in real time. It is of great significance to the energy efficiency optimization of simulated biosafety laboratories, can adapt to energy efficiency optimization of simulated biosafety laboratories of different standards and energy efficiency optimization needs of different simulated biosafety laboratories, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of the steps of the method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to the present invention;

[0078] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0079] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0080] The biosafety laboratory energy efficiency optimization method and system based on simulation of the present invention include the following steps:

[0081] like Figure 1 As shown, in this embodiment, the following steps are included:

[0082] Collecting simulation data and related data of a simulated biosafety laboratory, and preprocessing the simulation data and the related data;

[0083] In the actual assessment, the XXX biosafety laboratory was used as a simulation object, and a control group simulation data was collected;

[0084] The simulation data includes the experimenter’s operation steps, operation time, equipment used, sample collection data, storage data, processing data, transportation parameters, and biological sample test results;

[0085] Relevant data include video records of actual operations, detailed descriptions of experimental steps, operating status of equipment, fault records, maintenance logs, identity information of experimental personnel, training records, health status, source, type, quantity, storage conditions of samples, temperature, humidity, and air quality in the laboratory;

[0086] Comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio;

[0087] In the actual evaluation, the grouping data were: the operation steps, operation time, and equipment used by the experimenters in group 1, the collection data, storage data, processing data, transportation parameters, and test results of biological samples in group 2. The energy efficiency ratio of the control group was 0.241;

[0088] Performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient;

[0089] In the actual evaluation, the optimization rate of the control group was 0.354, and the coefficient of variation of the control group was 0.176;

[0090] Constructing a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimizing the biosafety laboratory energy efficiency optimization model, inputting the data to be optimized into the biosafety laboratory energy efficiency optimization model, and outputting the optimization result; comprising:

[0091] Construct the objective function based on the correction data:

[0092]

[0093] The control weight is The natural constant is e, and the energy efficiency of the κth simulation is The variable coefficient of the κth simulation is χ κ , the optimization rate φ of the κth simulation κ , the loss coefficient of the κth simulation is ζ κ ;

[0094] The energy efficiency optimization model of the biosafety laboratory includes decision tree algorithm, energy efficiency evaluation, and machine learning algorithm;

[0095] The decision tree algorithm classifies data based on the similarity of input data to obtain classified data;

[0096] Energy efficiency evaluation is carried out based on the classified data to obtain the energy efficiency value;

[0097] The machine learning algorithm analyzes the optimization patterns and relationships of the categorized data according to the differences in energy efficiency values, and makes predictions on the energy efficiency optimization of the biosafety laboratory based on the optimization patterns and relationships.

[0098] In this embodiment, the method of obtaining grouped data by comparing and clustering the simulation data according to the energy efficiency threshold includes:

[0099] Calculate the energy efficiency value based on the simulation data:

[0100]

[0101] The energy efficiency value is The unit price of the xth energy is The consumption of the xth energy at the sth moment is E x,s , the amount of energy is M1, and the weight of the vth energy efficiency evaluation index is η v , the value of the vth energy efficiency evaluation index at the sth moment is The upper limit of simulation time is T;

[0102] Cross-validation is used to obtain the energy efficiency threshold according to the energy efficiency value, and the simulation data greater than the energy efficiency threshold is used as the research data, and the non-repeated data in the research data are used to form the initial center set;

[0103] Calculate the similarity between the study data and the initial center set:

[0104]

[0105] The number of research data is P, and the number of research data for the cth simulation is P c , the number of research data in the τth initial center set is P τ , the i-th research data of the c-th simulation is u c,i , the i-th research data of the τ-th initial center set is u τ,i , the control factor is α, and the optimization coefficient is The similarity between the cth simulation research data and the τth initial center set is

[0106] The research data with the greatest similarity is used as the element of the initial center set to obtain the initial classification cluster;

[0107] Calculate the mean distance between the initial classification clusters:

[0108]

[0109] The mean distance between the initial classification clusters is The number of initial classification clusters is W, and the research data of the jth initial classification cluster is uj , the research data of the λth initial classification cluster is u λ , research data λ and research data j The distance is ds(u j ,u λ );

[0110] Compute central compactness:

[0111]

[0112] The center compactness of the jth initial classification cluster is The center point of the jth initial classification cluster is The number of elements in the jth initial classification cluster is W1;

[0113] Update the elements of the initial classification cluster by the center compactness until the center compactness reaches the maximum, and output the clustering result as grouped data, otherwise replace the initial center set.

[0114] In this embodiment, the method of performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio includes:

[0115]

[0116] The upper limit of the simulation time is T, and the vth temperature barrier coefficient at the sth time is The number of elements in the grouped data is The energy efficiency value of the kth group data at the sth time is The importance of the vth group data is The contribution of the kth group data is The energy efficiency of the cth simulation is The initial energy efficiency value of the cth group data is The energy efficiency ratio of the cth simulation is φ c .

[0117] In this embodiment, the method for obtaining an optimization rate by performing comparative analysis on the grouped data according to the energy efficiency ratio includes:

[0118] The comparative analysis method is used to compare the energy efficiency ratio of the grouped data and calculate the optimization rate:

[0119]

[0120] The control factor is The energy efficiency value of the kth group data is The energy efficiency value of the fth group data is The energy efficiency ratio of the kth group data is φ k , the energy efficiency ratio of the fth group data is φf , the optimization rate of the kth group data and the fth group data is χ(k,f).

[0121] In this embodiment, the method for obtaining the variable coefficient by performing correlation influence analysis on the relevant data includes:

[0122] Calculate the influence of relevant data:

[0123]

[0124] The influence of the zth related data is The initial energy efficiency value is H o , the energy efficiency value of the zth related data after the change is H z , the number of relevant data is N, the adjustment factor is γ, and the contribution of the zth relevant data to the energy efficiency change is

[0125] Calculate the correlation:

[0126]

[0127] The relevant data are The number of relevant data y is N1, the correlation coefficient between relevant data y and energy efficiency value change ΔH is ρ(ΔH,y), and the control factor of relevant data y is The correlation between the zth relevant data and the energy efficiency value change ΔH is ε z (ΔH z );

[0128] The relevant data with a correlation degree greater than 0.315 are taken as key data, and the machine learning algorithm is used to analyze the impact of key data according to energy efficiency changes;

[0129] Compute the coefficient of variation for correlated data:

[0130]

[0131] The predicted energy efficiency value of the zth related data is H z , the proportional control coefficient is ξ, and the predicted energy efficiency value is H z The probability of occurrence is Q(H z ), and the predicted energy efficiency value H z and key data z The probability is Q(H z ,y z ), the error coefficient is υ, and the variation coefficient of the zth related data is The first weight is The second weight is

[0132] In this embodiment, the method for optimizing the biosafety laboratory energy efficiency optimization model includes:

[0133] Introduce the search sub-population and perform chaotic mapping on the search sub-population. The expression is:

[0134]

[0135] The chaotic random number from 0 to 1 is b, and the w+1th dimension is X w+1 , the wth dimension is X w ;

[0136] Calculate the fitness of the searcher, take the position of the particle with the largest fitness as the optimal searcher position, and update the position of the searcher. The expression is:

[0137] Z a,w (t+1)=Z a,w (t)+r1·(Z a,w (t)-(Z * +β)·(1+ξ))

[0138] The random number from 1 to 2 is v1, and the position of the search sub-sub in the w-th dimension of the a-th search sub-sub in the t+1-th iteration of the exploration phase is Z a,w (t+1), the position of the searcher in the wth dimension of the ath searcher in the tth iteration is Z a,w (t), the first random number from 0 to 1 is r1, the Cauchy operator is ξ, the perturbation factor is β, and the optimal search subposition is Z * ;

[0139] Update the search sub-position according to the search area to obtain the regional position. The expression is:

[0140]

[0141] The region position of the w-th dimension of the a-th searcher in the t+1-th iteration is The decay factor is μ and the maximum number of iterations is t max , the adaptive inertia weight is l, the current number of iterations is t, the upper bound of the search area is Ub, and the lower bound of the search area is Lb;

[0142] The corrected position is obtained by using the position updated by the standard deviation, and the expression is:

[0143]

[0144] The suppression factor parameter is ω, and the standard deviation of the normal distribution random number κ is θ κ , the gamma function is The region position of the w-th dimension of the a-th searcher in the t-th iteration is The corrected position of the wth dimension of the ath searcher at the t+1th iteration is The lower bound of the w-th dimension is The upper bound of the w-th dimension is

[0145] The updated upper and lower bounds of the search area are expressed as:

[0146]

[0147] The updated upper bound of the search area is Ub * , the updated search area lower bound is Lb * ;

[0148] Iterate continuously until the maximum number of iterations is reached, then stop iterating, otherwise update the adaptive inertia weight.

[0149] The second aspect is the biosafety laboratory energy efficiency optimization system based on simulation, including:

[0150] Data acquisition module: used to collect simulation data and related data of the simulated biosafety laboratory, and pre-process the simulation data and the related data;

[0151] Cluster evaluation module: used for comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio;

[0152] Comparative analysis module: performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient;

[0153] Constructing an optimization module: used to construct a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimize the biosafety laboratory energy efficiency optimization model, input the data to be optimized into the biosafety laboratory energy efficiency optimization model, and output the optimization result.

[0154] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0155] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0156] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0157] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a user intention recognition device for a multi-dimensional human-computer interaction scene at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned user intention recognition methods for a multi-dimensional human-computer interaction scene.

[0158] The above application Figure 1The user intention recognition method for a multi-dimensional human-computer interaction scenario disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0159] The electronic device may also perform Figure 1 A user intention recognition method for multi-dimensional human-computer interaction scenarios is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0160] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute any of the user intention recognition methods for the aforementioned multi-dimensional human-computer interaction scenarios.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0165] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0166] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0167] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0168] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0169] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A biosafety laboratory energy efficiency optimization method based on simulation, characterized in that: The following steps are involved: Collecting simulation data and related data of a simulated biosafety laboratory, and preprocessing the simulation data and the related data; Comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio; Performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient; Constructing a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimizing the biosafety laboratory energy efficiency optimization model, inputting the data to be optimized into the biosafety laboratory energy efficiency optimization model, and outputting the optimization result; comprising: Construct the objective function based on the correction data: The control weight is , the natural constant is e, The energy efficiency of the simulation is , No. The variable coefficient of the simulation is , No. The optimization rate of the simulation , No. The loss coefficient of the simulation is ; The energy efficiency optimization model of the biosafety laboratory includes decision tree algorithm, energy efficiency evaluation, and machine learning algorithm; The decision tree algorithm classifies data based on the similarity of input data to obtain classified data; Energy efficiency evaluation is carried out based on the classified data to obtain the energy efficiency value; The machine learning algorithm analyzes the optimization patterns and relationships of the categorized data according to the differences in energy efficiency values, and makes predictions on the energy efficiency optimization of the biosafety laboratory based on the optimization patterns and relationships.

2. The method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to claim 1, characterized in that: The method of obtaining grouped data by comparing and clustering the simulation data according to the energy efficiency threshold comprises: Calculate the energy efficiency value based on the simulation data: The energy efficiency value is , the unit price of the x-th energy is , the consumption of the xth energy at the sth moment is , the amount of energy is , the weight of the vth energy efficiency evaluation index is , the value of the vth energy efficiency evaluation index at the sth moment is , the upper limit of simulation time is ; Cross-validation is used to obtain the energy efficiency threshold according to the energy efficiency value, and the simulation data greater than the energy efficiency threshold is used as the research data, and the non-repeated data in the research data are used to form the initial center set; Calculate the similarity between the study data and the initial center set: The amount of research data is , the number of research data for the cth simulation is , No. The number of research data in the initial center set is , the i-th research data of the c-th simulation is , No. The i-th research data of the initial center set is , the control factor is , the optimization coefficient is , the cth simulation research data and the The similarity of the initial center set is ; The research data with the greatest similarity is used as the element of the initial center set to obtain the initial classification cluster; Calculate the mean distance between the initial classification clusters: The mean distance between the initial classification clusters is , the number of initial classification clusters is , the research data of the jth initial classification cluster is , No. The research data of the initial classification cluster is , research data and research data The distance is ; Compute central compactness: The center compactness of the jth initial classification cluster is , the center point of the jth initial classification cluster is , the number of elements in the jth initial classification cluster is ; Update the elements of the initial classification cluster by the center compactness until the center compactness reaches the maximum, and output the clustering result as grouped data, otherwise replace the initial center set.

3. The method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to claim 1, characterized in that: The method of performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio includes: The upper limit of the simulation time is , No. The vth temperature barrier coefficient at the moment is , the number of elements of the grouped data is , the energy efficiency value of the kth group data at the sth time is , the importance of the vth group data is , the contribution of the kth group data is , the energy efficiency of the cth simulation is , the initial energy efficiency value of the cth group data is , the energy efficiency ratio of the cth simulation is .

4. The method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to claim 1, characterized in that: The method for obtaining an optimization rate by comparing and analyzing the grouped data according to the energy efficiency ratio includes: The comparative analysis method is used to compare the energy efficiency ratio of the grouped data and calculate the optimization rate: The control factor is , No. The energy efficiency of the grouped data is , No. The energy efficiency of the grouped data is , No. The energy efficiency ratio of grouped data is , No. The energy efficiency ratio of grouped data is , No. Group data and The optimization rate of grouped data is .

5. The method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to claim 1, characterized in that: The method for obtaining the variable coefficient by performing correlation influence analysis on the relevant data includes: Calculate the influence of relevant data: The influence of the zth related data is The initial energy efficiency is , the energy efficiency value after the zth related data is changed is , the number of relevant data is N, and the adjustment factor is , the contribution of the zth relevant data to the change in energy efficiency is ; Calculate the correlation: The relevant data are , related data The number of , related data and energy efficiency value changes The correlation coefficient is , related data The control factor is , the zth related data and energy efficiency value changes The correlation degree is ; The relevant data with a correlation degree greater than 0.315 are regarded as key data, and the machine learning algorithm is used to analyze the impact of key data according to energy efficiency changes; Compute the coefficient of variation for correlated data: The predicted energy efficiency value of the zth related data is , the proportional control coefficient is , predict energy efficiency value The probability of occurrence is , and the predicted energy efficiency value appears and key data The probability of , the error coefficient is , the coefficient of variation of the zth related data is , the first weight is , the second weight is .

6. The method for optimizing the energy efficiency of a biosafety laboratory based on simulation according to claim 1, characterized in that: The method for optimizing the biosafety laboratory energy efficiency optimization model comprises: Introduce the search sub-population and perform chaotic mapping on the search sub-population. The expression is: The chaotic random number between 0 and 1 is b, The dimensions are , No. The dimensions are ; Calculate the fitness of the searcher, take the position of the particle with the largest fitness as the optimal searcher position, and update the position of the searcher. The expression is: The random numbers from 1 to 2 are , the position of the search sub-sub in the w-th dimension of the a-th search sub-sub in the t+1-th iteration of the exploration phase is , the search subposition of the a-th search sub in the w-th dimension in the t-th iteration is , the first random number from 0 to 1 is , the Cauchy operator is , the disturbance factor is , the optimal search sub-position is ; Update the search sub-position according to the search area to obtain the regional position. The expression is: The region position of the w-th dimension of the a-th searcher in the t+1-th iteration is , the attenuation factor is , the maximum number of iterations is , the adaptive inertia weight is , the current number of iterations is t, and the upper bound of the search area is , the lower bound of the search area is ; The corrected position is obtained by using the position updated by the standard deviation, and the expression is: The inhibition factor parameter is , a normally distributed random number The standard deviation of , the gamma function is , the region position of the w-th dimension of the a-th search sub-iteration in the t-th iteration is , the corrected position of the w-th dimension of the a-th searcher in the t+1-th iteration is , the lower bound of the w-th dimension is , the upper bound of the w-th dimension is ; The updated upper and lower bounds of the search area are expressed as: The updated upper bound of the search area is , the updated search area lower bound is ; Iterate continuously until the maximum number of iterations is reached, then stop iterating, otherwise update the adaptive inertia weight.

7. A biosafety laboratory energy efficiency optimization system based on simulation, used to execute the method described in any one of claims 1 to 6, characterized in that: include: Data acquisition module: used to collect simulation data and related data of the simulated biosafety laboratory, and pre-process the simulation data and the related data; Cluster evaluation module: used for comparing and clustering the simulation data according to the energy efficiency threshold to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio; Comparative analysis module: performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing correlation impact analysis on the related data to obtain a variable coefficient; Constructing an optimization module: used to construct a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimize the biosafety laboratory energy efficiency optimization model, input the data to be optimized into the biosafety laboratory energy efficiency optimization model, and output the optimization result.

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