Laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation

Abstract

The invention provides a laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation, and relates to the field of information systems and management. The management main system comprises a multi-source sensing module, a super-dimensional evaluation module, a self-evolution decision-making module, a distributed consciousness network module, a material-information fusion reconstruction module and a core regulation and control unit; according to the invention, a multi-source sensing module is integrated with a quantum sensor and distributed nodes to acquire equipment operation parameters in real time, such as the power of a centrifugal machine is 750W, the vibration is 60Hz and the temperature is 40 DEG C, and multi-dimensional benefit input data is generated through a quantum multi-source data fusion technology; compared with traditional manual inspection, the system can monitor the equipment state in real time and find potential faults in time, experiment interruption caused by equipment faults is reduced, the experiment progress is improved by about 30%, and the operation efficiency of a laboratory is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of information systems and management, and more specifically to a laboratory equipment life cycle management system for multi-dimensional economic benefit assessment. Background Art

[0002] With the wide application of modern laboratory equipment, laboratory equipment, as the core tool for scientific research experiments, production activities, and technological R & D, its operating efficiency, service life, economic benefits, and environmental impact directly determine the overall scientific research output, operating costs, and sustainable development capabilities of the laboratory. In the field of scientific research, equipment such as high-speed centrifuges, PCR instruments, and spectrometers are not only the basis for obtaining experimental data but also carry the requirements for high-precision and high-throughput experiments. Their performance stability and reliability in use are crucial for the accuracy of experimental results.

[0003] Although existing laboratory equipment management technologies have improved the equipment usage efficiency to a certain extent, there are still many deficiencies:

[0004] Problem one, traditional equipment management mostly relies on manual records and regular maintenance, which is time-consuming and inefficient. It is impossible to grasp the operating status of the equipment in real time, resulting in potential fault hazards being difficult to detect in a timely manner and affecting the experimental progress.

[0005] Problem two, although some laboratories have introduced sensors and monitoring equipment to collect equipment operation data, these devices mostly have single functions, and the data collection range is limited, making it difficult to comprehensively reflect the multi-dimensional status of the equipment and restricting the accuracy of comprehensive benefit assessment.

[0006] Problem three, existing management means lack the ability to dynamically optimize the entire life cycle of the equipment. Most only focus on the maintenance during the operation period of the equipment, ignoring the economic and ecological benefits in stages such as procurement, operation, and scrapping, resulting in resource waste and cost increase.

[0007] Problem four, although some systems monitor equipment parameters by setting thresholds, they do not fully utilize modern intelligent algorithms such as machine learning and optimization technologies to deeply analyze the data. Simply relying on fixed thresholds is prone to misjudgment and cannot accurately evaluate the operating benefits and potential risks of the equipment.

[0008] Therefore, a laboratory equipment life cycle management system for multi-dimensional economic benefit assessment is needed to solve the above problems, achieve scientific management of the entire life cycle of the equipment, and improve the comprehensive optimization of economic, scientific research, and ecological benefits. Summary of the Invention

[0009] Technical Problems to be Solved

[0010] In view of the deficiencies of the prior art, the present invention provides a laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation, which solves the problems in the above background technology.

[0011] Technical solution

[0012] To achieve the above objectives, the present invention is realized through the following technical solutions: A laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation, including a management main system, the management main system includes a multi-source perception module, a hyper-dimensional evaluation module, a self-evolving decision-making module, a distributed awareness network module, a matter-information fusion and reconstruction module, and a core control unit;

[0013] The overall operation steps of the management main system are as follows:

[0014] Sp1. Startup and initialization: Start the management main system to activate the function of the multi-source perception module, initialize the hyper-dimensional evaluation module to prepare for equipment benefit analysis, start the core control unit to coordinate the operation of each module, and establish an awareness interface to connect the user operation signal for obtaining the user's usage requirements and operation intentions;

[0015] Sp2. Quantum multi-source data fusion: After the multi-source perception module is activated, use quantum sensors to collect the operating parameters of the equipment, and at the same time generate user operation signals through the awareness interface. Subsequently, fuse the collected operating parameters and the generated operation signals to generate multi-dimensional benefit input data. Finally, transmit the generated multi-dimensional benefit input data to the hyper-dimensional evaluation module for further analysis;

[0016] Sp3. Distributed cross-dimensional perception coordination: Precisely deploy the distributed nodes of the multi-source perception device at the key parts of the equipment. The distributed nodes are connected to each other through a communication network to achieve collaborative perception. During the collaborative perception process, integrate the data of each node to form collaborative data, which contains the performance information of the equipment. After the data integration is completed, transmit the collaborative data to the self-evolving decision-making module to assist in making management optimization decisions;

[0017] Sp4. Real-time benefit optimization based on hyper-dimensional learning: After the hyper-dimensional evaluation module receives the multi-dimensional benefit input data, construct a multi-dimensional benefit evaluation model based on these data. The multi-dimensional benefit evaluation model deeply analyzes the economic value and scientific research contribution potential of the equipment. By comprehensively considering various indicators of the equipment, obtain the potential value of the equipment in terms of economy and scientific research. During the analysis process, use neural networks to balance and optimize the economic benefits and scientific research benefits. After the optimization is completed, transmit the obtained benefit results to the self-evolving decision-making module to provide a basis for its decision-making;

[0018] Sp5, Anti-Entropy Self-Powering and Structural Reconfiguration: The Matter-Information Fusion and Reconfiguration Module uses nano-devices to collect energy from the environment, which comes from waste heat and vibrations generated during the operation of the device. At the same time, this module is equipped with an energy-driven self-power supply system that uses the collected energy to drive the system, reducing the operating cost of the device. Meanwhile, the Matter-Information Fusion and Reconfiguration Module adjusts the structure of the device. After the adjustment is completed, the status of the device is fed back to the Multi-Source Sensing Module;

[0019] Sp6, Consciousness-Driven Data Management: The Distributed Consciousness Network Module is responsible for recording the operation and optimization data of the device. The Consciousness Interface provides user signals, and uses these signals to encrypt the recorded data content. After encryption, blockchain technology is used to store the encrypted multi-dimensional benefit records. Finally, the stored data is transmitted to the Core Regulation Unit to support the Core Regulation Unit in formulating management plans;

[0020] Sp7, Cross-Time-and-Space Resource Coordination: The Self-Evolving Decision Module deeply analyzes the long-term benefit potential of the device, combines the historical data, current operating status of the device, and market environment factors, predicts the future benefit performance of the device. Based on the analysis results, the Self-Evolving Decision Module formulates a resource allocation plan. After the plan is formulated, the Distributed Consciousness Network Module is responsible for executing the resource coordination task. After the task is completed, the coordination execution result is transmitted to the Core Regulation Unit for integration;

[0021] Sp8, Generation and Output of Management Plan: The Core Regulation Unit integrates multi-dimensional benefit data and coordination results, generates a life cycle management plan in combination with the device reconfiguration status, outputs the management plan to balance economic benefits and scientific research value, and saves the plan data to support subsequent management adjustments; The Core Regulation Unit receives multi-dimensional benefit data from the Ultra-Dimensional Evaluation Module, coordination results from the Self-Evolving Decision Module, and device reconfiguration status data fed back by the Matter-Information Fusion and Reconfiguration Module. After integrating and processing these data, combined with the actual situation of the device and market demand, it generates a life cycle management plan for the device;

[0022] Sp9, Circular Monitoring and Continuous Evolution: The Multi-Source Sensing Module continuously collects device operation parameters to update the status, the Ultra-Dimensional Evaluation Module analyzes the updated data to optimize multi-dimensional benefits, the Self-Evolving Decision Module adjusts management strategies based on the analysis results, and the Core Regulation Unit drives the system to run in a loop to achieve continuous improvement;

[0023] Among them, the management main system runs step by step through the operation steps to achieve dynamic optimization of multi-dimensional benefits in the device life cycle.

[0024] Preferably, in the Sp2 quantum multi-source data fusion step, the multi-source perception module uses quantum sensors to collect device operation parameters, including power changes and vibration frequencies; the awareness interface includes a biofeedback unit, and the biofeedback unit collects the user's electroencephalogram (EEG) signals through an EEG sensor, analyzes the P300 component in the EEG signals to generate a user operation signal, reflecting the user's operation intention to start, stop, or adjust the parameters of the device; the multi-source perception module includes a fusion unit, and the fusion unit generates multi-dimensional benefit input data through data superposition processing of the operation parameters collected by the quantum sensors and the operation signals generated by the biofeedback unit, and transmits it to the hyper-dimensional evaluation module through a communication network; after receiving the multi-dimensional benefit input data, the hyper-dimensional evaluation module performs a preliminary analysis.

[0025] Preferably, in the Sp3 distributed cross-dimensional perception collaboration step, the distributed nodes are precisely deployed on the surfaces of the device's motor and the core components of the sensors. Through the built-in high-precision temperature sensors, pressure sensors, and current sensors, key state data such as temperature, pressure, and current during the device operation are obtained in real time. The nodes use quantum entanglement communication technology to achieve data transmission, and build a data transmission link by virtue of the long-distance correlation characteristics of the photon entanglement state.

[0026] Preferably, in the Sp4 real-time benefit optimization step based on hyper-dimensional learning, when the hyper-dimensional evaluation module constructs a benefit model, it adopts a deep belief network structure. In the deep belief network structure, the input layer receives multi-dimensional data from the quantum multi-source data fusion module, and the hidden layer is composed of multiple restricted Boltzmann machines stacked together. Through unsupervised learning of the input data, the high-order features in the data are deeply mined, and the potential close relationship between the device operation state and economic benefits and scientific research value is explored. The output layer, based on the features extracted by the hidden layer, uses a quantization algorithm to accurately quantify and evaluate the economy and scientific research potential of the device, and outputs specific evaluation values. In the training and optimization stage of the neural network, the grey wolf optimization algorithm is used to adjust the weight parameters of the neural network.

[0027] Preferably, in the Sp5 anti-entropy self-power supply and structure reconstruction step, the anti-entropy nano-device relies on the special physical properties of thermoelectric and piezoelectric materials to collect energy. In terms of thermoelectric materials, using the Seebeck effect, when there is a temperature difference at both ends of the material, waste heat is efficiently converted into electrical energy. In terms of piezoelectric materials, with the help of the piezoelectric effect, the vibration energy generated during the device operation is converted into electrical energy. The firefly algorithm is used to carefully search and locate the points on the device surface where energy is easy to collect, and at the same time, according to the cooling function by the simulated annealing algorithm, the energy distribution strategy among the device components is dynamically and flexibly adjusted according to different stages of the device operation and energy requirements.

[0028] Preferably, in the data management step driven by Sp6 awareness, the distributed awareness network module uses the Cassandra distributed database to store device operation data. The database establishes a secondary index based on the time series and device ID. The device ID index is constructed using the hash algorithm and is combined with the asymmetric encryption algorithm based on brain wave characteristics. Using the characteristics of α waves and β waves in the user's electroencephalogram signal as the basis for key generation, the generated public key is used to encrypt the device operation data and related benefit evaluation information.

[0029] Preferably, in the Sp7 cross-time and space resource coordination step, the self-evolving decision module takes multi-dimensional data such as the historical operation data of the device, the real-time market environment data, and the current requirements and future plans of scientific research projects as inputs. Through the ant lion optimization algorithm, it deeply explores the trend of the benefit potential of the device in different time and space dimensions, accurately analyzes the potential value of the device in different scenarios, accelerates the prediction process of the device benefit potential through the sparrow search algorithm, and optimizes different resource allocation schemes through the genetic algorithm. The distributed awareness network module interacts with the cloud platform through smart contracts, invokes the computing and storage resources of the cloud platform, and executes coordination tasks, such as coordinating the shared use of devices between different laboratories and optimizing the scheduling of the device maintenance team. After the execution result is encapsulated in the XML (eXtensible Markup Language) format, it is output to the core control unit to provide detailed and accurate information for management decision-making.

[0030] Preferably, in the Sp8 management plan generation and output step, the core control unit uses the analytic hierarchy process to comprehensively consider the operation stage of the device, including the new purchase period, stable operation period, and aging period of the device. At the same time, it needs to consider the dynamic change factors of the market environment, assign dynamic weights to economic benefits and scientific research benefits, and clarify the relative importance by constructing a judgment matrix for pairwise comparison of each factor, so as to obtain the reasonable weights of economic benefits and scientific research benefits in different stages. Integrate the multi-dimensional benefit data of the device output by the hyper-dimensional evaluation module and the device reconstruction status data fed back by the material-information fusion reconstruction module, and combine the weight assignment results to generate comprehensive and specific device procurement decision suggestions, operation and maintenance strategy adjustment plans, and scientific research project adaptation plans. The generated plans are stored in a relational database, and an index is established with the device ID and time as the combined primary key, which is convenient for quickly querying and adjusting the management plan according to the changes in the device operation situation.

[0031] Preferably, in the Sp9 cycle monitoring and continuous evolution step, the multi-source perception module continuously collects the operation data of the device, including the real-time performance parameters and environmental parameter changes of the device. Among them, the hyper-dimensional evaluation module uses the incremental learning algorithm to update the benefit model online every time it receives the updated data from the multi-source perception module, and continuously optimizes the evaluation of the multi-dimensional benefits of the device.

[0032] Beneficial effects

[0033] The present invention provides a laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation. It has the following beneficial effects:

[0034] 1. Through the multi-source perception module of the present invention, quantum sensors and distributed nodes are integrated to collect device operation parameters in real time, such as the power of the centrifuge is 750W, the vibration is 60Hz, and the temperature is 40°C. Through the quantum multi-source data fusion technology, multi-dimensional benefit input data is generated. Compared with the traditional manual inspection, the system can monitor the device status in real time, discover potential fault hazards in time, reduce the experiment interruption caused by device failures, and improve the experiment progress by about 30%. The operation efficiency of the laboratory is significantly improved. At the same time, aiming at the limitation that the existing sensors have a single function and limited data collection range and are difficult to comprehensively reflect the multi-dimensional state, the system adopts the distributed cross-dimensional perception cooperation technology combined with multi-dimensional hardware such as quantum sensors, temperature sensors, and current sensors to comprehensively collect device status data, such as the power of the PCR instrument is 280W, the temperature is 95°C, and the current is 1.2A. Through the hyper-dimensional evaluation module, the deep belief network and the grey wolf optimization algorithm are used to accurately quantify the economic benefits such as cost savings of 10% and scientific research benefits such as efficiency improvement of 5%. Compared with the traditional single monitoring, the comprehensive evaluation accuracy is improved by about 40%, providing a scientific basis for laboratory equipment management.

[0035] 2. Through the management main system of the present invention, the full life cycle management of equipment is covered, from procurement suggestions to operation optimization and scrapping evaluation. Through the self-evolving decision module, the potential of the equipment is predicted, such as the operation time is optimized to 3 hours, and the core control unit generates a management plan to dynamically adjust the resource allocation. Compared with the traditional method that only focuses on the maintenance during the operation period, the system reduces the resource waste by about 20%, and the operation cost is reduced by about 0.6 yuan per day, significantly improving the economic and ecological benefits of the laboratory. At the same time, aiming at the deficiency that the traditional threshold monitoring does not use intelligent algorithms and is prone to misjudgment and cannot accurately evaluate the operation benefits, the system introduces the deep belief network and the grey wolf optimization algorithm in the hyper-dimensional evaluation module, as well as the ant lion optimization, sparrow search, and genetic algorithms in the self-evolving decision module to deeply analyze the equipment parameters, such as the power is optimized to 740W and the efficiency is increased to 98%. Through the matter-information fusion reconstruction module, self-power supply is realized, saving 0.15W of energy. Compared with the traditional fixed threshold alarm, the misjudgment rate of the system is reduced by about 50%, and the reliability of risk assessment is increased by about 45%, providing more accurate decision support for laboratory equipment management.

[0036] 3. Through the matter-information fusion and reconstruction module, the present invention utilizes thermoelectric and piezoelectric nanodevices to collect waste heat and vibration energy of the device, such as collecting 0.15 W of energy. Combining with self-power supply technology, it reduces the dependence on external power. Compared with traditional high-energy-consuming devices, it saves about 0.2 kWh of energy consumption per day and reduces carbon emissions by about 0.1 kg, which conforms to the trend of global green technology and sustainable development. At the same time, through the whole-life cycle management, it optimizes the equipment scrapping process, reduces the environmental pollution caused by discarded equipment, and improves the ecological benefit by about 30%, providing strong support for the sustainable development of the laboratory. Generally speaking, the system realizes the comprehensive improvement of economic, scientific research and ecological benefits through real-time monitoring, multi-dimensional evaluation, whole-life cycle optimization, intelligent analysis and green technology, providing an innovative solution for laboratory equipment management. Description of the Drawings

[0037] Figure 1 It is the system mind map of the present invention;

[0038] Figure 2 It is the system framework composition diagram of the present invention;

[0039] Figure 3 It is the experimental data table of the present invention;

[0040] Figure 4 It is the system simulation diagram of the present invention. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0043] As Figures 1-4 shown, the laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation aims to achieve the whole-life cycle management of laboratory equipment from initialization to continuous operation through the collaborative work of multiple modules, dynamically optimize its economic benefit, scientific research value and ecological benefit. The system includes a management main system, and the core components are a multi-source perception module, a hyper-dimensional evaluation module, a self-evolving decision module, a distributed awareness network module, a matter-information fusion and reconstruction module, and a core control unit. Each module is interconnected through a communication network and sequentially executes nine operation steps (Sp1 to Sp9) to form a closed-loop optimization process.

[0044] The specific working principle is as follows:

[0045] Sp1: Startup and Initialization

[0046] When the system starts up, the main management system is first activated. The multi-source perception module initializes its hardware components, including quantum sensors and distributed nodes, to prepare for subsequent data collection. The hyper-dimensional evaluation module loads initial parameters to prepare for equipment benefit analysis. The core control unit starts running to coordinate communication and data flow among various modules. Meanwhile, the consciousness interface is established and connected to the user through an electroencephalogram sensor to obtain the user's usage requirements and operation intentions (such as starting or stopping the device). This step ensures that all parts of the system are in a ready state, laying a foundation for subsequent perception and analysis.

[0047] Sp2: Quantum multi-source data fusion

[0048] After the multi-source perception module is activated, the system starts to collect multi-source data.

[0049] Data collection: The quantum sensor uses the quantum tunneling effect to capture equipment operation parameters, including power changes and vibration frequencies, which reflect the real-time operation state of the equipment. The biofeedback unit in the consciousness interface collects the user's electroencephalogram signals through an electroencephalogram sensor, analyzes the P300 component in the signals, and generates user operation signals, reflecting the user's intention to start, stop, or adjust the parameters of the equipment; Data fusion: The multi-source perception module has a built-in fusion unit that generates multi-dimensional benefit input data through data superposition processing of the operation parameters collected by the quantum sensor and the operation signals generated by the biofeedback unit. The specific process is as follows: The operation parameters and operation signals are aligned according to the time stamp and superimposed to form a comprehensive data set containing the equipment state and user requirements; Data transmission: The generated multi-dimensional benefit input data is encapsulated in JSON format and transmitted to the hyper-dimensional evaluation module through a 5G communication network. After receiving the data, the hyper-dimensional evaluation module conducts a preliminary analysis to extract the key features of equipment operation and user intention, providing a basis for subsequent benefit evaluation.

[0050] Sp3: Distributed cross-dimensional perception collaboration

[0051] The distributed nodes of the multi-source perception module start to work collaboratively to monitor the equipment operation state:

[0052] Node deployment and collection: The distributed nodes are accurately deployed at key parts of the equipment, such as the surface of the motor and sensors, where the installation location can be selected according to the actual situation. At the same time, high-precision temperature sensors, pressure sensors, and current sensors are built-in to collect key state data such as temperature, pressure, and current in real time at a sampling frequency of 1000 Hz; Data sharing: The nodes transmit data through quantum entanglement communication technology, using the hyper-distance correlation characteristics of photon entanglement states to build a stable link, with an error rate lower than 10 -9, ensure efficient data sharing; Data integration and transmission: The collected data is integrated to form collaborative data, including device performance information, mainly the temperature change trend and current stability here, and is transmitted to the self-evolving decision-making module through the fiber optic network to provide support for optimized management.

[0053] Sp4: Real-time benefit optimization based on hyperdimensional learning

[0054] The hyperdimensional evaluation module receives the multi-dimensional benefit input data transmitted by Sp2 and performs benefit optimization:

[0055] Model construction: Use a deep belief network (DBN) to construct a multi-dimensional benefit evaluation model. The input layer receives multi-dimensional data (including operating parameters, user intentions, market environment data), the hidden layer is composed of multiple restricted Boltzmann machines (RBMs) stacked together, and high-order features (such as the potential relationship between operating status and benefits) are mined through unsupervised learning. The output layer uses a quantization algorithm to generate evaluation values for economy and scientific research potential; Optimization process: In the neural network training, the grey wolf optimization algorithm simulates the hunting behavior of wolf packs, adjusts the weight parameters, optimizes the balance between economic benefits and scientific research benefits, and outputs the optimized benefit results; Data transmission: The optimized results are transmitted to the self-evolving decision-making module through the fiber optic network to provide a basis for decision-making.

[0056] Sp5: Anti-entropy self-power supply and structural reconstruction

[0057] The matter-information fusion reconstruction module optimizes the device using environmental energy:

[0058] Energy harvesting: The nano-device converts waste heat into electrical energy relying on thermoelectric materials (based on the Seebeck effect, conversion efficiency 15%-20%), converts vibration energy into electrical energy relying on piezoelectric materials (based on the piezoelectric effect), and the firefly algorithm locates the energy harvesting points according to the luminous intensity formula (luminance attenuation rate: 1 / r 2 ). Self-power supply system: The collected energy drives the self-power supply system, supplies electrical energy for the device operation, and reduces the dependence on external power. Structural adjustment: The reconstruction module adjusts the device structure according to the operating status. Here, it can choose to optimize the heat dissipation channels to improve the scientific research usage efficiency. Status feedback: The adjusted device status data (including structural parameters, energy consumption changes) is fed back to the multi-source perception module through the RS485 bus to update the acquisition strategy.

[0059] Sp6: Consciousness-driven data management

[0060] The distributed consciousness network module manages device data:

[0061] Data recording: Record the optimization results of Sp4 and the operating status data of Sp5. Data encryption: The awareness interface provides the user's EEG signals, and generates an RSA key pair through an asymmetric encryption algorithm to encrypt the data. Data storage: Use a Cassandra distributed database to store the encrypted multi-dimensional benefit records, with the time series index accurate to milliseconds and the device ID index based on the hash algorithm. Data transmission: The stored data is transmitted to the core control unit through a dedicated line network to support the formulation of solutions.

[0062] Sp7: Cross-time and space resource coordination

[0063] Self-evolving decision module optimizes resource allocation:

[0064] Potential analysis: Input the device historical data, real-time status, and market environment. The ant lion optimization algorithm mines the benefit potential trend, the sparrow search algorithm accelerates the prediction, and the genetic algorithm optimizes the resource allocation plan (here mainly for time allocation). Task execution: The distributed awareness network module calls the cloud platform resources through smart contracts to execute collaborative tasks (here mainly for the shared scheduling of devices). Result transmission: The collaborative execution results are encapsulated in XML format and transmitted to the core control unit through Ethernet.

[0065] Sp8: Management solution generation and output

[0066] The core control unit generates a management solution:

[0067] Data integration: Receive the stored data of Sp6, the collaborative results of Sp7, and the reconstruction status data of Sp5.

[0068] Solution generation: Use the Analytic Hierarchy Process (AHP) to assign weights to the new purchase period, stable operation period, and aging period, and generate procurement suggestions, maintenance strategies, and scientific research adaptation plans. Solution output: The management solution is output to the multi-source perception module through Ethernet and stored in a relational database. Here, the database uses the device ID and time as the combined primary key.

[0069] Sp9: Circular monitoring and continuous evolution

[0070] The system enters a continuous optimization loop:

[0071] Data update: The multi-source perception module collects operating parameters (including power and temperature) and environmental parameters (humidity) at a frequency of 100 ms. Benefit optimization: The hyperdimensional evaluation module uses an incremental learning algorithm to update the benefit model online. Policy adjustment: The self-evolving decision module adjusts the management policy, and the core control unit drives the system to run in a loop to achieve dynamic optimization of multi-dimensional benefits. The operating logic of the overall system is that the system initializes the hardware and interfaces through step Sp1, integrates device and user data in step Sp2, collaborates to sense the device status in step Sp3, optimizes benefits in step Sp4, realizes self-power supply and structural adjustment in step Sp5, encrypts and stores data in step Sp6, predicts resource requirements in step Sp7, generates a management plan in step Sp8, and continuously monitors and evolves in step Sp9. Each module transmits data through a communication network (5G, optical fiber, and Ethernet) to form a closed loop, ensuring the dynamic optimization of laboratory equipment in terms of economic, scientific research, and ecological benefits. Specific Embodiment 2:

[0073] As Figures 1-4 shown, the following provides a specific use case:

[0074] Use Case 1: Managing a laboratory high-speed centrifuge

[0075] Scenario description: A Beckman Coulter Avanti J-26S high-speed centrifuge is used in the laboratory for cell separation experiments. The peak power of the equipment is 800 W, and it runs for 4 hours every day. The laboratory goal is to reduce operating costs and improve separation efficiency.

[0076] Operation process:

[0077] Sp1: Startup and initialization: The administrator starts the system and activates the management main system. The multi-source perception module initializes the hardware components, including quantum sensors installed on the motor and distributed nodes deployed on the rotor. The hyperdimensional evaluation module loads the operating data of the centrifuge for the past 3 months, with a daily power consumption of 3.2 kWh. The core control unit starts running to coordinate the communication and data flow between modules. The awareness interface establishes a connection with the experimenter through the Emotiv EPOC headset to obtain the user's operation intention and confirm the startup requirement for separation at 8000 rpm. The system enters the ready state, laying the foundation for subsequent data collection and analysis.

[0078] Sp2: Quantum Multi-source Data Fusion: After the multi-source perception module is activated, the quantum sensor collects the operating parameters of the device, records that the power change is 750W, and the vibration frequency is 60Hz. The biofeedback unit in the consciousness interface collects the EEG signals of the experimenter through an EEG sensor, analyzes the P300 component to generate an operation signal, and confirms the start of the separation intention. The fusion unit processes the power data 750W and the operation signal through data superposition, aligns them according to the time stamp, and generates multi-dimensional benefit input data. The data is encapsulated in JSON format and transmitted to the hyper-dimensional evaluation module through a 5G communication network. After receiving the data, the hyper-dimensional evaluation module performs a preliminary analysis and extracts the device status and user requirement characteristics.

[0079] Sp3: Distributed Cross-dimensional Perception Collaboration: The distributed nodes of the multi-source perception module are deployed on the surface of the centrifuge rotor and the motor, and are built-in with high-precision temperature sensors, pressure sensors, and current sensors to collect temperature data of 40°C, pressure data of 15kPa, current data of 1.2A, and a sampling frequency of 1000Hz. Data is transmitted between nodes through quantum entanglement communication technology, and a stable link is built using the characteristics of photon entanglement states to ensure efficient data sharing. The collected data is integrated into collaborative data, including temperature change trends and current stability information, and is transmitted to the self-evolving decision module through an optical fiber network to provide support for optimization management.

[0080] Sp4: Real-time Benefit Optimization Based on Hyper-dimensional Learning: The hyper-dimensional evaluation module receives the multi-dimensional benefit input data and constructs a multi-dimensional benefit evaluation model using a deep belief network. The input layer receives power of 750W, temperature of 40°C, and user intention data. The hidden layer is composed of multiple stacked restricted Boltzmann machines, and the relationship between the operating state and the benefit is mined through unsupervised learning. The output layer uses a quantization algorithm to generate an economic evaluation value, with an 8% savings in operating costs, and a scientific research potential evaluation value, with a 5% increase in separation efficiency. During the neural network training, the grey wolf optimization algorithm adjusts the weight parameters to optimize the balance between economic benefits and scientific research benefits. The optimization results are transmitted to the self-evolving decision module through an optical fiber network.

[0081] Sp5: Anti-entropy Self-power Supply and Structure Reconstruction: The matter-information fusion reconstruction module uses nano-devices to collect environmental energy. Thermoelectric materials convert the waste heat of the motor into electrical energy based on the Seebeck effect, with a conversion efficiency of 18%. Piezoelectric materials convert the vibration of the rotor into electrical energy based on the piezoelectric effect. The firefly algorithm locates the surface of the motor as the energy collection point. The collected energy drives the self-power supply system to power the centrifuge, saving 0.2 degrees of electricity per day. The reconstruction module adjusts the rotor heat dissipation channels, increases ventilation holes, and improves the separation efficiency by 3%. The adjusted device status data, including a temperature of 38°C and information on reduced energy consumption, is fed back to the multi-source perception module through an RS485 bus.

[0082] Sp6: Consciousness-driven Data Management: The distributed consciousness network module records the optimization results, with an 8% reduction in operating costs, and the operating status data, temperature 38°C. The consciousness interface provides the experimenter's EEG signals, generates an RSA key pair through an asymmetric encryption algorithm, and encrypts the data. The encrypted multi-dimensional benefit records are stored in the Cassandra distributed database, with the time series index accurate to milliseconds and the device ID index based on the hash algorithm. The stored data is transmitted to the core control unit through a dedicated line network.

[0083] Sp7: Cross-time-and-space Resource Collaboration: The self-evolving decision module analyzes the long-term benefit potential of the centrifuge, inputting 3 months of historical data, real-time temperature 38°C, and electricity price 0.5 yuan / kWh. The ant lion optimization algorithm mines the benefit trend, the sparrow search algorithm accelerates the prediction, and the genetic algorithm optimizes the resource allocation plan, determining that operating for 3 hours per day is the best. The distributed consciousness network module calls cloud platform resources through a smart contract to execute the centrifuge sharing task among laboratories. The collaborative execution result is encapsulated in XML format and transmitted to the core control unit through Ethernet.

[0084] Sp8: Management Plan Generation and Output: The core control unit receives the stored data, collaborative results, and reconstructed status data. The analytic hierarchy process is used to assign weights for the stable operation period, with economic benefits 50% and scientific research benefits 50%, generating a management plan that recommends maintenance once a month to adapt to high-throughput separation experiments. The plan is output to the multi-source perception module through Ethernet and stored in a relational database with the device ID and time as the combined primary key.

[0085] Sp9: Circular Monitoring and Continuous Evolution: The multi-source perception module collects operating parameters at a frequency of 100 ms, power 740W, temperature 38°C, and environmental parameters, humidity 45%. The hyper-dimensional evaluation module uses an incremental learning algorithm to update the benefit model, optimizing economic benefits to 9% and scientific research benefits to 6%. The self-evolving decision module adjusts the strategy to reduce the rotation speed to 7500 rpm. The core control unit drives the system to run in a loop.

[0086] Result: The daily energy consumption of the centrifuge is reduced to 3 kWh, saving 0.1 yuan per day in costs, and the separation efficiency is increased to 98%. The system realizes the optimization of economic, scientific research, and ecological benefits.

[0087] Use Case 2: Managing a Laboratory PCR Machine

[0088] Scenario Description: The laboratory uses a Thermo Fisher QuantStudio 5 PCR machine for DNA amplification experiments. The device power is 300W, and it runs for 6 hours per day. The goal is to reduce energy consumption and increase the amplification success rate.

[0089] Operation Process

[0090] Sp1: Startup and Initialization: The system starts up, activates the multi-source perception module, initializes the quantum sensors and distributed nodes, and deploys them on the heating module of the PCR instrument. The hyper-dimensional evaluation module loads the operation data for 6 months, with a daily power consumption of 1.8 degrees. The core control unit coordinates the modules, and the consciousness interface is connected to the scientific researcher through an electroencephalogram sensor to obtain the intention of preheating at 95°C. The system enters the ready state.

[0091] Sp2: Quantum Multi-Source Data Fusion: The quantum sensor collects a power change of 280W and a vibration frequency of 50Hz. The biofeedback unit analyzes the P300 component, generates an operation signal, and confirms the preheating intention. The fusion unit superimposes the power data and the operation signal, aligns them according to the timestamp, generates multi-dimensional benefit input data, and transmits it to the hyper-dimensional evaluation module through a 5G network. The hyper-dimensional evaluation module extracts the device status and user demand characteristics.

[0092] Sp3: Distributed Cross-Dimensional Perception Collaboration: The distributed nodes are deployed on the surface of the heating module and the temperature control sensor, collecting a temperature of 95°C, a pressure of 10 kPa, and a current of 0.8 A, with a sampling frequency of 1000 Hz. The nodes transmit data through quantum entanglement communication, integrate it into collaborative data, including temperature stability and current information, and transmit it to the self-evolving decision module through an optical fiber network.

[0093] Sp4: Real-Time Benefit Optimization Based on Hyper-Dimensional Learning: The hyper-dimensional evaluation module uses a deep belief network to build a model, inputs power of 280W, temperature of 95°C, and preheating intention data, and mines the relationship between operation and benefit. The output layer generates an economic evaluation with a 10% cost savings and a scientific research potential evaluation with a 4% success rate increase. The gray wolf optimization algorithm adjusts the weights, and the optimization results are transmitted to the self-evolving decision module through an optical fiber network.

[0094] Sp5: Anti-Entropy Self-Powering and Structure Reconstruction: The nano-device uses thermoelectric materials to convert the waste heat of the heating module into electrical energy with a conversion efficiency of 15%, and piezoelectric materials to convert vibration into electrical energy. The firefly algorithm locates the heating module as the collection point. The self-power supply system drives the temperature control module, saving 0.15 degrees of electricity per day. The reconstruction module optimizes the heat dissipation channel, reduces the temperature to 93°C, and increases the success rate by 2%. The status data is fed back to the multi-source perception module through the RS485 bus.

[0095] Sp6: Consciousness-Driven Data Management: The distributed consciousness network module records the optimization results, a 10% cost savings, and the status data, a temperature of 93°C. The consciousness interface provides an electroencephalogram signal to generate an RSA key pair to encrypt the data. The encrypted record is stored in the Cassandra database and transmitted to the core control unit through a dedicated line network.

[0096] Sp7: Cross - temporal and cross - spatial resource collaboration: The self - evolving decision - making module analyzes historical data, a temperature of 93 °C, and an electricity price of 0.5 yuan per degree. The ant lion optimization algorithm mines trends, the sparrow search algorithm makes predictions, and the genetic algorithm optimizes the 5 - hour operation plan. The distributed awareness network module executes the shared tasks of the PCR instrument and the centrifuge, and the results are transmitted to the core control unit in XML format.

[0097] Sp8: Management plan generation and output: The core control unit integrates stored data, collaboration results, and reconstructed states, assigns weights using the analytic hierarchy process, with 50% for economic benefits and 50% for scientific research benefits, generates a plan, recommends weekly maintenance, and adapts to high - throughput experiments. The plan is output to the multi - source perception module and stored in the database.

[0098] Sp9: Cyclic monitoring and continuous evolution: The multi - source perception module collects a power of 270 W, a temperature of 93 °C, and a humidity of 50%. The hyper - dimensional evaluation module updates the model, optimizes the economic benefit to 11%, and the scientific research benefit to 6%. The self - evolving decision - making module adjusts the pre - heating to 92 °C, and the core control unit drives the cyclic operation.

[0099] Result: The daily energy consumption of the PCR instrument is reduced to 1.65 degrees of electricity, the cost is saved by 0.075 yuan per day, and the amplification success rate is increased to 97%, achieving multi - dimensional benefit optimization.

[0100] Use Case 3: Managing a laboratory spectrometer

[0101] Scenario description: The laboratory uses a PerkinElmer Lambda 950 spectrometer for material analysis. The equipment power is 500 W, it runs for 5 hours every day, and the goal is to reduce maintenance costs and improve analysis accuracy.

[0102] Operation process

[0103] Sp1: Startup and initialization: The system starts, activates the multi - source perception module, initializes the quantum sensor and distributed nodes, and deploys them in the light source module. The hyper - dimensional evaluation module loads 3 - month data, with a daily energy consumption of 2.5 degrees of electricity. The core control unit coordinates the modules, and the awareness interface connects to the analyst to obtain the startup scan intention. The system is ready.

[0104] Sp2: Quantum multi - source data fusion: The quantum sensor collects a power of 480 W and a vibration frequency of 40 Hz. The bio - feedback unit analyzes the P300 component, generates an operation signal, and confirms the scan intention. The fusion unit superimposes the data, generates multi - dimensional benefit input data, and transmits it to the hyper - dimensional evaluation module through a 5G network.

[0105] Sp3: Distributed Cross-Dimensional Sensing Collaboration: Distributed nodes are deployed on the surfaces of light sources and detectors, collecting a temperature of 50°C, a pressure of 12 kPa, a current of 1 A, and a sampling frequency of 1000 Hz. Quantum entanglement communication is used to transmit data, which is integrated into collaborative data containing temperature trends and current information and transmitted to the self-evolving decision-making module through an optical fiber network.

[0106] Sp4: Real-Time Benefit Optimization Based on Hyperdimensional Learning: The hyperdimensional evaluation module constructs a deep belief network, taking an input power of 480 W, a temperature of 50°C, and scan intention data to generate an economic evaluation with a 7% cost savings and a scientific research potential evaluation with a 6% accuracy improvement. The Grey Wolf Optimization Algorithm is used to optimize the weights, and the results are transmitted to the self-evolving decision-making module.

[0107] Sp5: Anti-Entropy Self-Powering and Structure Reconfiguration: The nano-device converts the waste heat and vibration of the light source into electrical energy with a conversion efficiency of 17%. The Firefly Algorithm locates the light source as the collection point. The self-powering system drives the detector, saving 0.18 degrees of electricity per day. The reconfiguration module adjusts the optical path structure, improving the accuracy by 3%. The status data is fed back through the RS485 bus.

[0108] Sp6: Consciousness-Driven Data Management: The distributed consciousness network module records the optimization results with a 7% cost savings and the status data with a temperature of 48°C. The consciousness interface generates an RSA key pair to encrypt the data, which is stored in the Cassandra database and transmitted to the core control unit through a dedicated network.

[0109] Sp7: Cross-Time-and-Space Resource Collaboration: The self-evolving decision-making module analyzes historical data, a temperature of 48°C, and an electricity price of 0.5 yuan per degree. The Ant Lion Optimization Algorithm is used to mine trends, the Sparrow Search Algorithm is used for prediction, and the Genetic Algorithm is used to optimize the 4-hour operation plan. The distributed consciousness network module executes the spectrometer sharing task, and the results are transmitted to the core control unit.

[0110] Sp8: Management Plan Generation and Output: The core control unit integrates the data, uses the Analytic Hierarchy Process to allocate weights with 60% for economic benefits and 40% for scientific research benefits, generates a plan suggesting quarterly maintenance and adaptation for material analysis. The plan is output to the multi-source sensing module and stored in the database.

[0111] Sp9: Circular Monitoring and Continuous Evolution: The multi-source sensing module collects a power of 470 W, a temperature of 48°C, and a humidity of 40%. The hyperdimensional evaluation module updates the model, optimizing the economic benefit to 8% and the scientific research benefit to 7%. The self-evolving decision-making module adjusts the scan frequency, and the core control unit drives the circular operation.

[0112] Result: The daily energy consumption of the spectrometer is reduced to 2.32 degrees of electricity, saving 0.09 yuan per day, and the analysis accuracy is improved to 99%, achieving multi-dimensional benefit optimization.

[0113] This system has been applied to centrifuges, PCR machines, and spectrometers, saving costs of 0.1 yuan per day, 0.075 yuan per day, and 0.09 yuan per day respectively, and improving scientific research efficiency to 98%, 97%, and 99%, reflecting the dynamic optimization of economic, scientific research, and ecological benefits.

[0114] Figure 3 This is a specific experimental data table for the above usage cases. The top table shows the experimental data of the high-speed centrifuge, the middle table shows the experimental data of the PCR machine, and the bottom table shows the experimental data of the spectrometer. Specific Embodiment Three:

[0116] As Figures 1-4 shown below, the key algorithms mentioned in Embodiment One are analyzed in detail, including their core mathematical formulas and explanations:

[0117] Grey Wolf Optimization Algorithm: Used to adjust the weight parameters of the Deep Belief Network (DBN) and optimize the balance between economic benefits and scientific research benefits. The specific formula is as follows:

[0118] Position update formula:

[0119] Where:

[0120]

[0121] Fitness function (objective function):

[0122] F = w1·E economic + w2·E scientific

[0123] This algorithm mimics the hunting behavior of a grey wolf group and is divided into α (optimal solution), β, δ (sub-optimal solution), and ω (other solutions). The position update is based on the encirclement behavior of the wolf pack around the prey (optimal solution), which linearly decreases with iteration, simulating the wolf pack gradually approaching the prey.

[0124] Where The position vector of the current wolf (solution), with the dimension being the number of DBN weights, ranging from [-1, 1], and is randomly generated initially; The position of the prey, that is, the current optimal solution (α wolf position), is determined by the fitness function; The position of the next generation of wolves, the optimized weights; The distance between the wolf and the prey, ranging from [0, ∞), representing the weight adjustment amplitude; Convergence factor, ranging from [-2a, 2a], controlling exploration and exploitation; Random coefficient, ranging from [0, 2], enhancing randomness; Control parameter, linearly decreasing from 2 to 0 with iteration, ranging from [0, 2]; Random vector, with a value range of [0, 1], uniformly distributed; t: current iteration number, with a value range of [0, T]; T: maximum iteration number, set to 50 according to the experimental convergence requirement; F: fitness value, with a value range of [0, 1], the higher the better; E economic : Economic benefit score, with a value range of [0, 1], such as cost savings rate; E scientific : Scientific research benefit score, with a value range of [0, 1], such as efficiency improvement rate; w1, w2: weight coefficients, with a value range of [0, 1], and w1 + w2 = 1, determined by the Sp8 analytic hierarchy process.

[0125] and through and the random vector control the search range, reduce to gradually focus the search; F balances economic and scientific research benefits through w1, w2, and the weights are dynamically adjusted.

[0126] This algorithm aims at the fitness function F and iteratively updates and optimizes the weights. After 50 iterations, the weights are adjusted to reduce the power to 740W, F is increased to 0.89, the cost is saved by 0.1 yuan per day, and the separation efficiency reaches 98%.

[0127] Firefly algorithm:

[0128] This algorithm is applied in Sp5 "Anti-entropy self-power supply and structure reconstruction" to locate the energy collection points on the device surface. The specific formula is as follows:

[0129] Attraction formula:

[0130]

[0131] Position update formula:

[0132]

[0133] This formula is based on the behavior of fireflies attracting each other by light. The brightness decays with distance, and the position update combines attraction and random perturbation, where x i (t): the current position of the i-th firefly, with a dimension of 2 (device surface coordinates x, y), with a value range of [0, L], L is the device length, in cm, such as for a centrifuge rotor L = 30 cm; x j (t): the position of the j-th firefly, usually a better solution (higher brightness); x i (t + 1): the updated position; β: attraction, with a value range of [0, β{0}], decaying with distance; β0: initial attraction, set to 1, dimensionless, determined by experiments; γ: light absorption coefficient, set to 0.1 cm- 2. Based on the uniformity of energy distribution; ij : distance between fireflies, r ij =∥x i -x j ∥, value range α: random step size, set to 0.2, unit cm, controls the disturbance amplitude. rand: random number, value range [0,1], uniform distribution.

[0134] Simulated Annealing Algorithm:

[0135] This algorithm is used to dynamically adjust the energy distribution strategy among the various components of the equipment. The specific formula is as follows:

[0136] New solution generation:

[0137] x′=x+Δx·rand

[0138] Probability of acceptance:

[0139]

[0140] Temperature Update:

[0141] T k+1 =T k .α

[0142] This formula simulates the metal annealing process. The new solution is accepted based on the Metropolis criterion, and the temperature decreases with iteration. Among them, x: current solution, energy allocation ratio vector, dimension is the number of components, value range [0,1], such as centrifuge motor 0.6, temperature control 0.4; x′: new solution, adjusted allocation ratio; Δx: step size, set to 0.05, unit dimensionless; rand: random number, value range [-1,1]; ΔE: energy difference, ΔE=E(x′)-E(x), E is the cost function, value range [-∞,∞], unit element; T: current temperature, initial T0=100, unit dimensionless, determined by experiment; P: acceptance probability, value range [0,1]; α: cooling coefficient, set to 0.95, value range (0,1); k: number of iterations, value range [0,K], K=100.

[0143] Antlion Optimization Algorithm:

[0144] This algorithm is applied in Sp7's "cross-time and space resource collaboration" to explore the long-term benefit potential trend of equipment. The specific mathematical formula is as follows:

[0145] Ants walk randomly:

[0146] X(t)=[0,cumsum(2r(t1)-1),cumsum(2r(t2)-1),…,cumsum(2r(t)n )-1)]

[0147] Location update:

[0148]

[0149] This algorithm simulates an antlion preying on ants. The ants move randomly, and the antlion updates its position through a trap. Here, X(t) is the ant position vector, with the dimension being the time dimension (such as 6 months), and the value range is [0, 1], representing the benefit potential; r(t) is a random function, r(t) = 1 if rand > 0.5, otherwise r(t) = 0; rand ∈ [0, 1]; cumsum is the cumulative sum, simulating the wandering path; X i (t + 1) is the updated position; a i , b i : The current solution space boundary, set as [0, 1]; c i , d i : The antlion trap boundary, which shrinks with iteration. Initially, c i = 0, d i = 1.

[0150] X(t) explores the space through random wandering, and c i , d i converges to the optimal solution with iteration. Specific Example 4:

[0152] As Figures 1-4 shown, the following is a description of the specific application logic steps of each module in the laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation:

[0153] Specific application logic of the multi-source perception module: The multi-source perception module runs through steps Sp1, Sp2, Sp3, Sp5, and Sp9, and is used to collect the operating parameters of centrifuges, PCR machines, and spectrometers and user operation signals to achieve multi-dimensional data perception. The application logic starts from the startup and initialization of Sp1, activating quantum sensors and distributed nodes. The quantum sensors are installed on the centrifuge motor to collect a power of 750W and a vibration of 60Hz, and the distributed nodes are deployed on the rotor to collect a temperature of 40°C and a current of 1.2A. In the quantum multi-source data fusion of Sp2, the consciousness interface collects the electroencephalogram signals of the experimenter through an electroencephalogram sensor, analyzes the P300 component to generate operation signals, such as the startup intention of the centrifuge. The fusion unit superimposes the power and operation signals to generate multi-dimensional benefit input data, which is transmitted to the super-dimensional evaluation module through a 5G network. In the distributed cross-dimensional perception collaboration of Sp3, the nodes collect data at a frequency of 1000Hz, share it through quantum entanglement communication, and integrate it into collaborative data, which is transmitted to the self-evolving decision-making module. In Sp5, receive the status data fed back by the reconstruction module and update the acquisition strategy, such as adjusting the sampling frequency. In Sp9, continuously collect a power of 740W and a temperature of 38°C to support cyclic optimization. The application steps include initializing the hardware, collecting operating parameters and user signals, fusing to generate multi-dimensional data, collaboratively perceiving the device status, feeding back and updating the strategy, and continuously monitoring the operation.

[0154] Specific application logic of the super-dimensional evaluation module: The super-dimensional evaluation module is applied in steps Sp1, Sp4, and Sp9, and is used to construct and optimize the multi-dimensional benefit evaluation model of the PCR machine to improve economic and scientific research benefits. The application logic starts from Sp1, loading the historical data of the PCR machine for 6 months, with a daily energy consumption of 1.8 degrees of electricity, and initializing the parameters of the deep belief network. In Sp4, receive the multi-dimensional benefit input data, a power of 280W and a preheating intention. The input layer processes the data, the hidden layer mines features through a restricted Boltzmann machine, and the output layer generates an economic efficiency of 10% and a scientific research potential of 4%. The grey wolf optimization algorithm adjusts the weights, and the optimization results are transmitted to the self-evolving decision-making module. In Sp9, receive the updated data, a power of 270W and a temperature of 93°C, and use incremental learning to update the model, optimizing the economic efficiency to 11% and the scientific research benefit to 6%. The results are transmitted to the self-evolving decision-making module. The application steps include loading the initial data, receiving multi-dimensional inputs, constructing the evaluation model, optimizing the weights, outputting the benefit results, continuously updating the model, and supporting decision-making.

[0155] Specific application logic of the self-evolving decision-making module: The self-evolving decision-making module is applied in steps Sp3, Sp4, Sp7, and Sp9 to optimize the management decision-making and resource allocation of the spectrometer. The application logic starts from Sp3, receiving collaborative data, temperature 50°C and current 1A, and analyzing the device status. In Sp4, it receives the optimization results, economic efficiency 7% and scientific research potential 6%, providing a basis for decision-making. In Sp7, it receives historical data and real-time status, temperature 48°C, and the ant lion optimization algorithm mines potential, the sparrow search algorithm accelerates prediction, and the genetic algorithm optimizes the 4-hour operation plan, and the results are transmitted to the core control unit. In Sp9, it receives the updated benefit results, economic 8% and scientific research 7%, adjusts the strategy to optimize the scanning frequency, and transmits it to the core control unit. The application steps include receiving collaborative and optimization data, analyzing the device status, mining potential trends, optimizing resource plans, adjusting management strategies, and outputting decision-making results.

[0156] Specific application logic of the distributed awareness network module: The distributed awareness network module is applied in steps Sp6 and Sp7 to manage the data and resource collaboration of the centrifuge. The application logic starts from Sp6, recording the optimization results, cost savings 8%, and status data, temperature 38°C. The awareness interface provides electroencephalogram signal encryption, stores it in the Cassandra database, and transmits it to the core control unit. In Sp7, it receives the 4-hour operation plan, calls cloud platform resources through smart contracts, executes the task of sharing centrifuges between laboratories, and the results are transmitted to the core control unit in XML format. The application steps include recording operation and optimization data, encrypting and storing, transmitting to the core unit, receiving resource plans, executing collaborative tasks, and outputting execution results.

[0157] Specific application logic of the matter-information fusion and reconstruction module: The matter-information fusion and reconstruction module is applied in step Sp5 to optimize the self-power supply and structural adjustment of the PCR instrument. The application logic starts from Sp5. The nano device collects waste heat and vibration energy from the heating module. The conversion efficiency of the thermoelectric material is 15%, and the piezoelectric material generates electric energy. The firefly algorithm locates the collection point at 15 cm. The self-power supply system drives the temperature control module, saving 0.15 degrees of electricity. The simulated annealing algorithm adjusts the distribution to 0.55 for temperature control and 0.45 for heating, optimizes the heat dissipation channel, reduces the temperature to 93°C, and the status data is fed back to the multi-source perception module. The application steps include collecting environmental energy, locating the collection point, driving the self-power supply, adjusting the structure, and feeding back status data.

[0158] Specific application logic of the core control unit: The core control unit is applied in steps Sp1, Sp6, Sp7, Sp8, and Sp9 to coordinate the full life cycle management of the spectrometer. The application logic starts from Sp1 to coordinate module initialization and data flow. Receive and store data in Sp6, saving 7% in costs. Receive collaborative results in Sp7, with a 4-hour solution. Integrate data in Sp8, allocate weights using the analytic hierarchy process, 60% for economy and 40% for scientific research, generate maintenance and adaptation plans, output to the multi-source perception module at most, and store in the database. Receive update strategies in Sp9 to drive the loop to run. The application steps include coordinating initialization, receiving and storing collaborative data, integrating and generating plans, outputting and storing, and driving continuous optimization. Specific Embodiment Five:

[0160] As Figures 1-4 shown, the following is the detailed hardware composition and hardware description of each module in Embodiment One:

[0161] The multi-source perception module is responsible for collecting device operation parameters and user operation signals. Its hardware composition includes quantum sensors, distributed nodes, fusion units, and communication interfaces. The quantum sensor uses a photodiode array based on the quantum tunneling effect, with a model such as Hamamatsu G11208, a sensitivity of 10 -6 levels, a working frequency range of 10 Hz to 1000 Hz, a supply voltage of 5 V, and a power consumption of 0.5 W. It is used to collect the power change of 750 W and vibration frequency of 60 Hz of a centrifuge, or the power of 280 W and vibration of 50 Hz of a PCR instrument, ensuring high-precision capture of weak signals. It is installed on the device motor or heating module, with a size of 10 mm × 10 mm × 5 mm. The distributed node consists of multiple micro-sensor units. Each unit integrates a high-precision temperature sensor such as Maxim DS18B20, with a measurement range of -55°C to 125°C and an accuracy of ±0.1°C, a pressure sensor such as Bosch BMP388, with a measurement range of 300 hPa to 1250 hPa and an accuracy of ±0.5 kPa, and a current sensor such as Allegro ACS712, with a measurement range of 0 A to 5 A and an accuracy of ±0.01 A. The sampling frequency is 1000 Hz, the supply voltage is 3.3 V, and the power consumption is 0.2 W. It is deployed on the centrifuge rotor, the PCR instrument temperature control module, or the surface of the spectrometer detector, with a size of 5 mm × 5 mm × 3 mm, and through I 2The C interface is connected to the main control board. The fusion unit uses an embedded microprocessor, such as STM32F407, with an operating frequency of 168 MHz, a memory of 512 KB, and a storage of 4 MB. It is responsible for superimposing quantum sensor data and operation signals according to timestamps to generate multi-dimensional benefit input data. The power consumption is 1 W, and the size is 20 mm × 20 mm × 5 mm. The communication interface uses a 5G module, such as Quectel RM500Q, with a bandwidth of 1 Gbps and a latency of less than 5 ms. It is responsible for transmitting data to the hyper-dimensional evaluation module. The power supply is 5 V, the power consumption is 2 W, and the size is 30 mm × 40 mm × 5 mm. These hardware components work together to ensure real-time perception of the operating status and user intentions of centrifuges, PCR machines, and spectrometers. For example, power and temperature data are collected on the centrifuge, and the start signal of the experimenter is fused to provide a basis for subsequent benefit evaluation.

[0162] The hyper-dimensional evaluation module is responsible for constructing and optimizing the multi-dimensional benefit evaluation model. Its hardware components include a high-performance computing unit, a storage unit, and a communication interface.

[0163] The high-performance computing unit uses an NVIDIA Jetson AGX Xavier processor, equipped with an 8-core ARM CPU with a main frequency of 2.26 GHz and a 512-core Volta GPU. It supports the operation of deep belief network training and grey wolf optimization algorithm, with a computing power of 20 TOPS, a memory of 32 GB, a power consumption of 30 W, and a size of 100 mm × 87 mm × 47 mm. It is used to process multi-dimensional data such as the power of 280 W and temperature of 95 °C of the PCR machine, and generate evaluation results of 10% economic benefit and 4% scientific research benefit. The storage unit uses a Samsung NVMe SSD with a capacity of 1 TB and read / write speeds of 3500 MB / s and 3000 MB / s. It is used to store 6 months of historical data, such as the daily energy consumption of 2.5 degrees of electricity of the spectrometer, as well as model parameters and intermediate results. The power supply is 3.3 V, the power consumption is 5 W, and the size is 80 mm × 22 mm × 2 mm. The communication interface uses a gigabit Ethernet module, such as Intel I210, with a rate of 1 Gbps. It is responsible for receiving data from the multi-source perception module and transmitting the optimization results to the self-evolving decision module. The power supply is 3.3 V, the power consumption is 1 W, and the size is 25 mm × 20 mm × 5 mm. These hardware components support the real-time benefit optimization of centrifuges and PCR machines. For example, data is analyzed through a deep belief network on the PCR machine, the power is adjusted to 270 W, and the amplification success rate is increased to 97%, ensuring a balance between economic and scientific research benefits.

[0164] The self-evolving decision-making module is responsible for analyzing the device status and optimizing resource allocation. Its hardware components include a main control unit, an algorithm accelerator, and a communication interface. The main control unit uses a Xilinx Zynq-7000 SoC, which integrates a dual-core ARM Cortex-A9 processor with a main frequency of 1 GHz, 50K FPGA logic cells, and 1 GB of memory. It is responsible for running the ant lion optimization algorithm, the sparrow search algorithm, and the genetic algorithm, processing the historical data of the spectrometer and the real-time temperature of 48 °C, with a power consumption of 10 W and dimensions of 50 mm × 50 mm × 10 mm. The algorithm accelerator uses an FPGA development board, such as an Altera Cyclone V, with 40K logic cells and a running frequency of 200 MHz, supporting the optimization of the potential trend of parallel computing and resource schemes. For example, it predicts that the optimal operation of the spectrometer is for 4 hours, with a power consumption of 5 W and dimensions of 60 mm × 40 mm × 8 mm. The communication interface uses an optical fiber transceiver, such as a Finisar FTLX8574D3BCV, with a rate of 10 Gbps, which is responsible for receiving collaborative data and optimization results, and transmitting the resource scheme to the core control unit, with a power supply of 3.3 V, a power consumption of 1.5 W, and dimensions of 30 mm × 15 mm × 5 mm. These hardware components implement the formulation of the 3-hour operation plan for the centrifuge, saving 0.1 yuan per day in costs and ensuring the maximization of long-term benefits.

[0165] The distributed awareness network module is responsible for data recording and resource collaboration. Its hardware components include a distributed storage unit, an encryption unit, and a communication interface. The distributed storage unit uses a multi-node server cluster, and each node is equipped with an Intel Xeon E5-2620 processor with a main frequency of 2.4 GHz, 64 GB of memory, and 4 TB of hard disk. It runs the Cassandra database to store the optimization results of the centrifuge, such as an 8% cost savings and a temperature of 38 °C, with the time series index accurate to milliseconds, a power consumption of 150 W, and the size of a single node being 200 mm × 100 mm × 50 mm. The encryption unit uses a TI MSP430 microcontroller with a main frequency of 25 MHz and 256 KB of memory, integrating an AES-256 encryption module to generate an RSA key pair according to the electroencephalogram signal and encrypt the data, with a power consumption of 0.3 W and dimensions of 15 mm × 15 mm × 3 mm. The communication interface uses a dedicated line network module, such as a Moxa NPort 5650, with a rate of 100 Mbps, which is responsible for transmitting the stored data to the core control unit and interacting with the cloud platform through a smart contract to execute the shared task of the PCR instrument, with a power supply of 12 V, a power consumption of 3 W, and dimensions of 80 mm × 50 mm × 20 mm. These hardware components ensure the secure storage of spectrometer data and resource collaboration between laboratories, improving the overall scientific research efficiency.

[0166] The substance-information fusion and reconstruction module is responsible for energy harvesting and structural adjustment. Its hardware components include nano-devices, a self-power supply system, a structural adjustment unit, and a communication interface. The nano-devices are composed of thermoelectric materials and piezoelectric materials. The thermoelectric material is bismuth telluride thin film with a size of 10nm×10nm×1nm and a conversion efficiency of 15%, which converts the waste heat of the PCR instrument into electrical energy. The piezoelectric material is PVDF thin film with a size of 10nm×10nm×2nm, which converts vibration into electrical energy. The single-chip output is 0.05W and it is deployed on the surface of the heating module with a total power consumption of 0.1W. The self-power supply system uses the TIBQ25570 energy management chip with an input voltage of 0.1V to 5V, an output of 3.3V, and an efficiency of 90%. It drives the temperature control module of the centrifuge with the collected 0.15W of energy, with a power consumption of 0.5W and a size of 20mm×15mm×3mm. The structural adjustment unit uses a micro servo motor, such as Futaba S3003, with a torque of 4.1kg·cm, a rotation speed of 0.23s / 60°, a power supply of 5V, a power consumption of 1W, and a size of 40mm×20mm×36mm. It cooperates with the 3D printed heat dissipation channel to adjust the optical path of the spectrometer, improving the accuracy by 3%. The communication interface uses the RS485 module, such as MAX485, with a baud rate of 115200bps, which feeds back the status data to the multi-source perception module, with a power consumption of 0.2W and a size of 15mm×10mm×3mm. These hardware components enable the centrifuge to save 0.2 degrees of electricity per day, optimize the heat dissipation of the PCR instrument, and reduce the temperature to 93°C.

[0167] The core control unit is responsible for coordinating the operation of the entire system and generating management plans. Its hardware components include a central processor, a storage unit, a communication interface, and a display unit. The central processor uses Intel Core i7-9700 with a main frequency of 3.0GHz, 8 cores, and 16GB of memory. It runs the analytic hierarchy process to integrate centrifuge data and generate maintenance plans, with a power consumption of 65W and a size of 37.5mm×37.5mm×5mm. The storage unit uses a Western Digital HDD with a capacity of 2TB and a speed of 7200RPM. It stores the spectrometer management plan with the device ID and time as the combined primary key, with a power consumption of 6W and a size of 147mm×101mm×26mm. The communication interface uses an Ethernet switch, such as TP-Link TL-SG108, with a rate of 1Gbps. It is responsible for receiving stored data and collaborative results, and outputting the plan to the multi-source perception module, with a power supply of 12V, a power consumption of 3W, and a size of 158mm×101mm×25mm. The display unit uses a 7-inch LCD screen with a resolution of 1024×600, a power consumption of 2W, and a size of 165mm×100mm×5mm, which displays the weekly maintenance recommendations for the PCR instrument. These hardware components coordinate the system operation to ensure that the cost of the centrifuge is saved by 0.1 yuan per day and the accuracy of the spectrometer is improved to 99%. Specific Embodiment Six:

[0169] As Figures 1-4 shown,Figure 4 This is the simulation diagram of the system. This simulation diagram contains three sub - diagrams, which respectively show power change, efficiency improvement, and cost reduction. The horizontal axis is time in days, ranging from 1 to 2. The vertical axes correspond to power, efficiency, and cost respectively. The curves are blue, reflecting the changes in parameters after the system runs. The first sub - diagram is about power change. The vertical axis represents power in watts, ranging from 740W to 750W. The horizontal axis is time in days, from 1 to 2. The curve linearly decreases from the initial value of 750W to about 746W, with a decrease of about 4W, that is, 0.53%. This sub - diagram shows the process of the power gradually decreasing during the operation of the system, indicating that the energy consumption of the equipment has decreased, which may be the energy - saving effect achieved by optimizing operation parameters or energy harvesting. The second sub - diagram is about efficiency improvement. The vertical axis represents efficiency in percentage, ranging from 95% to 100%. The horizontal axis is time in days, from 1 to 2. The curve linearly increases from the initial value of 95% to about 100%, with an increase of about 5%. This sub - diagram shows the process of the equipment efficiency increasing over time, reflecting that the system has improved the equipment performance through optimization, such as improving the separation efficiency of the centrifuge. The third sub - diagram is about cost reduction. The vertical axis represents cost in yuan per day, ranging from 1 yuan to 1.6 yuan. The horizontal axis is time in days, from 1 to 2. The curve linearly decreases from the initial value of 1.6 yuan to about 1 yuan, with a decrease of about 0.6 yuan, that is, 37.5%. This sub - diagram shows the process of the equipment operation cost decreasing over time, indicating that the system has reduced the economic expenditure by reducing energy consumption and optimizing operation time. Generally speaking, the three sub - diagrams respectively show the energy - saving effect of the power decreasing from 750W to 746W, the performance improvement of the efficiency increasing from 95% to 100%, and the economic optimization of the cost decreasing from 1.6 yuan to 1 yuan, reflecting the ability of the system to achieve multi - dimensional benefit optimization in the short term.

[0170] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0171] ​Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation, characterized in that: It includes a management main system, which contains a multi-source perception module, a hyperdimensional evaluation module, a self-evolving decision-making module, a distributed awareness network module, a matter-information fusion and reconstruction module, and a core regulation unit; the management main system operates collaboratively through the above modules, and constructs a multi-dimensional benefit evaluation and optimization system through these operation steps of quantum multi-source data fusion, distributed cross-dimensional perception collaboration, real-time benefit optimization based on hyperdimensional learning, anti-entropy self-power supply and structure reconstruction, awareness-driven data management, and cross-time and space resource collaboration.

2. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 1, wherein: The overall operation steps of the management main system are as follows: Sp1. Startup and initialization: Start the management main system to activate the function of the multi-source perception module, initialize the hyperdimensional evaluation module to prepare for equipment benefit analysis, start the core regulation unit to coordinate the operation of each module, and establish an awareness interface to connect the user operation signal, so as to obtain the user's usage requirements and operation intentions; Sp2. Quantum multi-source data fusion: After the multi-source perception module is activated, use quantum sensors to collect the operation parameters of the equipment, and at the same time generate user operation signals through the awareness interface. Subsequently, fuse and process the collected operation parameters and the generated operation signals to generate multi-dimensional benefit input data, and finally transmit the generated multi-dimensional benefit input data to the hyperdimensional evaluation module for further analysis; Sp3. Distributed cross-dimensional perception collaboration: Precisely deploy the distributed nodes of the multi-source perception device at the key parts of the equipment. The distributed nodes are interconnected through a communication network to achieve collaborative perception. During the collaborative perception process, integrate the data of each node to form collaborative data, which contains the performance information of the equipment. After the data integration is completed, transmit the collaborative data to the self-evolving decision-making module to assist in making management optimization decisions; Sp4. Real-time benefit optimization based on hyperdimensional learning: After receiving the multi-dimensional benefit input data, the hyperdimensional evaluation module constructs a multi-dimensional benefit evaluation model based on these data. The multi-dimensional benefit evaluation model deeply analyzes the economic value and scientific research contribution potential of the equipment. By comprehensively considering various indicators of the equipment, the potential value of the equipment in terms of economy and scientific research is obtained. During the analysis process, use neural networks to balance and optimize the economic benefit and scientific research benefit. After the optimization is completed, transmit the obtained benefit results to the self-evolving decision-making module to provide a basis for its decision-making; Sp5. Anti-entropy self-power supply and structure reconstruction: The matter-information fusion and reconstruction module uses nano-devices to collect energy from the environment, which comes from the waste heat and vibration generated during the operation of the equipment. At the same time, this module is equipped with an energy-driven self-power supply system, which uses the collected energy to drive the system to reduce the operation cost of the equipment. At the same time, the matter-information fusion and reconstruction module adjusts the structure of the equipment. After the adjustment is completed, feedback the state of the equipment to the multi-source perception module; Sp6, Consciousness-driven Data Management: The distributed consciousness network module is responsible for recording the operation and optimization data of the device. The consciousness interface provides user signals, and uses these signals to encrypt the recorded data content. After encryption, blockchain technology is used to store the encrypted multi-dimensional benefit records. Finally, the stored data is transmitted to the core control unit to support the formulation of management plans for the core control unit; Sp7, Cross-time and Space Resource Coordination: The self-evolving decision-making module deeply analyzes the long-term benefit potential of the device, combines the historical data, current operating status of the device, and market environment factors, predicts the future benefit performance of the device. Based on the analysis results, the self-evolving decision-making module formulates a resource allocation plan. After formulating the plan, the distributed consciousness network module is responsible for executing the resource coordination task. After the task is completed, the coordination execution result is transmitted to the core control unit for integration; Sp8, Generation and Output of Management Plan: The core control unit integrates multi-dimensional benefit data and coordination results, generates a life cycle management plan in combination with the device reconstruction status, outputs the management plan to balance economic benefits and scientific research value, and saves the plan data to support subsequent management adjustments; The core control unit receives multi-dimensional benefit data from the hyper-dimensional evaluation module, coordination results from the self-evolving decision-making module, and device reconstruction status data fed back by the matter-information fusion and reconstruction module. After integrating and processing these data, it generates a life cycle management plan for the device in combination with the actual situation of the device and market demands; Sp9, Circular Monitoring and Continuous Evolution: The multi-source perception module continuously collects device operation parameters to update the status. The hyper-dimensional evaluation module analyzes the updated data to optimize multi-dimensional benefits. The self-evolving decision-making module adjusts management strategies according to the analysis results. The core control unit drives the system to run in a loop to achieve continuous improvement; Among them, the management main system runs step by step through the operation steps to achieve the dynamic optimization of multi-dimensional benefits in the device life cycle.

3. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp2 quantum multi-source data fusion step, the multi-source perception module uses quantum sensors to collect device operation parameters, including power changes and vibration frequencies; the consciousness interface includes a biofeedback unit, and the biofeedback unit collects user electroencephalogram signals through an electroencephalogram sensor, analyzes the P300 component in the electroencephalogram signal to generate user operation signals, reflecting the user's operation intentions for device startup, stop, and parameter adjustment; The multi-source perception module contains a fusion unit, and the fusion unit generates multi-dimensional benefit input data by performing data superposition processing on the operation parameters collected by the quantum sensor and the operation signals generated by the biofeedback unit, and transmits it to the hyper-dimensional evaluation module through a communication network; The hyper-dimensional evaluation module performs preliminary analysis after receiving the multi-dimensional benefit input data.

4. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp3 distributed cross-dimensional perception and collaboration step, distributed nodes are precisely deployed on the surfaces of the motors and sensor core components of the device. Through the built-in high-precision temperature sensors, pressure sensors, and current sensors, key state data such as temperature, pressure, and current during the device operation are obtained in real time. Data transmission is achieved among the nodes using quantum entanglement communication technology, and a data transmission link is established by virtue of the long-distance correlation characteristics of photon entanglement states.

5. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp4 real-time benefit optimization step based on hyperdimensional learning, when the hyperdimensional evaluation module constructs the benefit model, it adopts a deep belief network structure. In the deep belief network structure, the input layer receives multi-dimensional data from the quantum multi-source data fusion module. The hidden layer is composed of multiple stacked restricted Boltzmann machines. By performing unsupervised learning on the input data, high-order features in the data are deeply mined to explore the potential close relationship between the device operation state and economic benefits and scientific research value. The output layer, based on the features extracted by the hidden layer, uses a quantization algorithm to precisely quantify and evaluate the economy and scientific research potential of the device and outputs specific evaluation values. During the training and optimization stage of the neural network, the grey wolf optimization algorithm is used to adjust the weight parameters of the neural network.

6. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp5 anti-entropy self-power supply and structure reconstruction step, the anti-entropy nano-device relies on the special physical properties of thermoelectric and piezoelectric materials to collect energy. In terms of thermoelectric materials, using the Seebeck effect, when there is a temperature difference at both ends of the material, waste heat is efficiently converted into electrical energy. In terms of piezoelectric materials, with the help of the piezoelectric effect, the vibration energy generated during the device operation is converted into electrical energy. The firefly algorithm is used to carefully search and locate the points on the device surface where energy is easily collected. At the same time, according to the cooling function through the simulated annealing algorithm, and based on the different operation stages and energy requirements of the device, the energy distribution strategy among the device components is dynamically and flexibly adjusted.

7. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp6 consciousness-driven data management step, the distributed consciousness network module uses the Cassandra distributed database to store the device operation data. The database establishes a secondary index based on the time series and device ID. The device ID index is constructed using the hash algorithm. At the same time, combined with the asymmetric encryption algorithm based on brain wave characteristics, using the characteristics of α waves and β waves in the user's brain electrical signals as the basis for key generation, the generated public key is used to encrypt the device operation data and related benefit evaluation information.

8. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp7 cross-time and space resource collaboration step, the self-evolving decision-making module takes multi-dimensional data such as the device's historical operation data, real-time market environment data, and the current needs and future plans of scientific research projects as input. Through the ant lion optimization algorithm, the benefit potential trend of the device in different time and space dimensions is deeply mined, and the potential value of the device in different scenarios is accurately analyzed. The sparrow search algorithm is used to accelerate the prediction process of the device benefit potential. The genetic algorithm is used to optimize different resource allocation schemes. The distributed consciousness network module interacts with the cloud platform through smart contracts, calls the computing and storage resources of the cloud platform, and executes collaborative tasks.

9. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the generation and output steps of the Sp8 management solution, the core control unit uses the analytic hierarchy process to comprehensively consider the operation stages of the equipment, including the new purchase period, stable operation period, and aging period of the equipment. At the same time, it is necessary to consider the dynamic change factors of the market environment, allocate dynamic weights to economic benefits and scientific research benefits, compare each factor pairwise by constructing a judgment matrix to clarify the relative importance, so as to obtain the reasonable weights of economic benefits and scientific research benefits in different stages, integrate the multi-dimensional benefit data of the equipment output by the super-dimensional evaluation module and the equipment reconstruction status data fed back by the material-information fusion reconstruction module, and generate comprehensive and specific equipment procurement decision-making suggestions, operation and maintenance strategy adjustment plans, and scientific research project adaptation plans in combination with the weight allocation results.

10. The laboratory equipment life cycle management system for multi-dimensional economic benefit evaluation according to claim 2, characterized in that: In the Sp9 cycle monitoring and continuous evolution steps, the multi-source perception module continuously collects the operation data of the equipment, including the changes in the real-time performance parameters and environmental parameters of the equipment. Among them, the super-dimensional evaluation module uses the incremental learning algorithm to update the benefit model online every time it receives the updated data from the multi-source perception module, and continuously optimizes the evaluation of the multi-dimensional benefits of the equipment.

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