Laboratory equipment intelligent scheduling method and system based on environmental parameter driving

Abstract

The present application relates to the technical field of equipment scheduling, and discloses a laboratory equipment intelligent scheduling method and system based on environmental parameter driving, which is used to solve the problem that the mutual influence relationship between devices changes when scheduling the devices, comprising: for each device, obtaining each disturbance parameter, evaluating a mutual influence index according to the disturbance parameter, and screening the devices with mutual exclusion restrictions according to the mutual influence index; performing mutual exclusion judgment updating operation between the devices with mutual exclusion restrictions, obtaining a dynamic device mutual exclusion relationship, updating an initial device operation requirement set according to the dynamic device mutual exclusion relationship, obtaining an actual device operation requirement set, screening all devices that meet the starting conditions, and recording as an initial device set that meets the starting conditions; and screening out the devices that only do not meet the environmental conditions as candidate devices, thereby effectively improving the experimental efficiency and improving the accuracy of the experimental results.

Description

Technical Field

[0001] This invention relates to the field of equipment scheduling technology, and more specifically to a method and system for intelligent scheduling of laboratory equipment driven by environmental parameters. Background Technology

[0002] Laboratories, as crucial infrastructure for scientific research, testing, and precision manufacturing, typically integrate various types of experimental instruments, environmental protection systems, and automated control devices to support the stringent requirements of high-precision experiments regarding temperature, humidity, airflow organization, electromagnetic environment, and power supply quality. With the expansion of laboratory scale and the increase in experimental tasks, the parallel operation of multiple devices and the unified control of environmental conditions have gradually become important requirements for laboratory operation and maintenance. To support the stable operation of laboratories, existing systems generally deploy environmental monitoring equipment, air conditioning supply and exhaust systems, power supply and distribution systems, and related control platforms to achieve real-time monitoring and basic adjustment of the experimental environment, thereby ensuring that experimental equipment operates in a suitable environment and improving experimental safety and the reliability of results.

[0003] Existing laboratory equipment scheduling systems typically execute equipment startup procedures and operational coordination based on equipment operating requirements, environmental monitoring data, and pre-set equipment relationship configuration tables. Specifically, upon receiving a startup command, the system determines whether the equipment meets the startup conditions based on the current environmental conditions. It also refers to the configuration table to determine whether there are simultaneous startup or mutual exclusion relationships between devices, thus avoiding interference caused by conflicting operating characteristics. This mechanism can meet general operational needs in typical experimental scenarios, ensuring that equipment can conduct experimental activities in a basically controllable environment.

[0004] However, the above-mentioned technologies have at least the following technical problems: In real-world operating environments, the interactions between experimental devices are often not constant but are influenced by factors such as relative device positions, spatial layout, infrastructure status, and changes in the laboratory environment, exhibiting certain spatial coupling characteristics. Existing scheduling systems generally rely on static configuration tables to determine whether devices have mutual exclusion relationships. However, this configuration method cannot reflect the actual differences in the scope of impact caused by adjustments to device positions, changes in operating status, or changes in environmental conditions. When the static configuration table is inconsistent with the actual operating environment, it may mistakenly classify devices that could operate simultaneously as mutually exclusive, thereby reducing experimental efficiency; it may also overlook actual interference risks, causing sensitive devices to operate in unsuitable environments, leading to deviations in experimental results or equipment malfunctions. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for intelligent scheduling of laboratory equipment based on environmental parameters, so as to solve the problems existing in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The intelligent scheduling method for laboratory equipment driven by environmental parameters includes the following steps: Step 1: Within the current scheduling cycle, collect environmental monitoring data for the laboratory using a unified sampling period. This data includes temperature, humidity, air supply volume, exhaust volume, electromagnetic environment, and power supply / distribution status. An environmental parameter sequence is then formed based on this data. Step 2: Based on the operational requirements of the experimental equipment, extract the environmental conditions required for the startup of each device. These conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply / distribution status. Simultaneously, determine whether there are simultaneous startup requirements or environmental pre-adjustment requirements among the devices, forming an initial set of equipment operational requirements. Step 3: For each device, acquire various disturbance parameters, including those related to the mounting base. Step 4: For devices with mutual exclusion restrictions, perform a mutual exclusion determination update operation to obtain dynamic device mutual exclusion relationships. Update the initial device operation requirement set according to the dynamic device mutual exclusion relationships to obtain the actual device operation requirement set. Step 5: Compare the environmental parameter sequence with the actual device operation requirement set item by item. Determine whether the current environment meets the start-up conditions of each device based on the environmental conditions, start-up requirements, and dynamic device mutual exclusion relationships. Filter all devices that meet the start-up conditions. The devices that meet the startup conditions are denoted as the initial set of devices that meet the startup conditions; devices that only fail to meet the environmental conditions are selected as candidate devices; Step 6: For candidate devices, the environmental parameters that need to be adjusted are determined according to the environmental pre-adjustment requirements. The environmental parameters include temperature, humidity, ventilation volume or power supply parameters, and an environmental adjustment command is issued; after the environmental parameters reach the environmental conditions required for device startup, the device is added to the initial set of devices that meet the startup conditions, resulting in the final set of devices that meet the startup conditions; Step 7: Start-up commands are issued to the devices that finally meet the startup conditions; The step of obtaining the mutual exclusion influence index is as follows: by setting an acceleration sensor on the mounting base, The micro-vibration acceleration signal within the current scheduling cycle is acquired using an accelerometer, and the micro-vibration mutual exclusion influence coefficient is calculated based on the micro-vibration acceleration signal. The ground vibration transmission ratio at the contact point between the equipment and the ground is acquired, and the vibration transmission coupling influence coefficient is calculated based on the ground vibration transmission ratio. The voltage signal at the power supply end of the equipment is acquired using a voltage monitoring sensor at the equipment end, and the power fluctuation influence coefficient is calculated based on the voltage signal. The micro-vibration mutual exclusion influence coefficient, vibration transmission coupling influence coefficient, and power fluctuation influence coefficient are normalized, and the mutual exclusion influence index is calculated based on the normalized micro-vibration mutual exclusion influence coefficient, vibration transmission coupling influence coefficient, and power fluctuation influence coefficient.

[0007] Preferably, the steps for obtaining the initial equipment operation requirement set are as follows: Read the operation requirement data of each experimental device from the equipment operation requirement library. The operation requirement data includes startup conditions, records of coordinated startup, and records of pre-start environmental preparation. For each device, based on the startup conditions recorded in its operation requirement data, extract the environmental conditions required for startup. These environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply / distribution conditions. Based on the records of coordinated startup in the operation requirement data, determine whether there is a simultaneous startup requirement between devices. For devices with simultaneous startup requirements, mark the simultaneous startup requirement identifier in their corresponding operation requirement data and record the set of device numbers that start synchronously with them. Based on the records of pre-start environmental preparation in the operation requirement data, determine whether there is an environmental pre-adjustment requirement for the device. For devices with environmental pre-adjustment requirements, mark the environmental pre-adjustment requirement identifier in their operation requirement data. For each device, summarize the device's environmental conditions, simultaneous startup requirement identifier, and environmental pre-adjustment requirement identifier by device number to form the device's operation requirements. Summarize the operation requirements of each device by device number to form the initial equipment operation requirement set.

[0008] Preferably, the steps for obtaining the micro-vibration mutual exclusion influence coefficient are as follows: Within the current scheduling cycle, collect the micro-vibration acceleration signal of the mounting base of each device at a uniform sampling period; divide the micro-vibration acceleration signal into time windows, calculate the variance of the signal within each time window, and calculate the mean of all variances to obtain the vibration stability of the device; set up reference measuring points on the common foundation or supporting structure of the laboratory, collect the environmental acceleration signal of the reference measuring points, record it as the reference signal, and record it simultaneously with the vibration response of each device to form an environmental reference sequence; within each time window, calculate the square of the difference between the device's micro-vibration acceleration signal and the reference signal at all times, and take the square root of the average value to obtain the root mean square difference of the vibration response for that time window; calculate the mean of the root mean square differences of the vibration response for all time windows to obtain the vibration response difference degree; divide the response difference degree by the sum of the vibration stability degree and 1 to obtain the micro-vibration mutual exclusion influence coefficient.

[0009] Preferably, the step of obtaining the vibration coupling influence coefficient is as follows: within the current scheduling cycle, obtain the ground vibration ratio at the contact point between the equipment and the ground, and construct a vibration ratio sequence; obtain the maximum and minimum vibration ratios in the vibration ratio sequence, and calculate the difference between the maximum and minimum vibration ratios to obtain the range value; calculate the ratio between the minimum vibration ratio and the range value to obtain the vibration stability factor, and take the reciprocal of the vibration stability factor to obtain the vibration coupling influence coefficient.

[0010] Preferably, the steps for obtaining the power supply fluctuation impact coefficient are as follows: within the current scheduling cycle, the voltage signal at the power supply end of the equipment is collected by the equipment-side voltage monitoring sensor at a uniform sampling period to form a short-time voltage sampling sequence; the voltage change amplitude is calculated between adjacent sampling points of the voltage sampling sequence, and the absolute value of all voltage change amplitudes is taken and the mean is calculated to obtain the short-time voltage fluctuation amplitude; the mean of the data in the voltage sampling sequence is calculated to obtain the average power supply voltage, and the average power supply voltage is divided by the sum of the short-time voltage fluctuation amplitude and 1 to obtain the power supply stability; the reciprocal of the power supply stability is taken to obtain the power supply fluctuation impact coefficient.

[0011] Preferably, the step of filtering out devices with mutual exclusion restrictions based on the mutual exclusion influence index is as follows: comparing the mutual exclusion influence index with the influence threshold; if the mutual exclusion influence index is greater than or equal to the influence threshold, it is determined that the device will be affected by device mutual exclusion, and the device is recorded as a device with mutual exclusion restrictions; if the mutual exclusion influence index is less than the influence threshold, it is determined that the device will not be affected by device mutual exclusion.

[0012] Preferably, the steps for obtaining the dynamic device mutual exclusion relationship are as follows: Within the current scheduling cycle, for all devices with mutual exclusion restrictions, candidate device pairs are generated in a pairwise combination manner. The spatial coordinates, installation area number, and room number of each device are obtained. The spatial distance between candidate device pairs is calculated using Euclidean distance, and the spatial distance of all candidate device pairs is obtained to construct a device spatial location matrix. The average disturbance parameter of each device within the scheduling cycle is obtained. A device disturbance parameter vector is constructed based on the average disturbance parameter. The disturbance parameters are multiplied to obtain the device disturbance source intensity. The structural vibration transmission baseline values ​​of all candidate device pairs measured during the laboratory commissioning phase are called, and a structural vibration transmission baseline matrix is ​​constructed. Based on the device disturbance source intensity, the average disturbance parameter of each device within the scheduling cycle is obtained. The spatial distance of all candidate device pairs is obtained to construct a structural vibration transmission baseline matrix. The spatial distance of all candidate device pairs is calculated using Euclidean distance, and the ... The mutual exclusion influence intensity of candidate equipment pairs is calculated by taking the structural vibration baseline value of the selected equipment pairs and the spatial distance between the candidate equipment pairs. The mutual exclusion influence intensity of all candidate equipment pairs is obtained and compared with the mutual exclusion threshold. If the mutual exclusion influence intensity is greater than or equal to the mutual exclusion threshold, the candidate equipment pairs are determined to have a mutual exclusion relationship. If the mutual exclusion influence intensity is less than the mutual exclusion threshold, the candidate equipment pairs are determined not to have a mutual exclusion relationship. The original set of equipment mutual exclusion relationships from the previous scheduling period is obtained. For candidate equipment pairs that are determined to have a mutual exclusion relationship, an addition operation is performed. For candidate equipment pairs that are determined not to have a mutual exclusion relationship, if the equipment pair exists in the original set of equipment mutual exclusion relationships, a deletion operation is performed to obtain the dynamic set of equipment mutual exclusion relationships under the current scheduling period.

[0013] Preferably, the step of updating the initial device operation requirement set according to the dynamic device mutual exclusion relationship to obtain the actual device operation requirement set is as follows: For device pairs with mutual exclusion relationships in the dynamic device mutual exclusion relationship set, extract their corresponding co-start requirement information from the initial device operation requirement set; if a device pair does not have a co-start requirement in the initial device operation requirement set, then determine that the co-start requirement of the device pair conflicts with the mutual exclusion relationship; for device pairs that have been determined to have a conflict between the co-start requirement and the mutual exclusion relationship, cancel the collaborative start setting of one of the devices according to the device control priority, adjust it to independent start, and update its start sequence requirements in the device operation requirements to obtain the modified... The system retrieves the concurrent startup requirements information; for each pair of mutually exclusive devices in the dynamic device mutual exclusion relationship set, it extracts the environmental conditions required for device startup; combining the environmental parameter sequence, it determines whether there is an environmental condition conflict between the environmental conditions required for device startup and the environmental parameter sequence within the current scheduling cycle that cannot be simultaneously satisfied; for device pairs with conflicting environmental conditions, it modifies the environmental pre-tuning requirements of one of the devices according to the experimental configuration rules to obtain the modified environmental pre-tuning requirements information; and updates the modified concurrent startup requirements and the modified environmental pre-tuning requirements information to the initial device operation requirements set to obtain the actual device operation requirements set.

[0014] Preferably, the intelligent scheduling system for laboratory equipment driven by environmental parameters includes: an environmental data acquisition module, used to collect environmental monitoring data of the laboratory at a unified sampling period within the current scheduling cycle. The environmental monitoring data includes temperature, humidity, air supply volume, exhaust volume, electromagnetic environment, and power supply and distribution status, forming an environmental parameter sequence based on the environmental monitoring data; an initial equipment operation requirement acquisition module, used to extract the environmental conditions required for the start-up of each piece of equipment according to the operating requirements of the experimental equipment. The environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution conditions, and simultaneously determine whether there are simultaneous start-up requirements and environmental pre-adjustment requirements among the equipment, forming an initial equipment operation requirement set; a mutual exclusion restriction equipment acquisition module, used to acquire various disturbance parameters for each piece of equipment. The disturbance parameters include the micro-vibration acceleration signal of the mounting base, the ground vibration ratio, and the short-time voltage fluctuation indication. A mutual exclusion influence index is obtained based on the disturbance parameters, and equipment with mutual exclusion restrictions is screened based on the mutual exclusion influence index; and an actual equipment operation requirement acquisition module, used to acquire the actual equipment operation requirements. The system employs a multi-stage process: First, it checks for mutual exclusion between devices, updates the mutual exclusion criteria, and obtains dynamic mutual exclusion relationships. Based on these relationships, it updates the initial set of device operating requirements to obtain the actual set of device operating requirements. Second, it acquires devices that meet initial startup conditions by comparing the environmental parameter sequence with the actual set of device operating requirements. Based on environmental conditions, startup requirements, and dynamic mutual exclusion relationships, it determines whether the current environment meets the startup conditions for each device, filters all devices that meet the startup conditions, and records this as the initial set of devices that meet the startup conditions. Devices that only fail to meet environmental conditions are then selected as candidate devices. Third, it acquires devices that ultimately meet startup conditions by determining the environmental parameters to be adjusted for candidate devices based on environmental pre-adjustment requirements. These parameters include temperature, humidity, ventilation volume, or power supply parameters, and issues environmental adjustment commands. Once the environmental parameters meet the requirements for device startup, the device is added to the initial set of devices that meet the startup conditions, resulting in the final set of devices that meet the startup conditions. Finally, it issues startup commands to devices that ultimately meet the startup conditions.

[0015] The technical effects and advantages of this invention are as follows: For each device, various disturbance parameters are acquired, and a mutual exclusion influence index is obtained based on the disturbance parameters. Devices with mutual exclusion restrictions are screened based on the mutual exclusion influence index. For devices with mutual exclusion restrictions, a mutual exclusion determination update operation is performed between devices to obtain dynamic device mutual exclusion relationships. The initial device operation requirement set is updated based on the dynamic device mutual exclusion relationships to obtain the actual device operation requirement set. All devices that meet the startup conditions are screened and recorded as the initial set of devices that meet the startup conditions. Devices that only do not meet the environmental conditions are screened as candidate devices, which effectively improves experimental efficiency and the accuracy of experimental results. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the intelligent scheduling method for laboratory equipment based on environmental parameters provided in this application embodiment.

[0017] Figure 2 This is a structural diagram of an intelligent scheduling system for laboratory equipment based on environmental parameters, provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The intelligent scheduling method and system for laboratory equipment based on environmental parameters involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides a method for intelligent scheduling of laboratory equipment based on environmental parameters, such as... Figure 1 As shown, it includes the following steps: Step 1: During the current scheduling cycle, collect environmental monitoring data of the laboratory at a unified sampling cycle. The environmental monitoring data includes parameters such as temperature, humidity, air supply volume, exhaust volume, electromagnetic environment, and power supply and distribution status. Based on the environmental monitoring data, form an environmental parameter sequence to characterize the real-time status of the laboratory environment. Step 2: Based on the operating requirements of the experimental equipment, extract the environmental conditions required for the start-up of each piece of equipment. The environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution. At the same time, determine whether there are simultaneous start-up requirements and environmental pre-adjustment requirements among the equipment to form an initial set of equipment operating requirements. Simultaneous start-up requirement refers to the operational requirement that multiple experimental devices need to be started at the same time when performing the same experimental task or relying on the same experimental procedure. When a certain type of experiment relies on the collaborative work of multiple devices, such as preprocessing units, detection units, and recording units, these devices must enter the working state at the same point in time, a simultaneous start-up requirement exists among these devices.

[0020] Environmental preconditioning requirements refer to the requirement that the laboratory environment must meet specific operating conditions before certain equipment can be started, such as specific temperature, humidity, ventilation, or power supply stability.

[0021] In this embodiment, it should be specifically explained that the steps for obtaining the initial set of equipment operation requirements are as follows: The operation requirement data of each experimental device is read from the device operation requirement library. The operation requirement data includes start-up conditions, records of coordinated start-up, and records of pre-startup environmental preparation. The operation requirement data serves as a unified source for subsequent extraction of environmental conditions and identification of co-start requirements and environmental pre-adjustment requirements. The equipment operation requirements library is used to centrally store the operation requirements information of various equipment in the laboratory. The operation requirements library is usually configured and maintained by the laboratory when the equipment is registered or the scheduling system is put online. It provides a unified data source for the scheduling system to extract equipment operation requirements, compare execution conditions and make judgments in different scheduling cycles, thereby ensuring the consistency and traceability of equipment scheduling logic.

[0022] Start-up conditions describe the environmental requirements that equipment must meet before it can enter the operating state. They typically include specific limits or allowable ranges for temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution conditions, in order to ensure that the equipment starts up in a safe and stable environment.

[0023] The collaborative startup record describes whether the device needs to be started synchronously with other devices when performing a specific experimental task, and records the corresponding collaborative device number.

[0024] The pre-startup environmental preparation record describes whether the temperature, humidity, ventilation, or power supply parameters need to be adjusted in advance before the equipment is started.

[0025] For each device, based on the start-up conditions recorded in its operating requirements data, the environmental conditions required for the device to start up are extracted. The environmental conditions include temperature conditions, humidity conditions, ventilation conditions, electromagnetic environment conditions, and power supply and distribution conditions. The environmental conditions are matched one-to-one with the devices and saved for subsequent comparison. Based on the records of collaborative startup in the operation requirements data, determine whether there is a need for simultaneous startup between devices. For devices that have a need for simultaneous startup, mark the simultaneous startup requirement in their corresponding operation requirements data and record the set of device numbers that start synchronously with them. Based on the records of pre-start environment preparation in the operation requirements data, determine whether the equipment has an environmental pre-adjustment requirement. For equipment with an environmental pre-adjustment requirement, mark the environmental pre-adjustment requirement in its operation requirements data for subsequent triggering of environmental pre-adjustment operations. For each piece of equipment, the environmental conditions, start-up requirements, and environmental pre-adjustment requirements of the equipment are summarized by equipment number to form the operating requirements of the equipment. The operating requirements are saved in a one-to-one correspondence with the equipment number for easy retrieval and comparison later. The operational requirements stored for each device are summarized by device number to form an initial set of device operational requirements.

[0026] Step 3: For each device, obtain the disturbance parameters of each device. The disturbance parameters are used to determine whether there is mutual exclusion effect on the device. The disturbance parameters include the micro-vibration acceleration signal of the mounting base, the ground vibration ratio, and the short-time voltage fluctuation indication. The mutual exclusion effect index is obtained by evaluating the disturbance parameters. The devices with mutual exclusion restrictions are screened according to the mutual exclusion effect index. In this implementation, the determination of mutual exclusion effects adopts an "independent evaluation per device" approach, rather than the traditional "paired evaluation of devices." This is because laboratories typically have a large number of devices; if paired evaluation were used, the degree of mutual influence would need to be calculated for each pair of devices separately. The evaluation complexity increases exponentially with the number of devices, making it difficult to meet real-time scheduling requirements. By independently quantifying the operational disturbance capability of individual devices to obtain a mutual exclusion influence index, devices with potential mutual exclusion characteristics can be quickly identified. Then, in subsequent steps, mutual exclusion determination updates are performed on these devices. This significantly reduces the computational scale while maintaining evaluation accuracy, improving the real-time performance and scalability of the scheduling system.

[0027] In this embodiment, it should be specifically explained that the steps for obtaining the mutual exclusion influence index are as follows: By setting an accelerometer on the mounting base, the micro-vibration acceleration signal within the current scheduling cycle is obtained through the accelerometer, and the micro-vibration mutual exclusion influence coefficient is calculated based on the micro-vibration acceleration signal. Obtain the ground vibration transmission ratio at the contact point between the equipment and the ground, and calculate the vibration coupling influence coefficient based on the ground vibration transmission ratio; The voltage signal at the power supply end of the device is collected by the voltage monitoring sensor at the device end, and the power fluctuation impact coefficient is calculated based on the voltage signal. The mutual exclusion influence coefficient of micro-vibration, the vibration coupling influence coefficient, and the power fluctuation influence coefficient are normalized. Specifically, in this embodiment, vector normalization can be used to normalize these coefficients. Specifically, the three influence coefficients are combined into a three-dimensional vector. The norm value is obtained by calculating the square root of the sum of the squares of each component of this vector. Each influence coefficient is then divided by this norm value to complete the normalization operation. The purpose of this normalization method is to ensure that the three influence coefficients have a uniform order of magnitude and scale standard when calculating the mutual exclusion influence index, avoiding calculation bias or local parameter amplification effects caused by large differences in the original value range. Since the vector normalization method is existing technology, its mathematical processing method has been widely disclosed and applied in fields such as multi-index evaluation and signal processing. Therefore, this embodiment will not further elaborate on its specific algorithm steps. The mutual exclusion influence index is calculated based on the normalized mutual exclusion influence coefficient of micro-vibration, the vibration coupling influence coefficient, and the power fluctuation influence coefficient. The specific steps for obtaining the mutual exclusion influence index are as follows: ; In the formula, Represented as a mutually exclusive influence index, This is expressed as the normalized micro-vibration mutual exclusion influence coefficient. The micro-vibration mutual exclusion influence coefficient quantifies the degree of disturbance caused by the micro-vibration characteristics of a particular piece of equipment due to the characteristics of its base. A larger coefficient indicates more significant high-frequency or spike vibrations generated by the equipment during operation, and a greater likelihood that these vibrations will be transmitted to surrounding equipment through the structural path, thus interfering with the operational stability of other equipment. Since the mutual exclusion influence index reflects the potential interference intensity between equipment during operation, a higher micro-vibration mutual exclusion influence coefficient corresponds to a higher mutual exclusion influence index. The vibration coupling influence coefficient, after normalization, measures the ability of a piece of equipment to transfer vibration energy through the mechanical connection between its base structure and the mounting surface. A larger coefficient indicates that the vibration generated during equipment operation is more easily coupled and transmitted to other equipment, potentially causing disturbances and interference between them. Since the mutual exclusion influence index comprehensively reflects the potential interference of equipment on the operational stability of surrounding equipment, a larger vibration coupling influence coefficient for a piece of equipment increases the likelihood of it interfering with other equipment, leading to a corresponding increase in the mutual exclusion influence index. This is expressed as the normalized power fluctuation impact coefficient. The power fluctuation impact coefficient characterizes the ability of equipment to cause or withstand short-term voltage fluctuations during operation. A higher value indicates a more significant impact of the equipment on the stability of the power grid, or a greater sensitivity to voltage fluctuations. When equipment generates significant power disturbances during operation, it may cause instability to other equipment on the shared power supply circuit, especially when power adjustment response is slow or multiple devices start up at the same time. Therefore, a higher power fluctuation impact coefficient indicates a higher risk of operational interference between the equipment and other devices, and the corresponding mutual exclusion impact index also increases accordingly. , , This represents the weighting coefficients of the normalized micro-vibration mutual exclusion influence coefficient, the normalized vibration transmission coupling influence coefficient, and the normalized power supply fluctuation influence coefficient. , , , Obtained through the analytic hierarchy process, for example , , The values ​​can be 0.3, 0.4, or 0.3. The Analytic Hierarchy Process (AHP) is a structured judgment method used in multi-indicator decision-making scenarios. It decomposes the problem to be evaluated into multiple levels, such as the target layer, criterion layer, and indicator layer, constructing a comparison matrix to reflect the importance relationship of different indicators relative to the target. The elements of this matrix are typically based on the relative deviation of each indicator from its reference value or measurement characteristics. The weight coefficients of each indicator are obtained through eigenvector solving, and consistency checks are used to ensure the reliability of the judgment results. The AHP is an existing technology and has been widely used in multi-factor comprehensive evaluation, engineering decision-making, and strategy optimization. Therefore, this embodiment will not further explain its mathematical solution process when using this method to determine the weight coefficients.

[0028] In this embodiment, it should be specifically explained that the steps for obtaining the micro-vibration mutual repulsion influence coefficient are as follows: During the current scheduling cycle, the micro-vibration acceleration signal of the mounting base of each device is collected at a uniform sampling period, with a frequency range of 1Hz-80Hz. It should be noted that the micro-vibration acceleration signal can be obtained by an acceleration sensor installed on the mounting base. The micro-vibration acceleration signal is divided into time windows, the variance of the signal in each time window is calculated, and the mean of all variances is calculated to obtain the vibration stability of the equipment. Reference measuring points are set up on the common foundation or supporting structure of the laboratory, and the environmental acceleration signals of the reference measuring points are collected and recorded as reference signals. These signals are recorded simultaneously with the vibration response of each piece of equipment to form an environmental reference sequence. Within each time window, the square of the difference between the device's micro-vibration acceleration signal and the reference signal at all times is calculated, and the average value is taken and the square root is taken to obtain the root mean square difference of the vibration response for that time window. The mean value of the root mean square difference of the vibration response for all time windows is calculated to obtain the vibration response difference degree. Divide the response difference by the sum of the vibration stability and 1 to obtain the micro-vibration mutual exclusion influence coefficient. Add 1 to prevent the denominator from being zero.

[0029] Micro-vibrations are one of the most common forms of interference that propagate between equipment in laboratories, especially in environments with strong structural rigidity and dense equipment layouts. Low-frequency vibrations generated by equipment operation can often affect the stability of nearby equipment through structural paths such as floors and support frames. The peak amplitude of micro-vibrations can directly reflect the vibration intensity of equipment in critical sensitive frequency bands and is an important basis for determining whether the equipment may cause mechanical disturbances to surrounding equipment.

[0030] In this embodiment, it should be specifically explained that the steps for obtaining the vibration transmission coupling influence coefficient are as follows: Within the current scheduling cycle, obtain the ground vibration ratio at the contact point between the equipment and the ground, and construct a vibration ratio sequence. It should be noted that the vibration transmission ratio data at the equipment's contact point with the ground can be obtained by deploying high-precision accelerometers and ground reference sensors under the equipment's mounting base or at its four corners. During equipment operation, the vibration acceleration sequences at the equipment's feet and adjacent ground reference points are recorded separately, and the ratio between these two sequences is calculated at corresponding times to obtain the foot vibration transmission ratio data. This ratio reflects the amplification or attenuation characteristics of equipment vibration during structural transmission and can be used to assess the vibration coupling impact of equipment disturbances on surrounding equipment. This method is existing technology and has been widely applied in multi-source vibration monitoring and equipment structural response analysis scenarios.

[0031] Obtain the maximum and minimum transmission ratios in the transmission ratio sequence, and calculate the difference between the maximum and minimum transmission ratios to obtain the range value, which is used to reflect the transmission fluctuation amplitude of the equipment in this cycle. The vibration transmission stability factor is obtained by calculating the ratio of the minimum transmission ratio to the range value. The vibration transmission stability factor is then taken as its reciprocal to obtain the vibration transmission coupling influence coefficient, which indicates that the greater the fluctuation of the equipment's transmission ratio, the stronger its coupling influence and the higher the value.

[0032] Whether equipment vibration will have a real impact on other equipment depends not only on the vibration intensity of the equipment itself, but also on the propagation efficiency of the vibration within the structure. By comparing the vibration response of the equipment itself with that of adjacent structural locations, the degree of coupling between the equipment vibration and the environment can be reflected. The vibration transmission coupling influence coefficient can characterize the diffusion capability of equipment vibration within a spatial structure.

[0033] In this embodiment, it should be specifically explained that the steps for obtaining the power fluctuation impact coefficient are as follows: Within the current scheduling cycle, the voltage signal at the power supply end of the equipment is collected through the voltage monitoring sensor at the equipment end at a uniform sampling period to form a short-time voltage sampling sequence. The sampling frequency is consistent with that of the environmental monitoring module to ensure that the data timing is aligned. The voltage change amplitude is calculated between adjacent sampling points for the voltage sampling sequence. The absolute value of all voltage change amplitudes is taken and the mean is calculated to obtain the short-time voltage fluctuation amplitude, which reflects the degree of voltage fluctuation of the equipment within the scheduling cycle. The average supply voltage is obtained by averaging the data in the voltage sampling sequence. The average supply voltage is then divided by the sum of the short-time voltage fluctuation amplitude and 1 to obtain the supply stability. Adding 1 is to prevent the denominator from being zero. Taking the reciprocal of the power supply stability, we obtain the power fluctuation influence coefficient. When the power supply stability of the equipment decreases (i.e., the voltage fluctuation is large), the power fluctuation influence coefficient increases, indicating that the power supply disturbance of the equipment is more likely to cause mutual exclusion effects.

[0034] Many pieces of equipment in the laboratory share the same power supply and distribution system. Their startup or operation may cause voltage fluctuations and instantaneous power quality degradation, which in turn affect sensitive instruments on the same or adjacent branches. Power fluctuation indicators can reflect the actual impact of equipment operation on the stability of the power supply system.

[0035] In this embodiment, it should be specifically explained that the step of filtering out devices with mutual exclusion restrictions based on the mutual exclusion influence index is as follows: The mutual exclusion impact index is compared with the impact threshold. If the mutual exclusion impact index is greater than or equal to the impact threshold, the device is determined to be affected by device mutual exclusion and is recorded as having mutual exclusion restrictions. If the mutual exclusion impact index is less than the impact threshold, the device is determined not to be affected by device mutual exclusion. The impact threshold is obtained through an adaptive threshold method, a dynamic setting method that automatically determines the judgment threshold based on data distribution characteristics. By analyzing the statistical characteristics of the mutual exclusion impact index of each device in the current scheduling cycle, such as mean, variance, distribution concentration, or change amplitude, a threshold range matching the actual operating state is automatically calculated, allowing the threshold to be adjusted in real time according to changes in the device's operating environment. Compared with a fixed threshold, the adaptive threshold method can avoid misjudgment problems caused by changes in environmental conditions, equipment operating conditions, or noise levels, improving the stability and reliability of mutual exclusion risk identification. Since the adaptive threshold method is an existing technology, its principle and implementation have been widely used in the fields of dynamic detection, signal analysis, and multi-source evaluation, this embodiment will not further elaborate on its specific mathematical calculation process.

[0036] Step 4: For devices with mutual exclusion restrictions, perform mutual exclusion determination and update operations between devices to obtain dynamic device mutual exclusion relationships. Update the initial device operation requirement set according to the dynamic device mutual exclusion relationships to obtain the actual device operation requirement set. In this embodiment, it should be specifically explained that the steps for obtaining the dynamic device mutual exclusion relationship are as follows: Within the current scheduling cycle, for all devices with mutual exclusion constraints, candidate device pairs are generated by combining them in pairs, and the spatial coordinates of each device are obtained from the device management system. The installation area number and the room number are used to calculate the spatial distance between candidate device pairs using Euclidean distance, obtain the spatial distance of all candidate device pairs, and construct the device spatial location matrix. A scheduling cycle refers to the time window within which the system executes a complete scheduling decision process. Within each scheduling cycle, operations such as environmental parameter acquisition, equipment operation requirement extraction, mutual exclusion relationship updating, and equipment startup determination must be completed. This scheduling cycle can be set according to the response time requirements of the laboratory operation scenario, typically in seconds or minutes, to ensure the real-time nature of the scheduling response and adaptability to environmental changes.

[0037] Euclidean distance is the straight-line distance between two points in three-dimensional space, widely used to measure the proximity of spatial locations. In this embodiment, Euclidean distance is used to calculate the spatial distance between two devices, specifically obtained by taking the square root of the sum of the squares of their spatial coordinate differences.

[0038] Obtain the average disturbance parameters of each device during the scheduling cycle, construct a device disturbance parameter vector based on the average disturbance parameters, and multiply the disturbance parameters to obtain the device disturbance source strength; It should be noted that the average disturbance parameter is the average value of the disturbance parameter within the scheduling period.

[0039] The structural vibration transmission baseline values ​​of all candidate device pairs obtained from laboratory commissioning were used to construct the structural vibration transmission baseline matrix. , representing the baseline response amplitude of the disturbance propagated along the structure to device j when device i is running; It should be noted that the structural vibration transmission baseline value refers to the structural vibration transmission response characteristics between candidate equipment pairs measured during the laboratory commissioning phase, with each piece of equipment activated sequentially and all other equipment in a static state. This value is used to characterize the solid-borne vibration transmission effects caused by the propagation paths of the mounting platform, connecting structures, or foundation between the equipment. This structural vibration transmission baseline value serves as a static reference for assessing the range of mutual exclusion effects and is existing technology, widely used in precision equipment layout and vibration transmission analysis.

[0040] The mutual exclusion influence intensity of the candidate equipment pair is calculated based on the intensity of the equipment disturbance source, the structural vibration transmission baseline value of the candidate equipment pair, and the spatial distance between the candidate equipment pairs. The specific steps for obtaining this information are as follows: ; In the formula Let be the mutual exclusion strength of device i on device j. Let the disturbance source strength of device i be denoted as . This represents the baseline value of structural vibration transmission from device i to device j. Represented as the spatial distance between candidate device pairs i and j; It should be noted that mutually exclusive disturbances are directional, typically originating from one source device (vibration, short-term current fluctuations, etc.) and propagating to other devices. Therefore, to consider the impact of candidate devices on i and j, only the disturbance source strength of device i needs to be considered.

[0041] Obtain the mutual exclusion influence strength of all candidate device pairs, compare the mutual exclusion influence strength with the mutual exclusion threshold. If the mutual exclusion influence strength is greater than or equal to the mutual exclusion threshold, it is determined that the candidate device pair has a mutual exclusion relationship; if the mutual exclusion influence strength is less than the mutual exclusion threshold, it is determined that the candidate device pair does not have a mutual exclusion relationship. Obtain the original set of device mutual exclusion relationships from the previous scheduling period. For candidate device pairs that are determined to have mutual exclusion relationships, perform an add operation to add them to the original set of device mutual exclusion relationships. For candidate device pairs that are determined not to have mutual exclusion relationships, if the device pair exists in the original set of device mutual exclusion relationships, perform a delete operation to remove them from the original set of device mutual exclusion relationships. Finally, update the dynamic set of device mutual exclusion relationships for the current scheduling period.

[0042] In this embodiment, it should be specifically explained that the step of updating the initial set of device operation requirements based on the dynamic device mutual exclusion relationship to obtain the actual set of device operation requirements is as follows: For a pair of devices that have a mutual exclusion relationship in the dynamic device mutual exclusion relationship set, extract their corresponding co-starting requirement information from the initial device operation requirement set. If a pair of devices does not have a co-starting requirement in the initial device operation requirement set, then it is determined that the co-starting requirement of the pair of devices conflicts with the mutual exclusion relationship. For a pair of devices that has been determined to have conflicting requirements for co-starting and mutual exclusion, cancel the co-starting setting of one of the devices according to the device control priority, adjust it to independent start, and update its start-up sequence requirements in the device operation requirements to obtain the corrected co-starting requirement information. Device control priority refers to a system-preset parameter used to determine the startup order and operating weight of multiple devices when resource conflicts, environmental limitations, or mutual exclusions exist. This priority is set based on factors such as the criticality of the device in the experimental process, its sensitivity to the operating environment, and the dependency order in the device control strategy. It is usually represented by an integer value, a level label, or a sorting index, and is used to prioritize the preservation of the original operating configuration of high-priority devices in scheduling decisions, ensuring the overall stability of the system and the criticality of the experimental task.

[0043] For each pair of mutually exclusive devices in the dynamic device mutual exclusion set, extract the environmental conditions required for device startup; combine the environmental parameter sequence to determine whether there is a conflict between the environmental conditions required for device startup and the environmental parameter sequence within the current scheduling cycle that cannot be satisfied simultaneously. For equipment pairs with conflicting environmental conditions, the environmental pre-adjustment requirements of one of the equipment are modified according to the experimental configuration rules. The modification includes extending the environmental pre-adjustment time window or adjusting the environmental target parameter value so that it can avoid conflicting operation with the other equipment in the same scheduling cycle, and the modified environmental pre-adjustment requirements information is obtained. Experimental configuration rules refer to a set of pre-defined environmental resource coordination and scheduling priorities established under conditions where multiple devices share an operating environment, in order to ensure the feasibility of experimental tasks and the non-interference of equipment operation. These rules include, but are not limited to, environmental resource allocation priorities, the adaptability range of equipment operation to environmental conditions, the adjustable range of environmental parameters, conflict handling strategies (such as extending the pre-adjustment time window and adjusting parameter settings), and exclusive strategies for specific equipment to access key environmental factors. They are used to guide the reasonable correction and adjustment of the environmental pre-adjustment requirements of relevant equipment when environmental condition conflicts occur, ensuring the feasibility and stability of the overall experimental process.

[0044] The revised start-up requirements and revised environmental pre-adjustment requirements are updated to the initial equipment operation requirements set to obtain the actual equipment operation requirements set, which serves as the basis for subsequent equipment start-up control and environmental adjustment control.

[0045] Step 5: Compare the environmental parameter sequence with the actual equipment operation requirements set item by item. Based on the environmental conditions, simultaneous start-up requirements, and dynamic equipment mutual exclusion relationships, determine whether the current environment meets the start-up conditions of each device. Filter all devices that meet the start-up conditions and record them as the initial set of devices that meet the start-up conditions. Filter out devices that do not meet the environmental conditions only as candidate devices. In this embodiment, it should be specifically explained that the steps for obtaining the initial set of devices that meet the startup conditions are as follows: For each device in the actual equipment operation requirements set, extract the environmental conditions, simultaneous start-up requirements, and mutual exclusion restriction information required for its startup; The environmental conditions required for device startup are compared with the corresponding parameter values ​​in the environmental parameter sequence. If all parameter values ​​are within the environmental conditions required for device startup, then the environmental conditions are deemed met. If there is a need for simultaneous startup of devices, the environmental conditions of other devices associated with the simultaneous startup need are simultaneously retrieved. The simultaneous startup need is determined to be satisfied only when the environmental conditions of all associated devices are satisfied. If a device has a mutual exclusion restriction, the operating status of the device corresponding to the mutual exclusion restriction is judged. The mutual exclusion condition is determined to be satisfied only if the device corresponding to the mutual exclusion restriction is not in the started state. When the environmental conditions, start-up requirements, and mutual exclusion conditions of a device are met, the device is determined to meet the start-up conditions and is added to the initial set of devices that meet the start-up conditions.

[0046] Step 6: For the candidate equipment, determine the environmental parameters that need to be adjusted according to the environmental pre-adjustment requirements. The environmental parameters include temperature, humidity, ventilation volume or power supply parameters, and issue environmental adjustment instructions. After the environmental parameters meet the environmental conditions required for equipment startup, add the equipment to the initial set of equipment that meets the startup conditions to obtain the final set of equipment that meets the startup conditions. It is important to clarify that the environmental pre-adjustment for the candidate equipment is a multi-dimensional and multi-system independent adjustment operation. Temperature, humidity, ventilation, and power supply / distribution adjustments in the laboratory are implemented by independent environmental support subsystems, such as the air conditioning system, humidification and dehumidification system, ventilation system, and power distribution system. Each type of environmental parameter corresponds to its own independent control channel and execution unit. Therefore, when different equipment has different environmental pre-adjustment requirements, the system breaks down its operational needs into specific environmental parameters that need adjustment and maps them to the corresponding support subsystems to issue independent environmental adjustment commands. Because each support subsystem is independent in physical structure and control logic, the adjustment processes of various environmental parameters do not interfere with each other and can be executed in parallel. This ensures that multiple candidate devices can obtain corresponding environmental adjustments according to their own environmental needs during the pre-adjustment phase, without conflicts or overlaps caused by the pre-adjustment behavior of other devices.

[0047] Step 7: Issue a start command to the device that finally meets the start conditions; perform start operations synchronously for devices that have the same start requirement; for devices with mutual exclusion restrictions, only start the device that meets the mutual exclusion condition, and keep the other devices in a waiting state, thereby completing the scheduling decision process driven by environmental parameters.

[0048] In this embodiment, it should be specifically explained that, as Figure 2 As shown, a laboratory equipment intelligent scheduling system driven by environmental parameters is presented. The system includes: The environmental data acquisition module is used to collect environmental monitoring data of the laboratory at a uniform sampling period within the current scheduling cycle. The environmental monitoring data includes parameters such as temperature, humidity, air supply volume, exhaust volume, electromagnetic environment, and power supply and distribution status. An environmental parameter sequence is formed based on the environmental monitoring data. The initial equipment operation requirements acquisition module is used to extract the environmental conditions required for the start-up of each piece of equipment based on the operating requirements of the experimental equipment. The environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution conditions. At the same time, it determines whether there are simultaneous start-up requirements and environmental pre-adjustment requirements between the equipment, forming an initial equipment operation requirements set. The mutual exclusion restriction device acquisition module is used to acquire various disturbance parameters for each device. The disturbance parameters include the micro-vibration acceleration signal of the mounting base, the ground vibration ratio, and the short-time voltage fluctuation indication. The mutual exclusion influence index is obtained based on the disturbance parameters, and the devices with mutual exclusion restrictions are screened based on the mutual exclusion influence index. The actual equipment operation requirement acquisition module is used to perform mutual exclusion determination and update operations between devices with mutual exclusion restrictions, obtain dynamic device mutual exclusion relationships, update the initial equipment operation requirement set according to the dynamic device mutual exclusion relationships, and obtain the actual equipment operation requirement set. The initial startup device acquisition module compares the environmental parameter sequence with the actual device operation requirements set item by item. Based on the environmental conditions, simultaneous startup requirements, and dynamic device mutual exclusion relationships, it determines whether the current environment meets the startup conditions of each device, filters all devices that meet the startup conditions, and records them as the initial startup condition set; it also filters out devices that do not meet the environmental conditions only as candidate devices. The final set of equipment acquisition module is used to determine the environmental parameters that need to be adjusted for the candidate equipment based on the environmental pre-adjustment requirements. The environmental parameters include temperature, humidity, ventilation volume or power supply parameters, and issue environmental adjustment instructions. After the environmental parameters meet the environmental conditions required for equipment startup, the equipment is added to the initial set of equipment that meets the startup conditions, thus obtaining the final set of equipment that meets the startup conditions. The command issuance module is used to issue start commands to devices that finally meet the start conditions.

[0049] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent scheduling of laboratory equipment based on environmental parameters, characterized in that, Includes the following steps: Step 1: During the current scheduling cycle, collect environmental monitoring data of the laboratory at a uniform sampling cycle. The environmental monitoring data includes temperature, humidity, air supply volume, exhaust volume, electromagnetic environment, and power supply and distribution status. Form an environmental parameter sequence based on the environmental monitoring data. Step 2: Based on the operating requirements of the experimental equipment, extract the environmental conditions required for the start-up of each piece of equipment. The environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution. At the same time, determine whether there are simultaneous start-up requirements and environmental pre-adjustment requirements among the equipment to form an initial set of equipment operating requirements. Step 3: For each device, obtain the disturbance parameters, including the micro-vibration acceleration signal of the mounting base, the ground vibration ratio, and the short-time voltage fluctuation indication. Based on the disturbance parameters, obtain the mutual exclusion influence index, and screen out the devices with mutual exclusion restrictions based on the mutual exclusion influence index. Step 4: For devices with mutual exclusion restrictions, perform mutual exclusion determination and update operations between devices to obtain dynamic device mutual exclusion relationships. Update the initial device operation requirement set according to the dynamic device mutual exclusion relationships to obtain the actual device operation requirement set. Step 5: Compare the environmental parameter sequence with the actual equipment operation requirements set item by item. Based on the environmental conditions, simultaneous start-up requirements, and dynamic equipment mutual exclusion relationships, determine whether the current environment meets the start-up conditions of each device. Filter all devices that meet the start-up conditions and record them as the initial set of devices that meet the start-up conditions. Filter out devices that do not meet the environmental conditions only as candidate devices. Step 6: For the candidate equipment, determine the environmental parameters that need to be adjusted according to the environmental pre-adjustment requirements. The environmental parameters include temperature, humidity, ventilation volume or power supply parameters, and issue environmental adjustment instructions. After the environmental parameters meet the environmental conditions required for equipment startup, add the equipment to the initial set of equipment that meets the startup conditions to obtain the final set of equipment that meets the startup conditions. Step 7: Issue a start command to the device that finally meets the start conditions; The steps for obtaining the mutual exclusion influence index are as follows: By setting an accelerometer on the mounting base, the micro-vibration acceleration signal within the current scheduling cycle is obtained through the accelerometer, and the micro-vibration mutual exclusion influence coefficient is calculated based on the micro-vibration acceleration signal. Obtain the ground vibration transmission ratio at the contact point between the equipment and the ground, and calculate the vibration coupling influence coefficient based on the ground vibration transmission ratio; The voltage signal at the power supply end of the device is collected by the voltage monitoring sensor at the device end, and the power fluctuation impact coefficient is calculated based on the voltage signal. The mutual exclusion influence coefficient of micro-vibration, the vibration transmission coupling influence coefficient, and the power supply fluctuation influence coefficient are normalized. The mutual exclusion influence index is then calculated based on the normalized mutual exclusion influence coefficient of micro-vibration, the vibration transmission coupling influence coefficient, and the power supply fluctuation influence coefficient.

2. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The steps for obtaining the initial set of equipment operation requirements are as follows: Read the operation requirement data of each experimental device from the device operation requirement library. The operation requirement data includes startup conditions, records of coordinated startup, and records of environmental preparation before startup. For each device, based on the start-up conditions recorded in its operating requirements data, extract the environmental conditions required for the device to start up. The environmental conditions include temperature conditions, humidity conditions, ventilation conditions, electromagnetic environment conditions, and power supply and distribution conditions. Based on the records of collaborative startup in the operation requirements data, determine whether there is a need for simultaneous startup between devices. For devices that have a need for simultaneous startup, mark the simultaneous startup requirement in their corresponding operation requirements data and record the set of device numbers that start synchronously with them. Based on the records of pre-startup environmental preparation in the operation requirements data, determine whether the equipment has environmental pre-adjustment requirements. For equipment with environmental pre-adjustment requirements, mark the environmental pre-adjustment requirement in its operation requirements data. For each piece of equipment, the environmental conditions, start-up requirements, and environmental pre-adjustment requirements of the equipment are summarized by equipment number to form the operating requirements of the equipment. The operational requirements of each device are summarized by device number to form an initial set of device operational requirements.

3. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that, The steps for obtaining the mutual exclusion influence coefficient of micro-vibration are as follows: Within the current scheduling cycle, the micro-vibration acceleration signal of the mounting base of each device is collected at a uniform sampling period; The micro-vibration acceleration signal is divided into time windows, the variance of the signal in each time window is calculated, and the mean of all variances is calculated to obtain the vibration stability of the equipment. Reference measuring points are set up on the common foundation or supporting structure of the laboratory, and the environmental acceleration signals of the reference measuring points are collected and recorded as reference signals. These signals are recorded simultaneously with the vibration response of each piece of equipment to form an environmental reference sequence. Within each time window, the square of the difference between the device's micro-vibration acceleration signal and the reference signal at all times is calculated, and the average value is taken and the square root is taken to obtain the root mean square difference of the vibration response for that time window. The mean value of the root mean square difference of the vibration response for all time windows is calculated to obtain the vibration response difference degree. Dividing the response difference by the sum of the vibration stability and 1 yields the micro-vibration mutual exclusion influence coefficient.

4. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The steps for obtaining the vibration transmission coupling influence coefficient are as follows: Within the current scheduling cycle, obtain the ground vibration ratio at the contact point between the equipment and the ground, and construct a vibration ratio sequence. Obtain the maximum and minimum transmission ratios in the transmission ratio sequence, and calculate the difference between the maximum and minimum transmission ratios to obtain the range value; The vibration transmission stability factor is obtained by calculating the ratio of the minimum transmission ratio to the range value. The vibration transmission coupling influence coefficient is obtained by taking the reciprocal of the vibration transmission stability factor.

5. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The steps for obtaining the power fluctuation impact coefficient are as follows: Within the current scheduling cycle, voltage signals from the power supply end of the equipment are collected through the equipment-side voltage monitoring sensor at a uniform sampling period to form a short-time voltage sampling sequence; The voltage change amplitude is calculated between adjacent sampling points for the voltage sampling sequence. The absolute value of all voltage change amplitudes is taken and the mean is calculated to obtain the short-time voltage fluctuation amplitude. The average supply voltage is obtained by averaging the data in the voltage sampling sequence. The average supply voltage is then divided by the sum of the short-time voltage fluctuation amplitude and 1 to obtain the supply stability. The power supply fluctuation influence coefficient is obtained by taking the reciprocal of the power supply stability.

6. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The step of filtering out devices with mutual exclusion restrictions based on the mutual exclusion impact index is as follows: The mutual exclusion impact index is compared with the impact threshold. If the mutual exclusion impact index is greater than or equal to the impact threshold, the device is determined to be affected by device mutual exclusion and is recorded as a device with mutual exclusion restrictions. If the mutual exclusion impact index is less than the impact threshold, the device is determined not to be affected by device mutual exclusion.

7. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The steps for obtaining the dynamic device mutual exclusion relationship are as follows: Within the current scheduling cycle, for all devices with mutual exclusion restrictions, candidate device pairs are generated in a pairwise combination manner. The spatial coordinates, installation area number, and room number of each device are obtained. The spatial distance between the candidate device pairs is calculated using Euclidean distance. The spatial distance between all candidate device pairs is obtained, and the device spatial location matrix is ​​constructed. Obtain the average disturbance parameters of each device during the scheduling cycle, construct a device disturbance parameter vector based on the average disturbance parameters, and multiply the disturbance parameters to obtain the device disturbance source strength; The structural vibration transmission baseline values ​​of all candidate device pairs obtained from the laboratory commissioning phase are called up, and the structural vibration transmission baseline matrix is ​​constructed. The mutual exclusion influence intensity of the candidate equipment pair is calculated based on the equipment disturbance source intensity, the structural vibration transmission baseline value of the candidate equipment pair, and the spatial distance between the candidate equipment pairs. Obtain the mutual exclusion influence strength of all candidate device pairs, compare the mutual exclusion influence strength with the mutual exclusion threshold. If the mutual exclusion influence strength is greater than or equal to the mutual exclusion threshold, it is determined that the candidate device pair has a mutual exclusion relationship; if the mutual exclusion influence strength is less than the mutual exclusion threshold, it is determined that the candidate device pair does not have a mutual exclusion relationship. Obtain the original set of device mutual exclusion relationships from the previous scheduling period. For candidate device pairs that are determined to have mutual exclusion relationships, perform an add operation; for candidate device pairs that are determined not to have mutual exclusion relationships, if the device pair exists in the original set of device mutual exclusion relationships, perform a delete operation to obtain the dynamic set of device mutual exclusion relationships for the current scheduling period.

8. The intelligent scheduling method for laboratory equipment based on environmental parameters according to claim 1, characterized in that: The step of updating the initial set of equipment operating requirements based on the dynamic mutual exclusion relationship of equipment to obtain the actual set of equipment operating requirements is as follows: For a pair of devices that have a mutual exclusion relationship in the dynamic device mutual exclusion relationship set, extract their corresponding co-starting requirement information from the initial device operation requirement set. If a pair of devices does not have a co-starting requirement in the initial device operation requirement set, then it is determined that the co-starting requirement of the pair of devices conflicts with the mutual exclusion relationship. For a pair of devices that has been determined to have conflicting requirements for co-starting and mutual exclusion, cancel the co-starting setting of one of the devices according to the device control priority, adjust it to independent start, and update its start-up sequence requirements in the device operation requirements to obtain the corrected co-starting requirement information. For each pair of mutually exclusive devices in the dynamic device mutual exclusion set, extract the environmental conditions required for device startup; combine the environmental parameter sequence to determine whether there is a conflict between the environmental conditions required for device startup and the environmental parameter sequence within the current scheduling cycle that cannot be satisfied simultaneously. For equipment pairs with conflicting environmental conditions, the environmental pre-adjustment requirements of one of the equipment are modified according to the experimental configuration rules to obtain the modified environmental pre-adjustment requirements information. The revised simultaneous start-up requirements and revised environmental pre-adjustment requirements are updated to the initial equipment operation requirements set to obtain the actual equipment operation requirements set.

9. An intelligent scheduling system for laboratory equipment based on environmental parameters, used to implement the intelligent scheduling method for laboratory equipment based on environmental parameters as described in claims 1-8, characterized in that: The system includes: The environmental data acquisition module is used to collect environmental monitoring data of the laboratory at a uniform sampling period within the current scheduling cycle. The environmental monitoring data includes temperature, humidity, air supply volume, exhaust volume, electromagnetic environment and power supply and distribution status. An environmental parameter sequence is formed based on the environmental monitoring data. The initial equipment operation requirements acquisition module is used to extract the environmental conditions required for the start-up of each piece of equipment based on the operating requirements of the experimental equipment. The environmental conditions include temperature, humidity, ventilation, electromagnetic environment, and power supply and distribution conditions. At the same time, it determines whether there are simultaneous start-up requirements and environmental pre-adjustment requirements between the equipment, forming an initial equipment operation requirements set. The mutual exclusion restriction device acquisition module is used to acquire various disturbance parameters for each device. The disturbance parameters include the micro-vibration acceleration signal of the mounting base, the ground vibration ratio, and the short-time voltage fluctuation indication. The mutual exclusion influence index is obtained based on the disturbance parameters, and the devices with mutual exclusion restrictions are screened based on the mutual exclusion influence index. The actual equipment operation requirement acquisition module is used to perform mutual exclusion determination and update operations between devices with mutual exclusion restrictions, obtain dynamic device mutual exclusion relationships, update the initial equipment operation requirement set according to the dynamic device mutual exclusion relationships, and obtain the actual equipment operation requirement set. The initial startup device acquisition module compares the environmental parameter sequence with the actual device operation requirements set item by item. Based on the environmental conditions, simultaneous startup requirements, and dynamic device mutual exclusion relationships, it determines whether the current environment meets the startup conditions of each device, filters all devices that meet the startup conditions, and records them as the initial startup condition set; it also filters out devices that do not meet the environmental conditions only as candidate devices. The final set of equipment acquisition module is used to determine the environmental parameters that need to be adjusted for the candidate equipment based on the environmental pre-adjustment requirements. The environmental parameters include temperature, humidity, ventilation volume or power supply parameters, and issue environmental adjustment instructions. After the environmental parameters meet the environmental conditions required for equipment startup, the equipment is added to the initial set of equipment that meets the startup conditions, thus obtaining the final set of equipment that meets the startup conditions. The command issuance module is used to issue start commands to devices that finally meet the start conditions.

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