A laboratory environment quality intelligent analysis method and system

Abstract

The invention discloses a laboratory environment quality intelligent analysis method and system, which obtain the network address of each metrology laboratory and the network address of an environmental monitoring center, determine the maximum information transmission distance between the metrology laboratory and the environmental monitoring center, construct a logic tree according to the node depth, perform routing planning for the transmission of data collected by the metrology laboratory, find a suitable next-hop address, avoid multiple metrology laboratories sending data to the metrology laboratory at the same time and causing channel collision, and ensure the reliability of network transmission; input environmental parameter information and meteorological parameter information into a pre-trained metrology environment assessment neural network model to obtain the environmental quality level of the metrology laboratory, so that an intelligent control strategy can be formulated for the metrology laboratory with an unqualified environmental quality level, so as to adjust the environment of the metrology laboratory, so that the environmental quality of the metrology laboratory is within a qualified range, and the service life of the instrument is avoided from being affected.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory environmental quality monitoring, and in particular to a laboratory environmental quality intelligent analysis method and system. Background Art

[0002] The electric energy metering laboratory is a metering laboratory used to measure and record power generation, power supply, plant power consumption, line loss power and user power consumption. The precision instruments in the electric energy metering laboratory are very refined. For example, the temperature of the electric energy meter calibration laboratory should be maintained at (23±2)℃, and the relative humidity should be maintained at (60±15)%. Some metering equipment is easily affected by environmental factors, resulting in abnormal experimental results, and in severe cases, the metering equipment itself will be damaged. Therefore, it is necessary to provide technical means to analyze and regulate the environmental quality of the electric energy metering laboratory to maintain the qualification of the environmental quality of the electric energy metering laboratory.

[0003] In the patent with patent application number CN202210279308.9, the invention name is a method and system for environmental control of precision instrument laboratory, which obtains environmental parameter information of precision instrument laboratory; evaluates the current environmental conditions in precision instrument laboratory according to the environmental parameter information; obtains meteorological parameter information, and determines weather characteristics according to the meteorological parameter information; if the current environmental conditions are lower than the preset evaluation threshold, generates environmental control mode according to the current environmental conditions and the weather characteristics; controls the environment of precision laboratory by the environmental control mode, and corrects the environmental control mode according to the correction information generated by the environmental parameter information after control, so as to realize that the environmental control mode is determined by the environmental changes in the precision instrument laboratory so that the laboratory is always in an environment suitable for the storage of precision instruments. However, the technical scheme does not consider the reliability of information transmission of multiple laboratories. When the number of laboratories is too large and the data transmitted is too much, it is easy to cause network congestion, resulting in the inability to predict the future environmental conditions of the laboratory. There is a lack of strategies for intelligent environmental control, which makes the instruments in the laboratory operate in an unqualified environment, affecting the service life of the instruments. Summary of the invention

[0004] The present invention provides a method and system for intelligent analysis of laboratory environmental quality, which solves the technical problem that the existing laboratory environmental control method does not take into account the reliability of information transmission between multiple laboratories, and when there are too many laboratories and too much data to be transmitted, network congestion is easily caused, resulting in the inability to predict the future environmental conditions of the laboratories. There is a lack of strategies for intelligent environmental control, which causes instruments in the laboratory to operate in an unqualified environment, thus affecting the service life of the instruments.

[0005] In view of this, the first aspect of the present invention provides a laboratory environment quality intelligent analysis method, comprising:

[0006] Obtain the network address of the environmental monitoring center and the network address of each metrology laboratory;

[0007] According to the network address of the environmental monitoring center and the network address of each metrology laboratory, calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center;

[0008] Taking the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth, a logical tree consisting of the environmental monitoring center and all metrology laboratories is constructed;

[0009] For any metrology laboratory, after obtaining the environmental parameter information and meteorological parameter information, the next hop address is determined according to the branch connection relationship of the logic tree, and the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of root and direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center.

[0010] Inputting environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model;

[0011] When the current environmental quality assessment level does not reach the qualified line, the environmental quality of the metrology laboratory shall be adjusted so that the environmental quality of the metrology laboratory is within the qualified range.

[0012] Optionally, when the current environmental quality assessment level does not reach the qualified line, the environmental quality of the metrology laboratory is adjusted so that the environmental quality of the metrology laboratory is within the qualified range, including:

[0013] Determine whether the current environmental quality assessment level output by the metrological environmental assessment neural network model has reached the qualified line. If not, predict the future environmental quality level based on the current environmental quality assessment level and calculate the real-time environmental quality standard difference;

[0014] Calculate future environmental quality adjustment parameters based on meteorological parameter information, future environmental quality levels and real-time environmental quality standard differences;

[0015] According to the correspondence between the preset environmental intelligent control strategy and the future environmental quality adjustment parameter, an environmental intelligent control strategy corresponding to the future environmental quality adjustment parameter is generated.

[0016] Optionally, the calculation formula for calculating the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is:

[0017]

[0018] Among them, MTD is the maximum transmission distance between the measurement laboratory node and the environmental monitoring center node, d DA is the depth of the measurement laboratory node, d LA is the depth of the environmental monitoring center node, d F is the depth of the common root node of the environmental monitoring center node and the metrology laboratory node when the environmental monitoring center node is not a leaf node of the metrology laboratory node, and C is the number of address spaces that can be allocated to the current metrology laboratory node.

[0019] Optionally, the metrological environment assessment neural network model includes an input layer, a fuzzy layer, a rule layer and an output layer;

[0020] The structure of the input layer inputting neurons into the fuzzy layer is:

[0021]

[0022] Among them, I j is the input of the jth neuron in the fuzzy layer, ω ij is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, θ j is the bias of the blur layer, o i is the i-th neuron;

[0023] The calculation formula of the fuzzy layer is:

[0024]

[0025] Among them, M j is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, and σ is the width of the environmental assessment neural network;

[0026] The formula for the fuzzy layer to send the calculation results to the rule layer is:

[0027]

[0028] Among them, A r is the input of the rth neuron in the regular layer, ω jr is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, θ r is the bias of the regular layer;

[0029] The calculation formula of the rule layer is:

[0030]

[0031]

[0032]

[0033] Among them, O r is the result of nonlinear transformation, O r is the result of nonlinear transformation, F r is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, f(·) is the activation function, O is the lower limit of the nonlinear transformation results of all neurons in the regular layer, is the upper limit of the nonlinear transformation results of all neurons in the regular layer;

[0034] The calculation formula of the output layer is:

[0035]

[0036] Among them, y is the output of the output layer.

[0037] Optionally, the calculation formula for predicting the future environmental quality level based on the current environmental quality assessment level is:

[0038]

[0039] Among them, X Δt is the future environmental quality level, X t is the current environmental quality assessment level, t is the current time, μ is a constant, Δt is the time period length, γ t is the autocorrelation coefficient, ε t is the calculation error.

[0040] Optionally, the formula for calculating the future environmental quality adjustment parameter is:

[0041]

[0042] Among them, e 2 Adjust the parameters for future environmental quality, w t is the impact weight of meteorological parameter information, [-k, k] is the meteorological variation range, WH t is the estimated value of future meteorological parameter information, WH 0 is the historical meteorological parameter information, σ WH is the standard deviation of meteorological parameter information, is the variance of meteorological parameter information, e 1 It is the real-time environmental quality standard difference.

[0043] A second aspect of the present invention provides a laboratory environment quality intelligent analysis system, comprising:

[0044] An address acquisition module is used to obtain the network address of the environmental monitoring center and the network address of each metrology laboratory;

[0045] A distance calculation module, used to calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center according to the network address of the environmental monitoring center and the network address of each metrology laboratory;

[0046] A logic tree construction module is used to construct a logic tree consisting of the environmental monitoring center and all metrology laboratories, taking the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth;

[0047] The routing planning module is used for any metrology laboratory. After obtaining the environmental parameter information and meteorological parameter information, the next hop address is determined according to the branch connection relationship of the logic tree, and the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of the root and the direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center.

[0048] An environmental quality assessment module is used to input environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model;

[0049] The environmental control module is used to adjust the environmental quality of the metrology laboratory when the current environmental quality assessment level does not reach the qualified line, so that the environmental quality of the metrology laboratory is within the qualified range.

[0050] Optionally, an environment control module is also included, which is specifically used to:

[0051] Determine whether the current environmental quality assessment level output by the metrological environmental assessment neural network model has reached the qualified line. If not, predict the future environmental quality level based on the current environmental quality assessment level and calculate the real-time environmental quality standard difference;

[0052] Calculate future environmental quality adjustment parameters based on meteorological parameter information, future environmental quality levels and real-time environmental quality standard differences;

[0053] According to the correspondence between the preset environmental intelligent control strategy and the future environmental quality adjustment parameter, an environmental intelligent control strategy corresponding to the future environmental quality adjustment parameter is generated.

[0054] Optionally, the calculation formula for calculating the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is:

[0055]

[0056] Among them, MTD is the maximum transmission distance between the measurement laboratory node and the environmental monitoring center node, d DA is the depth of the measurement laboratory node, d LA is the depth of the environmental monitoring center node, d F is the depth of the common root node of the environmental monitoring center node and the metrology laboratory node when the environmental monitoring center node is not a leaf node of the metrology laboratory node, and C is the number of address spaces that can be allocated to the current metrology laboratory node.

[0057] Optionally, the metrological environment assessment neural network model includes an input layer, a fuzzy layer, a rule layer and an output layer;

[0058] The structure of the input layer inputting neurons into the fuzzy layer is:

[0059]

[0060] Among them, I j is the input of the jth neuron in the fuzzy layer, ω ij is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, θ j is the bias of the blur layer, o i is the i-th neuron;

[0061] The calculation formula of the fuzzy layer is:

[0062]

[0063] Among them, M j is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, and σ is the width of the environmental assessment neural network;

[0064] The formula for the fuzzy layer to send the calculation results to the rule layer is:

[0065]

[0066] Among them, A r is the input of the rth neuron in the regular layer, ω jr is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, θ r is the bias of the regular layer;

[0067] The calculation formula of the rule layer is:

[0068]

[0069]

[0070]

[0071] Among them, O r is the result of nonlinear transformation, F r is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, f(·) is the activation function, O is the lower limit of the nonlinear transformation results of all neurons in the regular layer, is the upper limit of the nonlinear transformation results of all neurons in the regular layer;

[0072] The calculation formula of the output layer is:

[0073]

[0074] Among them, y is the output of the output layer.

[0075] It can be seen from the above technical solutions that the laboratory environment quality intelligent analysis method and system provided by the present invention have the following advantages:

[0076] The laboratory environment quality intelligent analysis method provided by the present invention obtains the network address of each metrology laboratory and the network address of the environmental monitoring center, determines the maximum information transmission distance between the metrology laboratory and the environmental monitoring center, constructs a logic tree according to the node depth, performs routing planning for the transmission of data collected by the metrology laboratory, finds a suitable next-hop address, avoids multiple metrology laboratories sending data to the metrology laboratory at the same time and causing channel collision, and ensures the reliability of network transmission; inputs environmental parameter information and meteorological parameter information into a pre-trained metrology environment assessment neural network model to obtain the environmental quality level of the metrology laboratory, so that an intelligent control strategy can be formulated for the metrology laboratory with an unqualified environmental quality level, so as to adjust the environment of the metrology laboratory, so that the environmental quality of the metrology laboratory is within a qualified range, and solves the technical problems that the existing laboratory environment control method does not consider the reliability of information transmission of multiple laboratories, when the number of laboratories is too large and the transmitted data is too much, it is easy to cause network congestion, resulting in the inability to predict the future environmental conditions of the laboratory, lacks a strategy for intelligent environmental control, and makes the instruments in the laboratory operate in an unqualified environment, affecting the service life of the instruments.

[0077] At the same time, the laboratory environmental quality intelligent analysis method provided by the present invention, after obtaining the current environmental quality assessment level output by the metrological environment assessment neural network model, calculates the future environmental quality adjustment parameters for the situation that the current environmental quality assessment level does not reach the qualified line, and generates the corresponding environmental intelligent control strategy, so as to adjust the metrological laboratory environment according to the environmental intelligent control strategy, so that the environmental quality of the metrological laboratory is within the qualified range, prolongs the service life of the precision instruments in the metrological laboratory, and avoids abnormal experimental results due to environmental factors.

[0078] The laboratory environment quality intelligent analysis system provided by the present invention is used to execute the laboratory environment quality intelligent analysis method provided by the present invention. Its principles and technical effects are the same as those of the laboratory environment quality intelligent analysis method provided by the present invention, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 A schematic diagram of a flow chart of a laboratory environment quality intelligent analysis method provided in the present invention;

[0081] Figure 2 A schematic diagram of the structure of the logic tree provided in the present invention;

[0082] Figure 3 The present invention provides a schematic diagram of the structure of a laboratory environment quality intelligent analysis system. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] For easier understanding, see Figure 1 and Figure 2 The present invention provides an embodiment of a laboratory environment quality intelligent analysis method, comprising:

[0085] Step 101: Obtain the network address of the environmental monitoring center and the network address of each metrology laboratory.

[0086] It should be noted that the network address is the logical address of a node on the Internet in the network, which can be used to address the node. The network address is dynamically assigned by its parent node when the node joins the network. The network address is only used for routing mechanism and data transmission and is the unique identifier of each node. In the embodiment of the present invention, it is necessary to obtain the network address of the environmental monitoring center, denoted as DA, and the network address of each metrology laboratory, denoted as DA.

[0087] Step 102: Calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center according to the network address of the environmental monitoring center and the network address of each metrology laboratory.

[0088] It should be noted that, based on the network address of the environmental monitoring center and the network address of each metrology laboratory, the maximum information transmission distance between each metrology laboratory and the environmental monitoring center can be calculated. Specifically, the calculation formula for calculating the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is:

[0089]

[0090] Among them, MTD is the maximum transmission distance between the metrology laboratory node and the environmental monitoring center node, C is the number of address spaces that can be allocated to the current metrology laboratory node, and d DA is the depth of the measurement laboratory node, d LA is the depth of the environmental monitoring center node, d F When the environmental monitoring center node is not a leaf node of the metrology laboratory node, the depth of the root node shared by the environmental monitoring center node and the metrology laboratory node. The depth refers to the number of ancestors of the node, excluding the node itself, that is, how many edges there are from the current node to the root node.

[0091] Step 103: With the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth, a logic tree consisting of the environmental monitoring center and all metrology laboratories is constructed.

[0092] It should be noted that, taking the Environmental Monitoring Center as the root node, if the network address of the current metrology laboratory and the network address of the Environmental Monitoring Center satisfy: DA<LA<DA+C, then the current metrology laboratory node is the leaf node of the Environmental Monitoring Center node. The connection relationship between all metrology laboratory nodes and the connection relationship between the metrology laboratory and the Environmental Monitoring Center nodes are traversed in sequence to form a logical tree, such as Figure 2 shown.

[0093] Step 104: For any metrology laboratory, after obtaining the environmental parameter information and meteorological parameter information, determine the next hop address according to the branch connection relationship of the logic tree, and send the environmental parameter information and meteorological parameter information to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of root and direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center.

[0094] It should be noted that the precision instruments in the metrology laboratory are easily affected by environmental factors, resulting in abnormal experimental results. In severe cases, the precision instruments themselves will be damaged. Therefore, it is necessary to automatically perceive, intelligently evaluate and regulate the environmental data of the metrology laboratory to ensure that the test environment of the metrology laboratory is in a qualified state. The environmental parameter information obtained includes temperature, humidity, air particle concentration and gas concentration. In order to ensure the reliability of network transmission and avoid channel collision caused by multiple metrology laboratories sending data to the environmental monitoring center at the same time, it is necessary to plan the transmission route. Specifically, the next hop address is determined according to the branch connection relationship of the logic tree. If the metrology laboratory and the environmental monitoring center are the relationship between the root and the direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center. Then, according to the planned route, the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center.

[0095] Step 105: Input the environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model.

[0096] It should be noted that, in order to construct a metrological environmental assessment neural network model, a preset number of data from the experimental environmental standards and historical environmental parameter information are selected as training samples, and the environmental quality level corresponding to the experimental environmental standards and historical environmental parameter information is used as the expected output of the metrological environmental assessment neural network model. According to the expected output, the parameters in the metrological environmental assessment neural network model are corrected, and deep learning is performed to finally obtain a metrological environmental assessment neural network model that meets the preset success rate.

[0097] In one embodiment, the metrology environment assessment neural network model includes an input layer, a fuzzy layer, a rule layer, and an output layer;

[0098] The structure of the input layer inputting neurons into the fuzzy layer is:

[0099]

[0100] Among them, I j is the input of the jth neuron in the fuzzy layer, ω ij is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, θ j is the bias of the blur layer, o i is the i-th neuron;

[0101] The fuzzy layer selects Gaussian function as the membership function to fuzzify the input variables. The calculation formula of the fuzzy layer is:

[0102]

[0103] Among them, M j is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, and σ is the width of the environmental assessment neural network;

[0104] The formula for the fuzzy layer to send the calculation results to the rule layer is:

[0105]

[0106] Among them, A r is the input of the rth neuron in the regular layer, ω jr is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, θ r is the bias of the regular layer;

[0107] The calculation process of the rule layer is:

[0108] First, A r Perform nonlinear transformation, the transformation formula is:

[0109]

[0110] Among them, O r is the result of nonlinear transformation.

[0111] Then the activation calculation is performed according to the output threshold from the fuzzy layer to the regular layer. The formula is:

[0112]

[0113]

[0114] in, F r is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, f(·) is the activation function, Ois the lower limit of the nonlinear transformation results of all neurons in the regular layer, It is the upper limit of the nonlinear transformation results of all neurons in the rule layer. After the rule layer activates the data, it is transmitted to the output layer.

[0115] The output layer calculates the environmental quality level quantification, and the calculation formula is:

[0116]

[0117] Among them, y is the output of the output layer. The output layer matches the calculation result with the environmental quality level to obtain the model output corresponding to the current input sample. The error of the calculation result is calculated according to the expected output and the model output, and the error is transmitted in the reverse direction to update the parameters in the metrological environmental assessment neural network model, and obtain a metrological environmental assessment neural network model that meets the preset success rate.

[0118] Step 106: When the current environmental quality assessment level does not reach the qualified level, the environmental quality of the metrology laboratory is adjusted so that the environmental quality of the metrology laboratory is within the qualified range.

[0119] It should be noted that if the environmental quality assessment level of the current metrology laboratory does not meet the preset qualified level, the Environmental Monitoring Center needs to formulate an intelligent control strategy for the current metrology laboratory based on the meteorological parameter information of the current metrology laboratory, so as to adjust the environmental quality of the metrology laboratory so that the environmental quality of the metrology laboratory is within the qualified range.

[0120] The laboratory environment quality intelligent analysis method provided in the embodiment of the present invention obtains the network address of each metrology laboratory and the network address of the environmental monitoring center, determines the maximum information transmission distance between the metrology laboratory and the environmental monitoring center, constructs a logic tree according to the node depth, performs routing planning for the transmission of data collected by the metrology laboratory, and finds a suitable next-hop address to avoid multiple metrology laboratories sending data to the metrology laboratory at the same time and causing channel collision, thereby ensuring the reliability of network transmission; inputs environmental parameter information and meteorological parameter information into a pre-trained metrology environment assessment neural network model to obtain the environmental quality level of the metrology laboratory, so that an intelligent control strategy can be formulated for the metrology laboratory with an unqualified environmental quality level, so as to adjust the metrology laboratory environment, so that the environmental quality of the metrology laboratory is within the qualified range, and solves the problem that the existing laboratory environment control method does not consider the reliability of information transmission of multiple laboratories, when the number of laboratories is too large and the transmitted data is too much, it is easy to cause network congestion, resulting in the inability to predict the future environmental conditions of the laboratory, lacks a strategy for intelligent environmental control, and causes the instruments in the laboratory to operate in an unqualified environment, affecting the service life of the instruments.

[0121] In one embodiment, an analytical feature vector for measuring laboratory environment quality can be constructed: CVE = [X t ,X Δt ], where X Δt is the future environmental quality level, X t is the current environmental quality assessment level, and the result output by the metrological environmental assessment neural network model is X t According to the current environmental quality assessment level X t Calculate future environmental quality levels:

[0122]

[0123] Among them, t is the current time, μ is a constant, Δt is the time period length, γ t is the autocorrelation coefficient, ε t is the calculation error.

[0124] Set the value corresponding to the minimum qualified grade of the metrology laboratory to X' t , then the real-time environmental quality standard difference e 1 for:

[0125] e 1 =|X t -X' t |.

[0126] Combined with the meteorological parameter information of the metrology laboratory, the future environmental quality adjustment parameters can be obtained according to the future environmental quality level prediction value and the real-time environmental quality standard difference. The calculation formula is:

[0127]

[0128] Among them, e 2 Adjust the parameters for future environmental quality, w t is the impact weight of meteorological parameter information, [-k, k] is the meteorological variation range, WH t is the estimated value of future meteorological parameter information, WH 0 is the historical meteorological parameter information, σ WH is the standard deviation of meteorological parameter information, is the variance of meteorological parameter information, e 1 It is the real-time environmental quality standard difference.

[0129] The corresponding relationship between the environmental intelligent control strategy and the future environmental quality adjustment parameter can be preset. 2 Afterwards, the Environmental Monitoring Center can generate an intelligent environmental control strategy for the metrology laboratory based on future environmental quality adjustment parameters and adjust the environment of the metrology laboratory to ensure that the environmental quality of the metrology laboratory is within the qualified range.

[0130] The laboratory environment quality intelligent analysis method provided by the embodiment of the present invention, after obtaining the current environment quality assessment level output by the metrology environment assessment neural network model, calculates the future environment quality adjustment parameters for the situation that the current environment quality assessment level does not reach the qualified line, and generates the corresponding environment intelligent control strategy, so as to adjust the metrology laboratory environment according to the environment intelligent control strategy, so that the environment quality of the metrology laboratory is within the qualified range, prolongs the service life of the precision instruments in the metrology laboratory, and avoids abnormal experimental results due to environmental factors.

[0131] For easier understanding, see Figure 3 The present invention provides an embodiment of a laboratory environment quality intelligent analysis system, including:

[0132] An address acquisition module is used to obtain the network address of the environmental monitoring center and the network address of each metrology laboratory;

[0133] A distance calculation module, used to calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center according to the network address of the environmental monitoring center and the network address of each metrology laboratory;

[0134] A logic tree construction module is used to construct a logic tree consisting of the environmental monitoring center and all metrology laboratories, taking the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth;

[0135] The routing planning module is used for any metrology laboratory. After obtaining the environmental parameter information and meteorological parameter information, the next hop address is determined according to the branch connection relationship of the logic tree, and the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of the root and the direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center.

[0136] An environmental quality assessment module is used to input environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model;

[0137] The environmental control module is used to adjust the environmental quality of the metrology laboratory when the current environmental quality assessment level does not reach the qualified line, so that the environmental quality of the metrology laboratory is within the qualified range.

[0138] Environmental control module, specifically used for:

[0139] Determine whether the current environmental quality assessment level output by the metrological environmental assessment neural network model has reached the qualified line. If not, predict the future environmental quality level based on the current environmental quality assessment level and calculate the real-time environmental quality standard difference;

[0140] Calculate future environmental quality adjustment parameters based on meteorological parameter information, future environmental quality levels and real-time environmental quality standard differences;

[0141] According to the correspondence between the preset environmental intelligent control strategy and the future environmental quality adjustment parameter, an environmental intelligent control strategy corresponding to the future environmental quality adjustment parameter is generated.

[0142] The calculation formula for the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is:

[0143]

[0144] Among them, MTD is the maximum transmission distance between the measurement laboratory node and the environmental monitoring center node, d DA is the depth of the measurement laboratory node, d LA is the depth of the environmental monitoring center node, d F is the depth of the common root node of the environmental monitoring center node and the metrology laboratory node when the environmental monitoring center node is not a leaf node of the metrology laboratory node, and C is the number of address spaces that can be allocated to the current metrology laboratory node.

[0145] The metrological environment assessment neural network model includes input layer, fuzzy layer, rule layer and output layer;

[0146] The structure of the input layer inputting neurons into the fuzzy layer is:

[0147]

[0148] Among them, I j is the input of the jth neuron in the fuzzy layer, ω ij is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, θ j is the bias of the blur layer, o i is the i-th neuron;

[0149] The calculation formula of the fuzzy layer is:

[0150]

[0151] Among them, M j is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, and σ is the width of the environmental assessment neural network;

[0152] The formula for the fuzzy layer to send the calculation results to the rule layer is:

[0153]

[0154] Among them, A r is the input of the rth neuron in the regular layer, ω jr is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, θ r is the bias of the regular layer;

[0155] The calculation formula of the rule layer is:

[0156]

[0157]

[0158]

[0159] Among them, O r is the result of nonlinear transformation, F r is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, f(·) is the activation function, O is the lower limit of the nonlinear transformation results of all neurons in the regular layer, is the upper limit of the nonlinear transformation results of all neurons in the regular layer;

[0160] The calculation formula of the output layer is:

[0161]

[0162] Among them, y is the output of the output layer.

[0163] The calculation formula for predicting the future environmental quality level based on the current environmental quality assessment level is:

[0164]

[0165] Among them, X Δt is the future environmental quality level, X t is the current environmental quality assessment level, t is the current time, μ is a constant, Δt is the time period length, γ t is the autocorrelation coefficient, ε t is the calculation error.

[0166] The formula for calculating future environmental quality adjustment parameters is:

[0167]

[0168] Among them, e2 Adjust the parameters for future environmental quality, w t is the impact weight of meteorological parameter information, [-k, k] is the meteorological variation range, WH t is the estimated value of future meteorological parameter information, WH 0 is the historical meteorological parameter information, σ WH is the standard deviation of meteorological parameter information, is the variance of meteorological parameter information, e 1 It is the real-time environmental quality standard difference.

[0169] The laboratory environment quality intelligent analysis system provided by the present invention is used to execute the laboratory environment quality intelligent analysis method provided by the present invention. Its principles and technical effects are the same as those of the laboratory environment quality intelligent analysis method provided by the present invention, and will not be repeated here.

[0170] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent analysis method for laboratory environmental quality, It is characterized in that include: Obtain the network address of the environmental monitoring center and the network address of each metrology laboratory; According to the network address of the environmental monitoring center and the network address of each metrology laboratory, calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center; Taking the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth, a logical tree consisting of the environmental monitoring center and all metrology laboratories is constructed; For any metrology laboratory, after obtaining the environmental parameter information and meteorological parameter information, the next hop address is determined according to the branch connection relationship of the logic tree, and the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of root and direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center. Inputting environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model; When the current environmental quality assessment level does not reach the qualified level, the environmental quality of the metrology laboratory shall be adjusted to ensure that the environmental quality of the metrology laboratory is within the qualified range; The calculation formula for the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is: ； in, is the maximum transmission distance between the metrology laboratory node and the environmental monitoring center node, DA is the network address of the metrology laboratory node, and LA is the network address of the environmental monitoring center node. To measure the depth of the laboratory node, is the depth of the environmental monitoring center node, is the depth of the common root node of the environmental monitoring center node and the metrology laboratory node when the environmental monitoring center node is not a leaf node of the metrology laboratory node, and C is the number of address spaces that can be allocated to the current metrology laboratory node.

2. The laboratory environment quality intelligent analysis method according to claim 1, It is characterized in that When the current environmental quality assessment level does not meet the qualified line, the environmental quality of the metrology laboratory shall be adjusted to make it within the qualified range, including: Determine whether the current environmental quality assessment level output by the metrological environmental assessment neural network model has reached the qualified line. If not, predict the future environmental quality level based on the current environmental quality assessment level and calculate the real-time environmental quality standard difference; Calculate future environmental quality adjustment parameters based on meteorological parameter information, future environmental quality levels and real-time environmental quality standard differences; According to the correspondence between the preset environmental intelligent control strategy and the future environmental quality adjustment parameter, an environmental intelligent control strategy corresponding to the future environmental quality adjustment parameter is generated.

3. The laboratory environment quality intelligent analysis method according to claim 1, It is characterized in that The metrological environment assessment neural network model includes input layer, fuzzy layer, rule layer and output layer; The structure of the input layer inputting neurons into the fuzzy layer is: ； in, is the input of the jth neuron in the fuzzy layer, is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, is the bias of the blur layer, is the i-th neuron; The calculation formula of the fuzzy layer is: ； in, is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, Evaluate the width of the neural network for the environment; The formula for the fuzzy layer to send the calculation results to the rule layer is: ； in, is the input of the rth neuron in the regular layer, is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, is the bias of the regular layer; The calculation formula of the rule layer is: ； ； ； in, is the result of nonlinear transformation, is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, is the activation function, is the lower limit of the nonlinear transformation results of all neurons in the regular layer, is the upper limit of the nonlinear transformation results of all neurons in the regular layer; The calculation formula of the output layer is: ； Among them, y is the output of the output layer.

4. The laboratory environment quality intelligent analysis method according to claim 2, It is characterized in that The calculation formula for predicting the future environmental quality level based on the current environmental quality assessment level is: ； in, For the future environmental quality level, is the current environmental quality assessment level, t is the current moment, is a constant, is the time period length, is the autocorrelation coefficient, is the calculation error.

5. The laboratory environment quality intelligent analysis method according to claim 4, It is characterized in that The formula for calculating future environmental quality adjustment parameters is: ； in, Adjust parameters for future environmental quality, is the impact weight of meteorological parameter information, The range of meteorological changes, For the estimated value of future meteorological parameter information, For historical meteorological parameter information, is the standard deviation of meteorological parameter information, is the variance of meteorological parameter information, It is the real-time environmental quality standard difference.

6. An intelligent analysis system for laboratory environmental quality, It is characterized in that include: An address acquisition module is used to obtain the network address of the environmental monitoring center and the network address of each metrology laboratory; A distance calculation module, used to calculate the maximum information transmission distance between each metrology laboratory and the environmental monitoring center according to the network address of the environmental monitoring center and the network address of each metrology laboratory; A logic tree construction module is used to construct a logic tree consisting of the environmental monitoring center and all metrology laboratories, taking the environmental monitoring center as the root node and the maximum information transmission distance between each metrology laboratory and the environmental monitoring center as the node depth; The routing planning module is used for any metrology laboratory. After obtaining the environmental parameter information and meteorological parameter information, the next hop address is determined according to the branch connection relationship of the logic tree, and the environmental parameter information and meteorological parameter information are sent to the environmental monitoring center. If the metrology laboratory and the environmental monitoring center are in the relationship of the root and the direct leaf node, the next hop address of the metrology laboratory is the environmental monitoring center. Otherwise, the next hop address of the metrology laboratory node is the current root node, and then the current root node searches for the next hop address according to the branch connection relationship of the logic tree until the next hop address of the metrology laboratory is the environmental monitoring center. An environmental quality assessment module is used to input environmental parameter information and meteorological parameter information into a pre-trained metrological environmental assessment neural network model to obtain a current environmental quality assessment grade output by the metrological environmental assessment neural network model; The environmental control module is used to adjust the environmental quality of the metrology laboratory when the current environmental quality assessment level does not reach the qualified line, so that the environmental quality of the metrology laboratory is within the qualified range; The calculation formula for the maximum information transmission distance between each metrology laboratory and the environmental monitoring center is: ； in, is the maximum transmission distance between the metrology laboratory node and the environmental monitoring center node, DA is the network address of the metrology laboratory node, and LA is the network address of the environmental monitoring center node. To measure the depth of the laboratory node, is the depth of the environmental monitoring center node, is the depth of the common root node of the environmental monitoring center node and the metrology laboratory node when the environmental monitoring center node is not a leaf node of the metrology laboratory node, and C is the number of address spaces that can be allocated to the current metrology laboratory node.

7. The laboratory environment quality intelligent analysis system according to claim 6, It is characterized in that Environmental control module, specifically used for: Determine whether the current environmental quality assessment level output by the metrological environmental assessment neural network model has reached the qualified line. If not, predict the future environmental quality level based on the current environmental quality assessment level and calculate the real-time environmental quality standard difference; Calculate future environmental quality adjustment parameters based on meteorological parameter information, future environmental quality levels and real-time environmental quality standard differences; According to the correspondence between the preset environmental intelligent control strategy and the future environmental quality adjustment parameter, an environmental intelligent control strategy corresponding to the future environmental quality adjustment parameter is generated.

8. The laboratory environment quality intelligent analysis system according to claim 6, It is characterized in that The metrological environment assessment neural network model includes input layer, fuzzy layer, rule layer and output layer; The structure of the input layer inputting neurons into the fuzzy layer is: ； in, is the input of the jth neuron in the fuzzy layer, is the connection weight between the i-th neuron in the input layer and the j-th neuron in the fuzzy layer, is the bias of the blur layer, is the i-th neuron; The calculation formula of the fuzzy layer is: ； in, is the output of the jth neuron in the fuzzy layer, c is the center of the environmental assessment neural network, Evaluate the width of the neural network for the environment; The formula for the fuzzy layer to send the calculation results to the rule layer is: ； in, is the input of the rth neuron in the regular layer, is the connection weight between the jth neuron in the fuzzy layer and the rth neuron in the regular layer, is the bias of the regular layer; The calculation formula of the rule layer is: ； ； ； in, is the result of nonlinear transformation, is the lower limit of the output of the rth neuron in the regular layer, is the upper limit of the output of the rth neuron in the regular layer, is the activation function, is the lower limit of the nonlinear transformation results of all neurons in the regular layer, is the upper limit of the nonlinear transformation results of all neurons in the regular layer; The calculation formula of the output layer is: ； Among them, y is the output of the output layer.

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