A vision-optimized adjustment device and method

Abstract

A vision-optimized adjustment device and method involves obtaining a queue of laboratory test values ​​and separating it to obtain sub-queues of test values; grouping the sub-queues of test values ​​and obtaining fluctuation coherence estimates for each group based on the values ​​of the groups; combining multiple sub-queues of test values ​​to obtain a sub-queue of test values; obtaining fluctuation effect estimates based on the fluctuation coherence estimates and the sub-queues of test values ​​to encode the laboratory test values; within the queue of laboratory test values, obtaining a control sub-queue through a predefined queue to obtain refresh fluctuation effect estimates and an attribute queue; and performing separate estimation of the target test value based on the transmission attributes of the fluctuation trend attributes of the test value sub-queues. This approach maintains the useful value of the laboratory test value cache, improves the encoding reduction function of the encoded value queue, reduces the cache consumption of laboratory test values, and enhances the vision optimization effect.

Description

Technical Field

[0001] This invention belongs to the field of adjustment technology for visual optimization, and specifically relates to an adjustment device and method based on visual optimization. Background Technology

[0002] A biology laboratory is a place for conducting experiments related to biology. With increasingly stringent quality control requirements, the need for constant temperature and humidity environments is growing, and its applications are expanding. Most schools and hospitals have biology laboratories. People typically enter biology laboratories for learning and research.

[0003] Temperature and humidity monitoring in biological laboratories typically involves using temperature and humidity sensors mounted on culture racks within the laboratory. These sensors are directly connected to an industrial control computer. The sensors sample the temperature and humidity values ​​within the laboratory and transmit these values ​​to the computer for display. This process achieves the purpose of monitoring the temperature and humidity in the biological laboratory, and the resulting temperature or humidity values ​​are the laboratory's monitored values. For example, the prior art solution in patent publication number "CN208505360U" entitled "A Centralized Monitoring Platform for Laboratories" illustrates this.

[0004] In practical applications, temperature and humidity values ​​sampled from a biological laboratory are transmitted to an industrial control computer for display. Because the biological laboratory needs to perform tests based on a large number of laboratory test values, the industrial control computer must first cache the received laboratory test values, and then retrieve the laboratory test values ​​from the cache for display. However, given the limited cache area of ​​the industrial control computer, this often leads to the problem of laboratory test values ​​overflowing in the cache, causing subsequent test value display to lag and resulting in poor display function. Ultimately, this leads to poor visual perception for the controller. A better approach is to introduce an intermediate step to encode the sampled laboratory test values ​​to reduce their quantity. The reduced code is then transmitted to the industrial control computer's cache, and the industrial control computer retrieves the reduced code and performs decoding for display. This achieves better visual optimization for the controller and makes it easier to cache a higher number of values ​​within the limited cache area.

[0005] When temperature or humidity sensors sample laboratory test values, ensuring accuracy requires a high sampling speed. During high-speed sampling, the similarity of laboratory test values ​​is significant, resulting in high repeatability. However, while highly precise samples are very close, they are not identical. Directly applying a common Huffman coding method during decrementing encoding can lead to poor decrementing performance and substantial buffer usage after encoding. Similarly, directly applying another common wavelet coding method can cause significant loss of fluctuating values, resulting in the loss of useful buffered values. Therefore, the plr_sip method is used to actively segment the laboratory test value group before encoding. However, during continuous laboratory operation, test values ​​are constantly changing. The plr_sip method, which compares with the mean of some test values, cannot immediately grasp the trend of these changes. This often requires waiting until a complete queue is obtained before performing constant-state queue segmentation, weakening the decrementing function of the encoded value queue and increasing the buffer usage of laboratory test values. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a vision-optimized adjustment device and method. The method involves obtaining a queue of laboratory test values ​​and separating it to obtain sub-queues; grouping the sub-queues and obtaining fluctuation coherence estimates for each group based on the group values; combining multiple sub-queues to obtain a sub-segment queue of test values; obtaining fluctuation effect estimates based on the fluctuation coherence estimates and the sub-segment queues of test values; and using these estimates to refresh the fluctuation effect estimates and attribute queues, thereby encoding the laboratory test values ​​and sending them to the industrial control computer cache. Here, the fluctuation effect estimates are obtained based on the fluctuation coherence estimates and the sub-segment queues of test values. By combining the clustering attributes of the similar changes in the detection value groups obtained during the disassembly of the plr_sip method, the latency defect of the coding system in the complex detection value change scenario is overcome, and the application function of the coding is improved. In the laboratory detection value queue, the comparison sub-section queue is obtained through the constructed queue, thereby obtaining the refresh fluctuation effect estimate and attribute queue. Based on the transmission attribute of the detection value fluctuation trend attribute of the detection value sub-section queue, the target detection value is separately estimated, which maintains the useful value of the laboratory detection value cache, and can also improve the coding reduction function of the coding value queue, reduce the cache consumption of the laboratory detection value, and make the visual optimization effect better.

[0007] The present invention employs the following technical solution.

[0008] A visual optimization-based adjustment method includes:

[0009] Temperature and humidity sensors sample the temperature and humidity values ​​in the biological laboratory, respectively, and transmit the sampled values ​​to the main control chip for encoding. The main control chip then sends the encoded laboratory detection values ​​to the cache of the industrial control computer. Subsequently, the industrial control computer retrieves the encoded laboratory detection values ​​from the cache, decodes them, and displays them. The temperature or humidity value in the biological laboratory is the laboratory detection value.

[0010] The encoding method, executed on the main control chip, includes:

[0011] Step 1: Obtain the laboratory test value queue and the target test value;

[0012] Step 2: Based on all laboratory test values ​​in the laboratory test value queue, separate the laboratory test value queue into multiple test value sub-queues; based on the test value sub-queues, obtain multiple groups and the group centroid of each group; based on the scale of all test value sub-queues in each group and the group centroid of the group, obtain the fluctuation coherence estimate of each group.

[0013] Step 3: Based on the total number of detection value subqueues within each group, obtain multiple detection value subqueues for each group; based on the fluctuation coherence estimate of each group and the scale of the detection value subqueues, obtain the fluctuation effect estimate of each group on the target detection value.

[0014] Step 4: Based on the fluctuation effect estimate of the target detection value of all groups, obtain the refresh fluctuation effect estimate of the target detection value.

[0015] Step 5: Based on the refresh fluctuation effect estimate of the target detection value, obtain the attribute queue; perform encoding based on the attribute queue to obtain the encoded experimental detection value, and then transmit the encoded experimental detection value to the cache of the industrial control computer.

[0016] Preferably, the temperature sensor and humidity sensor use a predefined sampling rate. The temperature and humidity values ​​in the biological laboratory are sampled synchronously and transmitted to the main control chip. The temperature or humidity value in the biological laboratory is the laboratory test value. The main control chip arranges all the laboratory test values ​​sampled in the last two seconds according to the order of their sampling time to obtain the laboratory test value queue. In the laboratory test value queue, the last laboratory test value is defined as the target test value.

[0017] Preferably, the sampling rate X = 1000 times / second.

[0018] Preferably, Step 2 specifically includes: using the plr_sip method to separate the laboratory test value queue, obtaining multiple sub-queues, and defining all sub-queues outside the sub-queue where the target test value is located as the test value sub-queue.

[0019] Preferably, Step 2 further includes: taking the scale of the detection value sub-queue as the value on the X-axis, taking the standard deviation of all element values ​​in the detection value sub-queue as the value on the Y-axis, constructing a Cartesian system, associating all detection value sub-queues with the Cartesian system, using the DBSCAN method to group all numerical points in the Cartesian system, and obtaining multiple groups and the centroid of each group.

[0020] Preferably, Step 2 further includes: for any given group, obtaining the group centroid and the L1 norm between each numerical point within the group in the Cartesian system; The operational equations for the fluctuation coherence estimator of each group are:

[0021]

[0022] Within the equation, It is the first Estimators of the fluctuation coherence of each group; It is the first The number of laboratory test values ​​within a group, that is, the number of the first group. The number of all laboratory test values ​​in the subqueue corresponding to all numerical points in each group; It is the number of laboratory test values ​​in the laboratory test value queue; It is the first The mean of the scale of all test values ​​in a sub-cohort within a group, where the scale of the test value sub-cohort is the number of laboratory test values ​​in the test value sub-cohort. It is the first in the Descartes series The mean of the L1 norm of all numerical points within a group to the centroid of the group.

[0023] Preferably, Step 3 specifically includes: for any group, registering all laboratory test values ​​in the laboratory test value queue of all test value subqueues within the group as 1, registering other unregistered laboratory test values ​​in the laboratory test value queue as zero, defining a queue sub-segment formed by multiple consecutive test values ​​registered as 1 in the laboratory test value queue as a test value sub-segment queue of the group; extracting multiple test value sub-segments queues of the group, if a laboratory test value is registered as 1 and the adjacent laboratory test values ​​before and after it are all registered as zero, treating that laboratory test value as a test value sub-segment queue of the group.

[0024] Preferably, Step 3 further includes: The operational equation for estimating the fluctuation effect of each group on the target detection value is:

[0025]

[0026] Within the equation, For the first Estimator of the fluctuation effect of each group on the target detection value; It is the first Estimators of the fluctuation coherence of each group; It is the highest quantity of the fluctuation coherence estimator for all groups; It is the first The highest scale of the entire detection value sub-queue of each group; It is the first The number of element values ​​between the last detection value in the highest-scale detection value sub-queue of each group and the target detection value. It is the first The number of sub-segments in the detection value queue for each group; It is the first The first group The scale of the queue of individual detection value sub-sections; It is the first The mean of the scale of the entire detection value sub-section queue of each group; It is the first The standard deviation of the scale of the entire detection value sub-cohort of each group.

[0027] Preferably, Step 4 specifically includes: within the laboratory test value queue, taking the target test value as the starting point, constructing a predetermined queue before the target test value, wherein the scale of the predetermined queue is the mean of the scales of all test value sub-queues of all groups, and the predetermined queue is filled with test values ​​of its scale, and taking the target test value as the starting point, sequentially filling the predetermined queue of scales of the laboratory test value queue before the target test value into the predetermined queue; defining the test value sub-queue containing all laboratory test values ​​in the predetermined queue as the control sub-queue; calculating the mean of the fluctuation effect estimate of the group to which the control sub-queue is located on the target test value, and defining the value obtained by standardizing the mean using the Z-score method as the refresh fluctuation effect estimate of the target test value.

[0028] Preferably, Step 5 specifically includes: within the laboratory test value queue, if the estimated effect of the refresh fluctuation of the target test value exceeds a predefined estimation threshold... If the refresh fluctuation effect estimate of the target detection value is not higher than the predefined estimation threshold, no action will be taken. At that time, the interval between the target detection value and its previous adjacent detection value is defined as a separation point. The queue of laboratory detection values ​​between this separation point and the previous separation point is defined as an attribute queue. The plr_sip method is used to calculate the proximity constant of the attribute queue, and each detection value in the attribute queue is refreshed to the proximity constant to obtain the queue to be encoded. The Huffman coding method is used to encode the queue to be encoded to obtain the encoded experimental detection value, and the encoded experimental detection value is transmitted to the buffer of the industrial control computer. Then, the newly sampled laboratory detection values ​​are immediately identified and encoded according to the above method. .

[0029] A vision-optimized adjustment device includes:

[0030] Temperature and humidity sensors are installed on the culture racks in the biological laboratory. The temperature and humidity sensors are connected to the main control chip, which is also connected to the industrial control computer. The temperature and humidity sensors are used to sample the temperature and humidity values ​​in the biological laboratory and transmit the sampled temperature and humidity values ​​to the main control chip for encoding. The main control chip sends the encoded laboratory test values ​​to the buffer of the industrial control computer. The industrial control computer then retrieves the encoded laboratory test values ​​from the buffer, decodes them, and displays them. The temperature or humidity value in the biological laboratory is the laboratory test value.

[0031] The modules running on the main control chip include:

[0032] The target module is used to obtain the queue of laboratory test values ​​and the target test value;

[0033] The estimation module is used to separate the laboratory test value queue into multiple test value sub-queues based on all laboratory test values ​​in the queue; obtain multiple groups and the group centroid of each group based on the test value sub-queues; and obtain the fluctuation coherence estimate of each group based on the scale of all test value sub-queues in each group and the group centroid of the group.

[0034] The scaling module is used to obtain multiple detection value sub-queues for each group based on the total detection value sub-queues within each group; and to obtain the fluctuation effect estimate of each group on the target detection value based on the fluctuation coherence estimate of each group and the scaling of the detection value sub-queues.

[0035] The refresh module is used to obtain the refresh fluctuation estimate of the target detection value based on the fluctuation estimate of the target detection value of the entire group.

[0036] The encoding module is used to obtain the attribute queue based on the refresh fluctuation effect estimate of the target detection value; to perform encoding based on the attribute queue to obtain the encoded experimental detection value, and then to transmit the encoded experimental detection value to the cache of the industrial control computer.

[0037] The beneficial effects of the present invention are as follows: Compared with the prior art, the technical effects of the present invention include:

[0038] The process involves obtaining a queue of laboratory test values ​​and separating it to create sub-queues. These sub-queues are then grouped, and the fluctuation coherence estimate for each group is obtained based on its values. Multiple sub-queues are combined to create a sub-segment queue of test values. The fluctuation effect estimate is then obtained based on the fluctuation coherence estimate and the sub-segment queue. This is used to update the fluctuation effect estimate and attribute queue, ultimately encoding the laboratory test values ​​and sending them to the industrial control computer cache. Here, the fluctuation effect estimate obtained from the fluctuation coherence estimate and the sub-segment queue is combined with the values ​​obtained during the disassembly process using the `plr_sip` method. The accumulation attribute of the similar changes formed by the obtained detection value group overcomes the delay defect of the coding system in the complex detection value change scenario and improves the application function of coding. In the laboratory detection value queue, the control sub-section queue is obtained by constructing the set queue, thereby obtaining the refresh fluctuation effect estimate and attribute queue. Based on the transmission attribute of the detection value fluctuation trend attribute of the detection value sub-section queue, the target detection value is separately estimated, which maintains the useful value of the laboratory detection value cache and can further improve the coding reduction function of the coding value queue, reduce the cache consumption of laboratory detection values, and make the visual optimization effect better. Attached Figure Description

[0039] Figure 1 This is a flowchart of the visual optimization-based adjustment method described in this invention;

[0040] Figure 2 This is a partial structural diagram of the vision-optimized adjustment device described in this invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0042] like Figure 1 As shown, the adjustment method based on visual optimization according to the present invention includes:

[0043] Temperature and humidity sensors sample the temperature and humidity values ​​in the biological laboratory, respectively, and transmit these values ​​to the main control chip for encoding. The main control chip then sends the encoded laboratory detection values ​​to the cache of the industrial control computer. Subsequently, the industrial control computer retrieves the encoded laboratory detection values ​​from the cache, decodes them, and displays them. This achieves the purpose of detecting the temperature and humidity in the biological laboratory, and the temperature or humidity value in the biological laboratory is the laboratory detection value.

[0044] The encoding method, executed on the main control chip, includes:

[0045] Step 1: Obtain the laboratory test value queue and the target test value;

[0046] This application aims to improve the efficiency of laboratory test value caching by encoding various test values ​​during motor operation.

[0047] In a preferred but non-limiting embodiment of the present invention, the temperature sensor and the humidity sensor use a predefined sampling rate. The temperature and humidity values ​​in the biological laboratory are sampled synchronously and transmitted to the main control chip. The temperature or humidity value in the biological laboratory is the laboratory test value. The main control chip arranges all the laboratory test values ​​sampled in the last two seconds according to the order of their sampling time to obtain the laboratory test value queue. In the laboratory test value queue, the last laboratory test value is defined as the target test value.

[0048] In a preferred but non-limiting embodiment of the present invention, the sampling rate X = 1000 times / second.

[0049] Step 2: Based on all laboratory test values ​​in the laboratory test value queue, separate the laboratory test value queue into multiple test value sub-queues; based on the test value sub-queues, obtain multiple groups and the group centroid of each group; based on the scale of all test value sub-queues in each group and the group centroid of the group, obtain the fluctuation coherence estimate of each group.

[0050] In a preferred but non-limiting embodiment of the present invention, laboratory test values ​​within a wider sampling time range often exhibit wider fluctuations within the laboratory test value queue. However, when faced with laboratory test values ​​at high sampling rates, the fluctuations of laboratory test values ​​within a wider sampling time range are formed by the aggregation of considerable fluctuations. Therefore, the laboratory test value queue is initially separated using the plr_sip method, and the fluctuations formed by the laboratory test value queue are used to describe the similarity of data changes within the partial sampling time range of the target test value.

[0051] Step 2 specifically includes: using the plr_sip method to separate the laboratory test value queue, obtaining multiple sub-queues, and defining all sub-queues outside the sub-queue where the target test value is located as the test value sub-queue.

[0052] Because the plr_sip method identifies laboratory test values ​​with similar local variations, it separates the laboratory test value queue when significant changes occur. This results in the plr_sip method dividing the laboratory test value queue into smaller sub-queues with clearly distinct values ​​within each sub-queue. Therefore, it can group test values ​​based on the sampling time point outside the sub-queues and the differences between the values ​​within each sub-queue, obtaining the overall numerical fluctuation scenario within the current test values, and performing fluctuation-level calculations on the current test values.

[0053] In a preferred but non-limiting embodiment of the present invention, Step 2 further includes: using the scale of the detection value sub-queue as the value on the X-axis, using the standard deviation of all element values ​​in the detection value sub-queue as the value on the Y-axis, constructing a Cartesian system, associating all detection value sub-queues with the Cartesian system, using the DBSCAN method to group all numerical points in the Cartesian system, using the L1 norm between numerical points in the Cartesian system for the spacing of the DBSCAN method, and obtaining multiple groups and the centroid of each group.

[0054] Each group contains sub-cohorts of test values ​​with similar scales and standard deviations. The dispersion of the sub-cohorts within any group is used to determine the consistency of fluctuations in the laboratory test value cohorts.

[0055] In a preferred but non-limiting embodiment of the present invention, Step 2 further includes: for any given group, obtaining the group centroid and the L1 norm between each numerical point within the group in the Cartesian system; The operational equations for the fluctuation coherence estimator of each group are:

[0056]

[0057] Within the equation, It is the first Estimators of the fluctuation coherence of each group; It is the first The number of laboratory test values ​​within a group, that is, the number of the first group. The number of all laboratory test values ​​in the subqueue corresponding to all numerical points in each group; It is the number of laboratory test values ​​in the laboratory test value queue; It is the first The mean of the scale of all test values ​​in a sub-cohort within a group, where the scale of the test value sub-cohort is the number of laboratory test values ​​in the test value sub-cohort. It is the first in the Descartes series The mean of the L1 norm of all numerical points within a group to the centroid of the group.

[0058] and Representing the The ratio of the number of laboratory test values ​​in each group to the total number of laboratory test values ​​in the laboratory test value cohort. The higher the level, the more likely it is to be the first The subqueue of detected values ​​within the group has a higher detection value ratio, which means that the first group has a higher detection value ratio. The higher the frequency of detection values ​​with similar fluctuation attributes within a group, the higher the frequency of the sub-queues. The greater the similarity in the fluctuation properties between the sub-queues of test values ​​and the laboratory test value queues within a group; Representing the The scalar scale of the sub-queue of detected values ​​within each group and the Cartesian series. The hierarchy of the product of all numerical points within a group The higher the level, the more likely it is to be the first The deviation within each group is less obvious, the first The fluctuations in laboratory test values ​​within each sub-group were more stable.

[0059] Step 3: Based on the total number of detection value subqueues within each group, obtain multiple detection value subqueues for each group; based on the fluctuation coherence estimate of each group and the scale of the detection value subqueues, obtain the fluctuation effect estimate of each group on the target detection value.

[0060] The more clustered the test value sub-queues in any group are on the laboratory test value queue, the more obvious the relationship between the test value changes formed by the test value sub-queues is. In other words, the higher the continuity of the similarity of laboratory test values ​​among the test value sub-queues on the laboratory test value queue, the more likely the test value sub-queues in the group are to be combined on the laboratory test value queue. Based on the combined queue, the fluctuation effect of each group on the target test value is estimated.

[0061] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically includes: for any group, registering all laboratory test values ​​in the laboratory test value queue of all test value sub-queues within the group as 1, registering other unregistered laboratory test values ​​in the laboratory test value queue as zero, defining a queue sub-section formed by multiple consecutive test values ​​registered as 1 in the laboratory test value queue as a test value sub-section queue of the group; extracting multiple test value sub-section queues of the group, if a laboratory test value is registered as 1 and the adjacent laboratory test values ​​before and after it are all registered as zero, treating that laboratory test value as a test value sub-section queue of the group.

[0062] In a preferred but non-limiting embodiment of the present invention, Step 3 further includes: The operational equation for estimating the fluctuation effect of each group on the target detection value is:

[0063]

[0064] Within the equation, For the first Estimator of the fluctuation effect of each group on the target detection value; It is the first Estimators of the fluctuation coherence of each group; It is the highest quantity of the fluctuation coherence estimator for all groups; It is the first The highest scale of the entire detection value sub-queue of each group; It is the first The number of element values ​​between the last detection value in the highest-scale detection value sub-queue of each group and the target detection value. It is the first The number of sub-segments in the detection value queue for each group; It is the first The first group The scale of the queue of individual detection value sub-sections; It is the first The mean of the scale of the entire detection value sub-section queue of each group; It is the first The standard deviation of the scale of the entire sequence of test values ​​in a group is the number of test values.

[0065] and Representing the The clarity of the fluctuation coherence estimator for each group across all groups. The higher the level, the more likely it is to be the first The bias attribute of the first group is more obvious. The higher the accumulation level of the sub-queue of test values ​​within a group, the higher the influence of the fluctuation trend attribute of the test value queue on the trend of the target test value, and the less necessary it is to separate the test value queue through the fluctuation effect estimator. The trends of the target detection values ​​are similar, and the levels remain consistent. The higher the level, the more likely it is to be the first The higher the level of the fluctuation trend of the sub-queue of the detection value within a group is similar to the trend of the target detection value, the more it will maintain the trend of the target detection value, and the less necessary it is to split the queue at the target detection value. Representing the The hierarchy of the scale of the detection value sub-section queue of each group. The higher the level, the more likely it is to be the first The differences in the scale of the detection value sub-queues of each group are significant. The detection values ​​with similar changes in the detection value sub-queues show more uneven time consumption. The comparison between the queues of laboratory detection values ​​is weaker, and the necessity of performing queue splitting at the target detection value is smaller.

[0066] Therefore, the fluctuation effect of all groups on the target detection value is estimated based on the above method.

[0067] Step 4: Based on the fluctuation effect estimate of the target detection value of all groups, obtain the refresh fluctuation effect estimate of the target detection value.

[0068] The higher the estimate of the fluctuation effect near the target detection value, the less necessary it is to split the execution queue at the target detection value.

[0069] In a preferred but non-limiting embodiment of the present invention, Step 4 specifically includes: within the laboratory test value queue, taking the target test value as the starting point, constructing a set queue before the target test value, wherein the scale of the set queue is the mean of the scales of all test value sub-queues of all groups, the set queue is filled with test values ​​of its scale, and taking the target test value as the starting point, sequentially filling the set queue with the scales of the test values ​​of the set queue before the target test value in the laboratory test value queue; defining the test value sub-queue containing all laboratory test values ​​in the set queue as the control sub-queue; calculating the mean of the fluctuation effect estimate of the group to which the control sub-queue is located on the target test value, and defining the value obtained by standardizing the mean using the Z-score method as the refresh fluctuation effect estimate of the target test value.

[0070] Step 5: Based on the refresh fluctuation effect estimate of the target detection value, obtain the attribute queue; perform encoding based on the attribute queue to obtain the encoded experimental detection value, and then transmit the encoded experimental detection value to the cache of the industrial control computer.

[0071] In a preferred but non-limiting embodiment of the present invention, Step 5 specifically includes: within the laboratory test value queue, if the estimated amount of the refresh fluctuation effect of the target test value is higher than a predefined estimation threshold... If the refresh fluctuation effect estimate of the target detection value is not higher than the predefined estimation threshold, no action will be taken. At this time, the interval between the target detection value and its previous adjacent detection value is defined as a separation point. The queue of laboratory detection values ​​between this separation point and the previous separation point is defined as an attribute queue. The plr_sip method is used to calculate the proximity constant of the attribute queue, and each detection value in the attribute queue is refreshed to the proximity constant to obtain the queue to be encoded. If there is no previous separation point, no encoding process is performed, because the laboratory detection values ​​in the laboratory detection value queue are sampled in real time, so the target detection value will be refreshed continuously. Therefore, the above determination is performed on the refreshed target detection value to obtain the separation point. The Huffman coding method is used to encode the queue to be encoded to obtain the encoded experimental detection value, and the encoded experimental detection value is transmitted to the buffer of the industrial control computer. Then, the newly sampled laboratory detection values ​​are immediately determined and encoded according to the above method. .

[0072] like Figure 2 As shown, the visual optimization-based adjustment device of the present invention includes:

[0073] Temperature and humidity sensors are installed on culture racks in the biological laboratory. These sensors are connected to a main control chip, which in turn is connected to an industrial control computer. The temperature and humidity sensors are used to sample the temperature and humidity values ​​in the biological laboratory and transmit these values ​​to the main control chip for encoding. The main control chip then sends the encoded laboratory test values ​​to the buffer of the industrial control computer. Subsequently, the industrial control computer retrieves the encoded laboratory test values ​​from the buffer, decodes them, and displays them. This achieves the purpose of detecting the temperature and humidity in the biological laboratory, and the temperature or humidity value in the biological laboratory is the laboratory test value. The main control chip can be a microcontroller or a PLC.

[0074] The modules running on the main control chip include:

[0075] The target module is used to obtain the queue of laboratory test values ​​and the target test value;

[0076] The estimation module is used to separate the laboratory test value queue into multiple test value sub-queues based on all laboratory test values ​​in the queue; obtain multiple groups and the group centroid of each group based on the test value sub-queues; and obtain the fluctuation coherence estimate of each group based on the scale of all test value sub-queues in each group and the group centroid of the group.

[0077] The scaling module is used to obtain multiple detection value sub-queues for each group based on the total detection value sub-queues within each group; and to obtain the fluctuation effect estimate of each group on the target detection value based on the fluctuation coherence estimate of each group and the scaling of the detection value sub-queues.

[0078] The refresh module is used to obtain the refresh fluctuation estimate of the target detection value based on the fluctuation estimate of the target detection value of the entire group.

[0079] The encoding module is used to obtain the attribute queue based on the refresh fluctuation effect estimate of the target detection value; to perform encoding based on the attribute queue to obtain the encoded experimental detection value, and then to transmit the encoded experimental detection value to the cache of the industrial control computer.

[0080] The beneficial effects of the present invention are as follows: Compared with the prior art, the technical effects of the present invention include:

[0081] The process involves obtaining a queue of laboratory test values ​​and separating it to create sub-queues. These sub-queues are then grouped, and the fluctuation coherence estimate for each group is obtained based on its values. Multiple sub-queues are combined to create a sub-segment queue of test values. The fluctuation effect estimate is then obtained based on the fluctuation coherence estimate and the sub-segment queue. This is used to update the fluctuation effect estimate and attribute queue, ultimately encoding the laboratory test values ​​and sending them to the industrial control computer cache. Here, the fluctuation effect estimate obtained from the fluctuation coherence estimate and the sub-segment queue is combined with the values ​​obtained during the disassembly process using the `plr_sip` method. The accumulation attribute of the similar changes formed by the obtained detection value group overcomes the delay defect of the coding system in the complex detection value change scenario and improves the application function of coding. In the laboratory detection value queue, the control sub-section queue is obtained by constructing the set queue, thereby obtaining the refresh fluctuation effect estimate and attribute queue. Based on the transmission attribute of the detection value fluctuation trend attribute of the detection value sub-section queue, the target detection value is separately estimated, which maintains the useful value of the laboratory detection value cache and can further improve the coding reduction function of the coding value queue, reduce the cache consumption of laboratory detection values, and make the visual optimization effect better.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention without departing from the spirit and scope of the present invention. Any modifications or equivalent substitutions should be covered within the scope of protection of the claims of the present invention.

Claims

1. A visual optimization-based adjustment method, characterized in that, include: Temperature and humidity sensors sample the temperature and humidity values ​​in the biological laboratory, respectively, and transmit the sampled values ​​to the main control chip for encoding. The main control chip then sends the encoded laboratory detection values ​​to the cache of the industrial control computer. Subsequently, the industrial control computer retrieves the encoded laboratory detection values ​​from the cache, decodes them, and displays them. The temperature or humidity value in the biological laboratory is the laboratory detection value. The encoding method, executed on the main control chip, includes: Step 1: Obtain the laboratory test value queue and the target test value; Step 2: Based on all laboratory test values ​​in the laboratory test value queue, separate the laboratory test value queue into multiple test value sub-queues; based on the test value sub-queues, obtain multiple groups and the group centroid of each group; based on the scale of all test value sub-queues in each group and the group centroid of the group, obtain the fluctuation coherence estimate of each group. Step 3: Based on the total number of detection value subqueues within each group, obtain multiple detection value subqueues for each group; based on the fluctuation coherence estimate of each group and the scale of the detection value subqueues, obtain the fluctuation effect estimate of each group on the target detection value. Step 4: Based on the fluctuation effect estimate of the target detection value of all groups, obtain the refresh fluctuation effect estimate of the target detection value. Step 5: Based on the refresh fluctuation effect estimate of the target detection value, obtain the attribute queue; perform encoding based on the attribute queue to obtain the encoded experimental detection value, and then transmit the encoded experimental detection value to the cache of the industrial control computer; Step 2 specifically includes: using the plr_sip method to separate the laboratory test value queue, obtaining multiple sub-queues, and defining all sub-queues outside the sub-queue where the target test value is located as the test value sub-queue; Step 2 specifically includes: taking the scale of the detection value sub-queue as the value on the X-axis, taking the standard deviation of all element values ​​in the detection value sub-queue as the value on the Y-axis, constructing a Cartesian system, associating all detection value sub-queues with this Cartesian system, using the DBSCAN method to group all numerical points in this Cartesian system, and obtaining multiple groups and the centroid of each group. Step 2 specifically includes: for any given group, within the Cartesian system, obtaining the group centroid and the L1 norm between each numerical point within the group; the operational equation for the fluctuation coherence estimator of the Rth group is: Within the equation, Y R It is the fluctuation coherence estimator for the R-th group; p R p is the number of laboratory test values ​​within the R-th group, which is the total number of laboratory test values ​​in the corresponding test value subqueue for all numerical points in the R-th group; p′ is the number of laboratory test values ​​in the laboratory test value queue. It is the mean of the scales of all test value sub-cohorts within the R-th group, where the scale of the test value sub-cohort is the number of laboratory test values ​​within the test value sub-cohort. It is the mean of the L1 norms of all numerical points in the R-th group within the Cartesian system up to the centroid of the group; Step 3 specifically includes: For any group, register all laboratory test values ​​in the laboratory test value queue of all subqueues of the test values ​​in the group as 1, register the other unregistered laboratory test values ​​in the laboratory test value queue as zero, and define the queue sub-segment formed by multiple consecutive test values ​​registered as 1 in the laboratory test value queue as a test value sub-segment queue of the group; extract multiple test value sub-segments queues of the group, and if a laboratory test value is registered as 1 and the adjacent laboratory test values ​​before and after it are all registered as zero, treat that laboratory test value as a test value sub-segment queue of the group; Step 3 further includes: the operational equation for estimating the fluctuation effect of the R-th group on the target detection value is: Within the equation, H R Y is the estimator of the fluctuation effect of the R-th group on the target detection value; R Y is the estimator of the fluctuation coherence of the R-th group; zg It is the highest measure of the fluctuation coherence estimator for all groups; M′ R，zg It is the highest scale of the entire sequence of detection values ​​in the R-th group; E′ R，zg p″ is the number of element values ​​between the last detection value in the highest-scale sub-section queue of the R-th group and the target detection value; R M′ is the number of sub-string queues of the detection values ​​of the R-th group; R，j It is the scale of the queue of the j-th detection value sub-section of the R-th group; It is the mean of the scale of the entire sub-group of the R-th group; It is the standard deviation of the scale of the entire sub-group of the R-th group of test values.

2. The adjustment method based on visual optimization according to claim 1, characterized in that, Temperature and humidity sensors synchronously sample the temperature and humidity values ​​in the biological laboratory at a predefined sampling rate X and transmit them to the main control chip. The temperature or humidity value in the biological laboratory is the laboratory test value. The main control chip arranges all the laboratory test values ​​sampled within the last two seconds according to the order of their sampling time to obtain the laboratory test value queue. In the laboratory test value queue, the last laboratory test value is defined as the target test value.

3. The adjustment method based on visual optimization according to claim 2, characterized in that, Sampling rate X = 1000 times / second.

4. The adjustment method based on visual optimization according to claim 3, characterized in that, Step 4 specifically includes: Within the laboratory test value queue, taking the target test value as the starting point, constructing a predefined queue before the target test value. The scale of this predefined queue is the mean of the scales of all test value sub-queues in the entire group. This predefined queue is filled with test values ​​of its scale. Taking the target test value as the starting point, the scales of the predefined queues before the target test value in the laboratory test value queue are sequentially filled into the predefined queue. The test value sub-queue containing all laboratory test values ​​in the predefined queue is defined as the control sub-queue. The mean of the fluctuation estimate of the group in the entire control sub-queue to the target test value is calculated, and the value obtained by standardizing this mean using the Z-score method is defined as the refresh fluctuation estimate of the target test value.

5. The adjustment method based on visual optimization according to claim 4, characterized in that, Step 5 specifically includes: Within the laboratory test value queue, if the estimated refresh fluctuation of the target test value is higher than the predefined estimated threshold S, no action is taken; if the estimated refresh fluctuation of the target test value is not higher than the predefined estimated threshold S, the interval between the target test value and its previously adjacent test value is defined as a separation point, and the queue formed by the laboratory test values ​​between this separation point and the previous separation point is defined as an attribute queue. The plr_sip method is used to calculate the approximate constant of this attribute queue, and each test value in this attribute queue is refreshed to this approximate constant to obtain the queue to be encoded; the Huffman coding method is used to encode the queue to be encoded to obtain the encoded experimental test value, and the encoded experimental test value is transmitted to the buffer of the industrial control computer; then, based on the above method, real-time identification and encoding are performed on the newly sampled laboratory test values.

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