Adjusting device and method based on visual optimization

Abstract

An adjustment device and method based on visual optimization includes: obtaining a laboratory detection value queue and performing separation on the laboratory detection value queue to obtain a detection value sub-queue; grouping the detection value sub-queues, and obtaining the fluctuation coherence estimator of each group according to the values of the groups; combining the plurality of detection value sub-queues to obtain a detection value sub-section queue; obtaining a fluctuation effect estimator according to the fluctuation coherence estimator and the detection value sub-section queue, and achieving the coding of the laboratory detection value; in a laboratory detection value queue, a contrast sub-section queue is obtained through a constructed and set queue so as to obtain a refresh fluctuation effect estimator and an attribute queue, and a target detection value is separately estimated according to a transmission attribute of a detection value fluctuation trend attribute of the detection value sub-section queue, so that a useful value of a laboratory detection value cache is maintained; and the coding reduction function of the coding value queue can be improved, the cache consumption of laboratory detection values is weakened, and the visual optimization effect can be better.

Description

Technical Field

[0001] The present invention belongs to the technical field of visual optimization adjustment, and in particular relates to an adjustment device and method based on visual optimization. Background Art

[0002] Biological laboratories are places for conducting experiments related to biological subjects. As the requirements for quality control become more and more stringent, the demand for constant temperature and humidity environments is getting higher and higher, and the application areas are becoming wider and wider. Generally, there are biological laboratories in schools, and most laboratories in hospitals are also biological laboratories. People usually go to biological laboratories for learning and research.

[0003] The temperature and humidity of the biological laboratory are usually detected by temperature sensors and humidity sensors installed on the culture racks in the biological laboratory. The temperature sensors and humidity sensors are directly connected to the industrial control computer, so that the temperature and humidity values ​​in the biological laboratory are sampled by the temperature sensors and humidity sensors respectively, and the sampled temperature and humidity values ​​in the biological laboratory are transmitted to the industrial control computer for display, so as to achieve the purpose of detecting the temperature and humidity of the biological laboratory. The temperature or humidity value in the biological laboratory is the laboratory detection value. For example, the existing technical solution in the patent publication number "CN208505360U" and the patent name "A laboratory centralized monitoring platform".

[0004] In actual applications, the sampled temperature and humidity values ​​in the biological laboratory are transmitted to the industrial control computer for display. Because the biological laboratory needs to be tested based on a large number of laboratory test values, the industrial control computer must first cache the collected laboratory test values, and then take out the laboratory test values ​​from the cache for display. However, facing the limited cache area in the industrial control computer, this often causes the problem of laboratory test values ​​overflowing in the cache, causing subsequent test value displays to become stuck and resulting in poor display performance, ultimately resulting in poor visual perception for the controller. A better method is to introduce an intermediate link to encode the sampled laboratory test values ​​to reduce them, and then transmit the reduced codes to the cache of the industrial control computer, and then the industrial control computer takes out the reduced codes for decoded display. In this way, the controller's visual optimization effect can be better achieved, and it is suitable to use the limited cache area to cache a higher number of values.

[0005] When the temperature sensor or humidity sensor samples the laboratory test value, in order to ensure the accuracy of the test, the sampling speed is usually not low. The similarity of the laboratory test value is not low when the laboratory test value is sampled at a high speed, so that the sampled laboratory test value has a large repeatability. However, the sampled precision test values ​​are very close but not the same as a whole. When the reduction coding is directly executed, a commonly used Huffman coding method will result in poor coding reduction performance, and the cache area consumption after coding is not small; when the reduction coding is directly executed, another commonly used wavelet coding will result in a lot of test value fluctuations. The value is lost, and the useful value of the laboratory test value cache is lost. Therefore, the plr_sip method is used before encoding to perform active segmentation of the laboratory test value group. However, during the continuous operation of the laboratory, the test value is in a state of dynamic change. Because the plr_sip method needs to be compared with the average of some test values, it is impossible to grasp the trend of the test value change in real time, resulting in the need to wait until the complete queue is obtained to perform constant queue segmentation, which weakens the coding reduction function of the coding value queue and increases the cache consumption of the laboratory test value. Summary of the invention

[0006] In order to solve the defects in the prior art, the present invention proposes an adjustment device and method based on visual optimization, which obtains a laboratory test value queue and separates it to obtain a test value sub-queue; the test value sub-queue is grouped, and the fluctuation consistency estimation of each group is obtained according to the value of the group; a plurality of test value sub-queues are combined to obtain a test value sub-node queue; the fluctuation effect estimation is obtained according to the fluctuation consistency estimation and the test value sub-node queue, thereby obtaining a refresh fluctuation effect estimation and an attribute queue, and the encoding of the laboratory test value is achieved and sent to the industrial control computer cache; here, the fluctuation effect estimation is obtained according to the fluctuation consistency estimation and the test value sub-node queue , combined with the accumulation attribute of the change similarity formed by the test value group obtained when the plr_sip method is executed, the defect of delay in encoding system recognition under complex test value change scenarios is overcome, and the application performance of encoding is improved; in the laboratory test value queue, the comparison sub-node queue is obtained through the queue set by the structure, so as to obtain the refresh fluctuation effect estimate and the attribute queue, and the target test value is separately estimated according to the transmission attribute of the test value fluctuation trend attribute of the test value sub-node queue, which protects the effective value of the laboratory test value cache, and can also improve the encoding reduction function of the encoding value queue, reduce the cache consumption of the laboratory test value, and make the visual optimization effect better.

[0007] The present invention uses the following technical solutions.

[0008] A method for adjusting based on visual optimization, comprising: The temperature sensor and humidity sensor respectively sample the temperature value and humidity value in the biological laboratory and transmit the sampled temperature value and humidity value in the biological laboratory to the main control chip for encoding. The main control chip sends the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer takes out the encoded laboratory test value in the cache and decodes it for display. The temperature value or humidity value in the biological laboratory is the laboratory test value. The encoding method is executed on a main control chip, comprising: Step 1, obtain the laboratory test value queue and the target test value; Step 2, according to all laboratory test values ​​in the laboratory test value queue, the laboratory test value queue is separated to obtain multiple test value sub-queues; according to the test value sub-queues, multiple groups and the group centroids of each group are obtained; according to the scale of all test value sub-queues in each group and the group centroid of the group, the fluctuation continuity estimation of each group is obtained; Step 3, based on the total detection value sub-queues in each group, obtain multiple detection value sub-queues of each group; based on the fluctuation continuity estimate of each group and the scale of the detection value sub-queue, obtain the fluctuation effect estimate of each group on the target detection value; Step 4, obtaining an updated estimate of the fluctuation effect of the target detection value based on the estimated effect of all groups on the target detection value; Step 5, obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

[0009] Preferably, 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 laboratory test values ​​sampled within the last two seconds according to the chronological order of their sampling time points to obtain a laboratory test value queue; in the laboratory test value queue, the last laboratory test value is defined as the target test value.

[0010] Preferably, the sampling rate X=1000 times / second.

[0011] Preferably, Step 2 specifically includes: using the plr_sip method to separate the laboratory test value queue to obtain multiple sub-queues, and defining all sub-queues except the sub-queue where the target test value is located as the test value sub-queue.

[0012] Preferably, Step 2 specifically also includes: taking the scale of the detection value subqueue as the value on the X-axis, and the standard deviation of all element values ​​in the detection value subqueue as the value on the Y-axis, constructing a Cartesian system, associating all detection value subqueues into the Cartesian system, and using the DBSCAN method to group all numerical points in the Cartesian system to obtain multiple groups and the group centroid of each group.

[0013] Preferably, Step 2 specifically includes: for a random group, obtaining the centroid of the group and the L1 norm between each numerical point in the group in the Cartesian system; The operational equation for the fluctuation coherence estimator of groups is:

[0014] In the equation, It is Estimator of the fluctuation coherence of the groups; It is The number of laboratory test values ​​in each group, that is, The number of all laboratory test values ​​in the test value sub-cohort corresponding to all numerical points in each group; is the number of laboratory test values ​​in the laboratory test value cohort; It is The mean of the scales of all test value sub-cohorts in 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 The mean of the L1 norm of all numerical points in a group to the centroid of the group.

[0015] Preferably, Step 3 specifically includes: facing a random group, registering all laboratory test values ​​of all test value subqueues in the group as one in the laboratory test value queue, registering the laboratory test values ​​that are not registered in the laboratory test value queue as zero, and defining a queue sub-node formed by multiple consecutive test values ​​registered as one in the laboratory test value queue as a test value sub-node queue of the group; extracting multiple test value sub-node queues of the group, if a laboratory test value is registered as one and the front and rear adjacent laboratory test values ​​are registered as zero, treating the laboratory test value as a test value sub-node queue of the group.

[0016] Preferably, Step 3 specifically further includes: The operational equation for estimating the fluctuation effect of each group on the target detection value is:

[0017] In the equation, For the An estimate of the fluctuation effect of each group on the target detection value; It is Estimator of the fluctuation coherence of the groups; is the highest value of the fluctuation coherence estimate of all groups; It is The maximum size of the total detection value sub-node queue of the group; It is The number of element values ​​between the last detection value of the highest-scale detection value sub-node queue of each group and the target detection value; It is The number of detection value sub-section queues for each packet; It is The first group The size of the detection value sub-section queue; It is The mean of the scale of all detection value sub-node queues of the group; It is The standard deviation of the scale of the overall detection value sub-section of the group.

[0018] Preferably, Step 4 specifically includes: in the laboratory test value queue, taking the target test value as the starting point, constructing a set queue before the target test value, the scale of the set queue is the average of the scales of all test value sub-node queues of all groups, the set queue is filled with test values ​​of its scale number, and taking the target test value as the starting point, the scale test values ​​of the set queue before the target test value in the laboratory test value queue are filled into the set queue one by one in sequence; defining the test value sub-node queue where all laboratory test values ​​contained in the set queue are located as a control sub-node queue; calculating the average of the fluctuation effect estimates of the groups where all control sub-node queues are located on the target test value, and defining the value obtained by standardizing the average using the Z-score method as the refresh fluctuation effect estimate of the target test value.

[0019] Preferably, Step 5 specifically includes: in the laboratory test value queue, if the refresh fluctuation effect estimate of the target test value is higher than the pre-defined estimated critical value If the refresh fluctuation effect estimate of the target detection value is not higher than the pre-defined estimated critical value, no processing is performed; When the target detection value and its previous adjacent detection value are separated, the interval is defined as a separation, and the queue formed by the laboratory detection values ​​between the separation and the previous separation is defined as an attribute queue. The plr_sip method is used to calculate the approximate constant of the attribute queue, and each detection value on the attribute queue is refreshed to the 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 laboratory detection value, and the encoded laboratory detection value is transmitted to the cache of the industrial control computer; then the laboratory detection value transmitted by the new sample is immediately identified and encoded according to the above method; .

[0020] A regulating device based on visual optimization, comprising: The temperature sensor and humidity sensor are arranged on the culture rack in the biological laboratory. The temperature sensor and humidity sensor are connected to the main control chip, which is also connected to the industrial control computer. The temperature sensor and humidity sensor are used to sample the temperature value and humidity value in the biological laboratory respectively and transmit the sampled temperature value and humidity value in the biological laboratory to the main control chip for encoding. The main control chip is used to send the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer is used to take out the encoded laboratory test value in the cache and decode it for display. The temperature value or humidity value in the biological laboratory is the laboratory test value. The modules running on the main control chip include: A destination module, which is used to obtain a laboratory test value queue and a destination test value; An estimation module is used to separate the laboratory test value queue into multiple test value sub-queues according to all laboratory test values ​​in the laboratory test value queue; obtain multiple groups and the group centroid of each group according to the test value sub-queues; obtain the fluctuation continuity estimation of each group according to the scale of all test value sub-queues in each group and the group centroid of the group; A scale module, which is used to obtain multiple detection value sub-node queues of each group according to the entire detection value sub-queue in each group; obtain an estimate of the fluctuation effect of each group on the target detection value according to the fluctuation continuity estimate of each group and the scale of the detection value sub-node queue; A refreshing module, which is used to obtain an estimated amount of refresh fluctuation effect of the target detection value according to the estimated amount of fluctuation effect of all groups on the target detection value; The encoding module is used to obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

[0021] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The laboratory test value queue is obtained and separated to obtain the test value sub-queue; the test value sub-queue is grouped, and the fluctuation consistency estimate of each group is obtained according to the value of the group; multiple test value sub-queues are combined to obtain the test value sub-node queue; the fluctuation effect estimate is obtained according to the fluctuation consistency estimate and the test value sub-node queue, so as to obtain the refresh fluctuation effect estimate and the attribute queue, and the laboratory test value is encoded and sent to the industrial control computer cache; here, the fluctuation effect estimate is obtained according to the fluctuation consistency estimate and the test value sub-node queue, and the plr_sip method is combined to obtain the The accumulation property of similar changes formed by the obtained test value group overcomes the defect of delay in coding system recognition under complex test value change scenarios and improves the application performance of coding; in the laboratory test value queue, the reference sub-node queue is obtained through the structured set queue to obtain the refresh fluctuation effect estimate and the attribute queue, and the target test value is separately estimated according to the transmission attribute of the test value fluctuation trend attribute of the test value sub-node queue, which protects the effective value of the laboratory test value cache, improves the coding reduction function of the coding value queue, reduces the cache consumption of laboratory test values, and makes the visual optimization effect better. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the adjustment method based on visual optimization described in the present invention; Figure 2 It is a partial structural diagram of the adjustment device based on visual optimization described in the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely express the technical solution of the present invention. The embodiments expressed in this application are only some embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the protection scope of the present invention.

[0024] like Figure 1 As shown, the adjustment method based on visual optimization described in the present invention includes: The temperature sensor and humidity sensor respectively sample the temperature and humidity values ​​in the biological laboratory and transmit the sampled temperature and humidity values ​​in the biological laboratory to the main control chip for encoding. The main control chip sends the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer takes out the encoded laboratory test value in the cache and decodes it for display, thereby achieving the purpose of performing temperature and humidity detection on the biological laboratory. The temperature or humidity value in the biological laboratory is the laboratory test value. The encoding method is executed on a main control chip, comprising: Step 1, obtain the laboratory test value queue and the target test value; The present application aims to improve the efficiency of laboratory test value cache by encoding multiple types of test values ​​during motor operation.

[0025] 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 laboratory test values ​​sampled within the last two seconds according to the chronological order of their sampling time points to obtain a laboratory test value queue; in the laboratory test value queue, the last laboratory test value is defined as the target test value.

[0026] In a preferred but non-limiting embodiment of the present invention, the sampling rate X=1000 times / second.

[0027] Step 2, according to all laboratory test values ​​in the laboratory test value queue, the laboratory test value queue is separated to obtain multiple test value sub-queues; according to the test value sub-queues, multiple groups and the group centroids of each group are obtained; according to the scale of all test value sub-queues in each group and the group centroid of the group, the fluctuation continuity estimation of each group is obtained; In a preferred but non-restrictive embodiment of the present invention, within a laboratory test value queue, laboratory test values ​​in a wider sampling time point range often form wider fluctuations. However, in the face of laboratory test values ​​at high sampling speeds, the fluctuations of laboratory test values ​​in a wider sampling time point range are formed by the aggregation of considerable fluctuations. Therefore, the laboratory test value queue is initially separated by the plr_sip method, and the fluctuation status formed by the laboratory test value queue is used to describe the data variation similarity of the partial sampling time point range where the target test value is located.

[0028] 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 except the sub-queue where the target test value is located as the test value sub-queue.

[0029] Because the plr_sip method identifies laboratory test values ​​with similar local changes, it separates the laboratory test value queue when the laboratory test value has a significant change. Therefore, within the range of test values ​​with significant changes, the plr_sip method will divide the laboratory test value queue into smaller partial test value sub-queues with obvious test value differences. Therefore, the test value groups can be grouped based on the scale of the sampling time points outside the test value sub-queue and the test value differences within it, and the overall value fluctuation scene of the current test value can be obtained, and the fluctuation level calculation can be performed on the current test value.

[0030] In a preferred but non-restrictive implementation of the present invention, Step 2 specifically also includes: taking the scale of the detection value subqueue as the value on the X-axis, and the standard deviation of all element values ​​in the detection value subqueue as the value on the Y-axis, constructing a Cartesian system, associating all detection value subqueues to the Cartesian system, and using the DBSCAN method to group all numerical points in the Cartesian system. The spacing of the DBSCAN method uses the L1 norm between the numerical points in the Cartesian system to obtain multiple groups and the group centroid of each group.

[0031] Each group contains test value sub-cohorts with similar scales and standard deviations. The dispersion of the arrangement of the sub-cohorts in any group is used to determine the consistency of the fluctuations of the test value sub-cohorts represented by the group on the laboratory test value cohorts.

[0032] In a preferred but non-limiting embodiment of the present invention, Step 2 specifically includes: facing a random group, obtaining the group centroid of the group and the L1 norm between each numerical point in the group in the Cartesian system; The operational equation for the fluctuation coherence estimator of groups is:

[0033] In the equation, It is Estimator of the fluctuation coherence of the groups; It is The number of laboratory test values ​​in each group, that is, The number of all laboratory test values ​​in the test value sub-cohort corresponding to all numerical points in each group; is the number of laboratory test values ​​in the laboratory test value cohort; It is The mean of the scales of all test value sub-cohorts in 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 The mean of the L1 norm of all numerical points in a group to the centroid of the group.

[0034] and Representative The ratio of the number of laboratory test values ​​in the group to the number of laboratory test values ​​in the laboratory test value group. The higher, the The detection value sub-queue within the group has a higher detection value ratio, which means that The higher the frequency of the detection value sub-queues of similar fluctuation attributes in a group, the higher the The higher the similarity between the fluctuation attributes of the test value sub-cohort and the laboratory test value cohort within each group, Representative The scale of the detection value sub-cohort within each group and the Cartesian system The product level of all numerical points in a group, The higher, the The deviations within the groups are less obvious. The fluctuation of laboratory test values ​​in the test value sub-cohort within each group was more stable.

[0035] Step 3, based on the total detection value sub-queues in each group, obtain multiple detection value sub-queues of each group; based on the fluctuation continuity estimate of each group and the scale of the detection value sub-queue, obtain the fluctuation effect estimate of each group on the target detection value; The more concentrated the arrangement of the test value sub-queues in any group on the laboratory test value queue is, the more obvious the relationship between the test value change conditions formed by the test value sub-queues is, that is, the higher the consistency of similar laboratory test value conditions between the test value sub-queues on the laboratory test value queue is. Therefore, the test value sub-queues within the group are combined on the laboratory test value queue, and based on the combined queue, the estimated effect of each group on the fluctuation of the target test value is determined.

[0036] In a preferred but non-restrictive implementation of the present invention, Step 3 specifically includes: facing an arbitrary group, registering all laboratory test values ​​of the test value subqueues in the group as one in the laboratory test value queue, registering the laboratory test values ​​that are not registered in the laboratory test value queue as zero, and defining a queue sub-node formed by multiple consecutive test values ​​registered as one in the laboratory test value queue as a test value sub-node queue of the group; extracting multiple test value sub-node queues of the group, if the laboratory test value is registered as one and the front and rear adjacent laboratory test values ​​are registered as zero, treating the laboratory test value as a test value sub-node queue of the group.

[0037] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically includes: The operational equation for estimating the fluctuation effect of each group on the target detection value is:

[0038] In the equation, For the An estimate of the fluctuation effect of each group on the target detection value; It is Estimator of the fluctuation coherence of the groups; is the highest value of the fluctuation coherence estimate of all groups; It is The maximum size of the total detection value sub-node queue of the group; It is The number of element values ​​between the last detection value of the highest-scale detection value sub-node queue of each group and the target detection value; It is The number of detection value sub-section queues for each packet; It is The first group The size of the detection value sub-section queue; It is The mean of the scale of all detection value sub-node queues of the group; It is The standard deviation of the measure of the overall detection value subset of a group is the number of detection values.

[0039] and Representative The apparent degree of the fluctuation coherence estimate of each group within the whole group, The higher, the The deviation property of the first group is more obvious. The higher the level of aggregation of the test value sub-cohorts within a group, the higher the level of effect of the test value fluctuation trend attribute of the laboratory test value cohort on the trend of the target test value, and the less necessary it is to perform separation of the laboratory test value cohort via the fluctuation effect estimate; The trend of the target detection value is similar to the maintenance level, The higher, the The higher the level of consistency between the fluctuation of the detection value sub-queues within a group and the trend of the target detection value, the more likely it is to maintain the trend of the target detection value, and the less necessary it is to split the queue at the target detection value; Representative The product level of the scale of the detection value sub-node queue of each group, The higher, the The differences in the sizes of the test value sub-node queues of each group are significant, the time taken to present test values ​​with similar changes in the test value sub-node queues is more uneven, the comparison of queue splitting with laboratory test value queues is weaker, and the necessity of performing queue splitting at the target test value is smaller.

[0040] Therefore, based on the above method, an estimate of the fluctuation effect of all groups on the target detection value is obtained.

[0041] Step 4, obtaining an updated estimate of the fluctuation effect of the target detection value based on the estimated effect of all groups on the target detection value; The higher the estimated amount of fluctuation effect near the target detection value, the less necessary it is to perform queue splitting at the target detection value.

[0042] In a preferred but non-restrictive embodiment of the present invention, Step 4 specifically includes: in the laboratory test value queue, taking the target test value as the starting point, constructing a set queue before the target test value, the scale of the set queue is the mean of the scales of all test value sub-node queues of all groups, the set queue is filled with test values ​​of its scale number, and taking the target test value as the starting point, the scale test values ​​of the set queue before the target test value in the laboratory test value queue are filled into the set queue one by one in sequence; the test value sub-node queue in which all laboratory test values ​​contained in the set queue are located is defined as a control sub-node queue; the mean of the estimated amount of fluctuation effect of the groups in which all control sub-node queues are located on the target test value is calculated, and the value obtained by standardizing the mean using the Z-score method is defined as the refresh fluctuation effect estimate of the target test value.

[0043] Step 5, obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

[0044] In a preferred but non-limiting embodiment of the present invention, Step 5 specifically includes: in the laboratory test value queue, if the refresh fluctuation effect estimate of the target test value is higher than the pre-defined estimated critical value If the refresh fluctuation effect estimate of the target detection value is not higher than the pre-defined estimated critical value, no processing is performed; When the interval between the target detection value and its previous adjacent detection value is defined as a separation, the queue formed by the laboratory detection values ​​between the separation and the previous separation is defined as an attribute queue, the approximate constant of the attribute queue is calculated by the plr_sip method, and each detection value on the attribute queue is refreshed to the approximate constant to obtain the queue to be encoded; if there is no previous separation, the encoding process is not performed, because the laboratory detection values ​​in the laboratory detection value queue are sampled in real time, then the target detection value will be continuously refreshed backward, and the above identification is performed on the refreshed target detection value to obtain the separation; the Huffman coding method is used to encode the queue to be encoded to obtain the encoded laboratory detection value, and the encoded laboratory detection value is transmitted to the cache of the industrial control computer; then the laboratory detection value transmitted by the new sampling is immediately identified and encoded according to the above method; .

[0045] like Figure 2 As shown, the adjustment device based on visual optimization described in the present invention includes: The temperature sensor and humidity sensor are arranged on the culture rack in the biological laboratory. The temperature sensor and humidity sensor are connected to the main control chip, and the main control chip is also connected to the industrial control computer. The temperature sensor and humidity sensor are respectively used to sample the temperature value and humidity value in the biological laboratory and transmit the sampled temperature value and humidity value in the biological laboratory to the main control chip for encoding. The main control chip is used to send the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer is used to take out the encoded laboratory test value in the cache and decode it for display, so as to achieve the purpose of performing detection on the temperature and humidity of the biological laboratory. The temperature value or humidity value in the biological laboratory is the laboratory test value; the main control chip can be a single-chip microcomputer or a PLC.

[0046] The modules running on the main control chip include: A destination module, which is used to obtain a laboratory test value queue and a destination test value; An estimation module is used to separate the laboratory test value queue into multiple test value sub-queues according to all laboratory test values ​​in the laboratory test value queue; obtain multiple groups and the group centroid of each group according to the test value sub-queues; obtain the fluctuation continuity estimation of each group according to the scale of all test value sub-queues in each group and the group centroid of the group; A scale module, which is used to obtain multiple detection value sub-node queues of each group according to the entire detection value sub-queue in each group; obtain an estimate of the fluctuation effect of each group on the target detection value according to the fluctuation continuity estimate of each group and the scale of the detection value sub-node queue; A refreshing module, which is used to obtain an estimated amount of refresh fluctuation effect of the target detection value according to the estimated amount of fluctuation effect of all groups on the target detection value; The encoding module is used to obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

[0047] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The laboratory test value queue is obtained and separated to obtain the test value sub-queue; the test value sub-queue is grouped, and the fluctuation consistency estimate of each group is obtained according to the value of the group; multiple test value sub-queues are combined to obtain the test value sub-node queue; the fluctuation effect estimate is obtained according to the fluctuation consistency estimate and the test value sub-node queue, so as to obtain the refresh fluctuation effect estimate and the attribute queue, and the laboratory test value is encoded and sent to the industrial control computer cache; here, the fluctuation effect estimate is obtained according to the fluctuation consistency estimate and the test value sub-node queue, and the plr_sip method is combined to obtain the The accumulation property of similar changes formed by the obtained test value group overcomes the defect of delay in coding system recognition under complex test value change scenarios and improves the application performance of coding; in the laboratory test value queue, the reference sub-node queue is obtained through the structured set queue to obtain the refresh fluctuation effect estimate and the attribute queue, and the target test value is separately estimated according to the transmission attribute of the test value fluctuation trend attribute of the test value sub-node queue, which protects the effective value of the laboratory test value cache, improves the coding reduction function of the coding value queue, reduces the cache consumption of laboratory test values, and makes the visual optimization effect better.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for adjusting based on visual optimization, characterized in that: include: The temperature sensor and humidity sensor respectively sample the temperature value and humidity value in the biological laboratory and transmit the sampled temperature value and humidity value in the biological laboratory to the main control chip for encoding. The main control chip sends the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer takes out the encoded laboratory test value in the cache and decodes it for display. The temperature value or humidity value in the biological laboratory is the laboratory test value. The encoding method is executed on a main control chip, comprising: Step 1, obtain the laboratory test value queue and the target test value; Step 2, according to all laboratory test values ​​in the laboratory test value queue, the laboratory test value queue is separated to obtain multiple test value sub-queues; according to the test value sub-queues, multiple groups and the group centroids of each group are obtained; according to the scale of all test value sub-queues in each group and the group centroid of the group, the fluctuation continuity estimation of each group is obtained; Step 3, based on the total detection value sub-queues in each group, obtain multiple detection value sub-queues of each group; based on the fluctuation continuity estimate of each group and the scale of the detection value sub-queue, obtain the fluctuation effect estimate of each group on the target detection value; Step 4, obtaining an updated estimate of the fluctuation effect of the target detection value based on the estimated effect of all groups on the target detection value; Step 5, obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

2. The adjustment method based on visual optimization according to claim 1, characterized in that: Temperature sensors and humidity sensors use predefined sampling rates 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 laboratory test values ​​sampled within the last two seconds according to the chronological order of their sampling time points to obtain a 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 speed X = 1000 times / second.

4. The adjustment method based on visual optimization according to claim 3 is characterized in that: 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 except the sub-queue where the target test value is located as the test value sub-queue.

5. The adjustment method based on visual optimization according to claim 4, characterized in that: Step 2 specifically includes: taking the scale of the detection value subqueue as the value on the X-axis, taking the standard deviation of all element values ​​in the detection value subqueue as the value on the Y-axis, constructing a Cartesian system, associating all detection value subqueues into the Cartesian system, using the DBSCAN method to group all numerical points in the Cartesian system, and obtaining multiple groups and the group centroid of each group; Step 2 specifically includes: for a random group, in the Cartesian system, obtaining the centroid of the group and the L1 norm between each numerical point in the group; The operational equation for the fluctuation coherence estimator of groups is: In the equation, It is Estimator of the fluctuation coherence of the groups; It is The number of laboratory test values ​​in each group, that is, The number of all laboratory test values ​​in the test value sub-cohort corresponding to all numerical points in each group; is the number of laboratory test values ​​in the laboratory test value cohort; It is The mean of the scales of all test value sub-cohorts in 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 The mean of the L1 norm of all numerical points in a group to the centroid of the group.

6. The adjustment method based on visual optimization according to claim 5, characterized in that: Step 3 specifically includes: facing a random group, registering all laboratory test values ​​of the test value sub-queues in the group as one in the laboratory test value queue, registering the unregistered laboratory test values ​​in the laboratory test value queue as zero, and defining the queue sub-node formed by multiple consecutive test values ​​registered as one in the laboratory test value queue as a test value sub-node queue of the group; extracting multiple test value sub-node queues of the group, if the laboratory test value is registered as one and the adjacent laboratory test values ​​are registered as zero, treating the laboratory test value as a test value sub-node queue of the group.

7. The adjustment method based on visual optimization according to claim 6, characterized in that: Step 3 specifically includes: The operational equation for estimating the fluctuation effect of each group on the target detection value is: In the equation, For the An estimate of the fluctuation effect of each group on the target detection value; It is Estimator of the fluctuation coherence of the groups; is the highest value of the fluctuation coherence estimate of all groups; It is The maximum size of the total detection value sub-node queue of the group; It is The number of element values ​​between the last detection value of the highest-scale detection value sub-node queue of each group and the target detection value; It is The number of detection value sub-section queues for each packet; It is The first group The size of the detection value sub-section queue; It is The mean of the scale of all detection value sub-node queues of the group; It is The standard deviation of the scale of the overall detection value sub-section of the group.

8. The adjustment method based on visual optimization according to claim 7, characterized in that: Step 4 specifically includes: in the laboratory test value queue, taking the target test value as the starting point, constructing a set queue before the target test value, the scale of the set queue is the mean of the scales of all test value sub-node queues of all groups, the set queue is filled with test values ​​of its scale number, and taking the target test value as the starting point, the scale test values ​​of the set queue before the target test value in the laboratory test value queue are filled into the set queue one by one; defining the test value sub-node queue where all laboratory test values ​​contained in the set queue are located as the control sub-node queue; calculating the mean of the estimated amount of fluctuation effect of the groups where all control sub-node queues are 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.

9. The adjustment method based on visual optimization according to claim 8, characterized in that: Step 5 specifically includes: in the laboratory test value queue, if the refresh fluctuation effect estimate of the target test value is higher than the pre-defined estimated critical value If the refresh fluctuation effect estimate of the target detection value is not higher than the pre-defined estimated critical value, no processing is performed; When the target detection value and its previous adjacent detection value are separated, the interval is defined as a separation, and the queue formed by the laboratory detection values ​​between the separation and the previous separation is defined as an attribute queue. The plr_sip method is used to calculate the approximate constant of the attribute queue, and each detection value on the attribute queue is refreshed to the 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 laboratory detection value, and the encoded laboratory detection value is transmitted to the cache of the industrial control computer; then the laboratory detection value transmitted by the new sample is immediately identified and encoded according to the above method; .

10. An adjustment device based on visual optimization, characterized in that: include: The temperature sensor and humidity sensor are arranged on the culture rack in the biological laboratory. The temperature sensor and humidity sensor are connected to the main control chip, which is also connected to the industrial control computer. The temperature sensor and humidity sensor are used to sample the temperature value and humidity value in the biological laboratory respectively and transmit the sampled temperature value and humidity value in the biological laboratory to the main control chip for encoding. The main control chip is used to send the encoded laboratory test value to the cache of the industrial control computer. Then the industrial control computer is used to take out the encoded laboratory test value in the cache and decode it for display. The temperature value or humidity value in the biological laboratory is the laboratory test value. The modules running on the main control chip include: A destination module, which is used to obtain a laboratory test value queue and a destination test value; An estimation module is used to separate the laboratory test value queue into multiple test value sub-queues according to all laboratory test values ​​in the laboratory test value queue; obtain multiple groups and the group centroid of each group according to the test value sub-queues; obtain the fluctuation continuity estimation of each group according to the scale of all test value sub-queues in each group and the group centroid of the group; A scale module, which is used to obtain multiple detection value sub-node queues of each group according to the entire detection value sub-queue in each group; obtain an estimate of the fluctuation effect of each group on the target detection value according to the fluctuation continuity estimate of each group and the scale of the detection value sub-node queue; A refreshing module, which is used to obtain an estimated amount of refresh fluctuation effect of the target detection value according to the estimated amount of fluctuation effect of all groups on the target detection value; The encoding module is used to obtain the attribute queue according to the refresh fluctuation effect estimate of the target detection value; perform encoding according to 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.

Images