Thermostatic control method, system and device applied to freezing workshop

Abstract

The present application relates to the technical field of frozen workshop control, in particular to a constant temperature control method, system and device applied to a frozen workshop. The present application firstly obtains a temperature anomaly coefficient according to local time sequence fluctuation of refrigeration temperature of a refrigeration device, and preliminarily determines a regulation and control instruction in combination with deviation thereof; analyzes a change relationship between a refrigeration temperature difference and a refrigeration power of the refrigeration device to be judged, and a difference in temperature anomaly coefficients corresponding to different refrigeration devices, and screens out a specific refrigeration device, so as to re-determine the regulation and control instruction according to time sequence fluctuation characteristics of the refrigeration temperature, the refrigeration temperature difference and the refrigeration power. The present application firstly analyzes fluctuation change of the refrigeration temperature of the refrigeration device and deviation from a target, and further comparatively analyzes different refrigeration devices to evaluate refrigeration performance, so as to accurately evaluate regulation and control necessity of the refrigeration device and determine the regulation and control instruction for constant temperature regulation and control, thereby avoiding frequent regulation and control of the whole, and improving constant temperature regulation and control effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of frozen workshop control, in particular to a constant temperature control method, system and device applied to a frozen workshop. BACKGROUND

[0002] The frozen workshop is a place for processing or storing perishable food, medicine or other sensitive materials at low temperature. The frozen workshop relies on a refrigeration device to maintain a constant low temperature. However, the transportation of goods or the entry and exit of personnel in the workshop may cause the door of the workshop to be frequently opened, thereby introducing heat. In addition, the operation of the machines and equipment in the workshop also generates heat. When the temperature in the workshop deviates from the set low temperature value for a long time, bacteria and microorganisms may rapidly multiply, thereby affecting the product quality. Therefore, it is crucial to control the constant temperature of the frozen workshop.

[0003] Currently, the constant temperature control of the frozen workshop is usually based on a set temperature threshold. In addition, there are usually multiple refrigeration devices in the frozen workshop for cooperative refrigeration. When the temperature in the workshop is higher than the set value, all refrigeration devices are simultaneously started or the refrigeration power is increased. When the temperature in the workshop is lower than the set value, all refrigeration devices are simultaneously controlled to reduce or stop refrigeration. This may cause all refrigeration devices in the frozen workshop to be frequently regulated and controlled, thereby increasing energy consumption. Even worse, it may cause over-regulation, thereby resulting in poor constant temperature control effect of the frozen workshop. SUMMARY

[0004] In order to solve the technical problem of poor constant temperature control effect of the frozen workshop, the purpose of the present application is to provide a constant temperature control method, system and device applied to a frozen workshop. The technical solution adopted is as follows:

[0005] The constant temperature control method applied to the frozen workshop comprises the following steps:

[0006] Obtaining the refrigeration power, refrigeration temperature and refrigeration temperature difference of each refrigeration device in the frozen workshop at each collection time in a preset historical period at a to-be-regulated time;

[0007] For each refrigeration device, a temperature anomaly coefficient corresponding to the collection time is obtained according to the local time sequence fluctuation characteristics of each refrigeration temperature, and a regulation instruction of the refrigeration device is determined in combination with the deviation characteristics of each refrigeration temperature. The refrigeration device performs constant temperature control based on the regulation instruction. The regulation instruction includes regulation, no regulation and to-be-judged.

[0008] For each refrigeration device to be judged, according to the change relationship between the refrigeration temperature difference and the refrigeration power, and the time sequence fluctuation difference between the temperature anomaly coefficient corresponding to different refrigeration devices, a refrigeration specific coefficient is obtained; based on the refrigeration specific coefficient, a specific refrigeration device is screened out; the control instruction of the non-specific refrigeration device is set to be not controlled; for each specific refrigeration device, according to the time sequence fluctuation characteristics of the refrigeration temperature, the refrigeration temperature difference and the refrigeration power, the control instruction is re-determined.

[0009] Further, the temperature anomaly coefficient acquisition method comprises:

[0010] For each refrigeration temperature, a time analysis window of a preset length is constructed, and a temperature time sequence of the refrigeration temperature in the time analysis window is fitted; the temperature time sequence is decomposed by STL to obtain a trend item and a periodic item;

[0011] According to the change trend of the upper envelope line and the lower envelope line of the periodic item, and the change trend of the trend item, the temperature anomaly coefficient corresponding to the collection time of each refrigeration temperature is obtained.

[0012] Further, the control instruction acquisition method comprises:

[0013] Based on the temperature anomaly coefficient, a first anomaly label is given to each collection time, and based on the deviation of the refrigeration temperature from the preset target temperature, a second anomaly label is given to each collection time;

[0014] The collection time with both the first anomaly label and the second anomaly label is taken as an abnormal time, the collection time without both the first anomaly label and the second anomaly label is taken as a normal time, and the remaining collection time is taken as a fuzzy time;

[0015] The number proportion of the abnormal time, the normal time and the fuzzy time in the preset historical period is calculated respectively; when the number proportion of the abnormal time is greater than the preset proportion, the control instruction is control; when the number proportion of the normal time is greater than the preset proportion, the control instruction is not control; when the number proportion of the fuzzy time is greater than the preset proportion, the control instruction is to be judged; the preset proportion is greater than 0.5.

[0016] Further, the refrigeration specific coefficient acquisition method comprises:

[0017] For each refrigeration device to be judged, based on the linear relationship between the refrigeration temperature difference and the refrigeration power, a refrigeration abnormal parameter is obtained; based on the refrigeration abnormal parameter, the refrigeration device to be judged is classified into a high abnormal device and a low abnormal device;

[0018] For each low abnormal device, a low abnormal specificity parameter is obtained according to the difference in the time sequence fluctuation of the temperature abnormality coefficient corresponding to the low abnormal device and the remaining low abnormal devices within a preset historical period; and the low abnormal specificity parameter and the refrigeration abnormal parameter are fused to obtain a refrigeration specificity coefficient corresponding to the low abnormal device.

[0019] For each high abnormal device, a high abnormal specificity parameter is obtained according to the difference in the time sequence fluctuation of the temperature abnormality coefficient corresponding to the high abnormal device and each low abnormal device within a preset historical period; and the high abnormal specificity parameter and the refrigeration abnormal parameter are fused to obtain a refrigeration specificity coefficient corresponding to the high abnormal device.

[0020] Further, the refrigeration abnormal parameter obtaining method comprises:

[0021] The refrigeration power and the refrigeration temperature difference of the refrigeration device within a preset historical period are linearly fitted to obtain a decisive coefficient in the linear fitting process and a refrigeration power change curve;

[0022] According to the negative correlation mapping result of the decisive coefficient and the change slope of the refrigeration power change curve, and the fitting deviation of the refrigeration power at each collection time, a refrigeration abnormal parameter is obtained.

[0023] Further, the specific refrigeration device obtaining method comprises:

[0024] The refrigeration device to be judged, whose refrigeration specificity coefficient is greater than a preset specificity threshold, is taken as a specific refrigeration device.

[0025] Further, the re-determination of the control instruction comprises:

[0026] The refrigeration temperature, the refrigeration temperature difference and the refrigeration power are all taken as target indexes; and the reference fluctuation range of each target index is determined according to the fluctuation characteristics of each target index of the refrigeration device which is not controlled within a preset historical period according to all control instructions;

[0027] For each specific refrigeration device, a normal parameter is obtained according to the fluctuation deviation of each target index relative to the corresponding reference fluctuation range within a preset historical period; the control instruction of the specific refrigeration device whose normal parameter is greater than a preset normal threshold is set to be not controlled, and the control instruction of the specific refrigeration device whose normal parameter is less than or equal to the preset normal threshold is set to be controlled.

[0028] Further, the normal parameter obtaining method comprises:

[0029] In a preset historical period, a first normal parameter is obtained according to a fluctuation deviation frequency of each target index relative to a corresponding reference fluctuation range, and a second normal parameter is obtained according to a deviation of each target index relative to the corresponding reference fluctuation range and a time sequence change trend of the deviation; the first normal parameter and the second normal parameter are fused to obtain a normal parameter.

[0030] The application relates to a constant temperature control system applied to a refrigeration workshop.

[0031] The refrigeration monitoring module is used for acquiring refrigeration power, refrigeration temperature and refrigeration temperature difference of each refrigeration device in the refrigeration workshop at each collection time in a preset historical period at a time to be controlled.

[0032] The control judgment module is used for acquiring a temperature abnormality coefficient corresponding to a collection time according to a local time sequence fluctuation feature of each refrigeration temperature, and determining a control instruction of the refrigeration device in combination with a deviation feature of each refrigeration temperature, so that the refrigeration device is controlled based on the control instruction; wherein the control instruction comprises control, no control and to-be-judged; for each refrigeration device with the to-be-judged control instruction, a refrigeration specificity coefficient is acquired according to a change relationship between the refrigeration temperature difference and the refrigeration power, and a time sequence fluctuation difference between the temperature abnormality coefficient corresponding to different refrigeration devices; a specific refrigeration device is screened out based on the refrigeration specificity coefficient; the control instruction of a non-specific refrigeration device is set as no control; for each specific refrigeration device, a control instruction is redetermined according to time sequence fluctuation features of the refrigeration temperature, the refrigeration temperature difference and the refrigeration power.

[0033] The application relates to a constant temperature control device applied to a refrigeration workshop, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor realizes the steps of the constant temperature control method applied to the refrigeration workshop when the computer program is executed.

[0034] The application has the following beneficial effects:

[0035] This invention first acquires the cooling power, cooling temperature, and cooling temperature difference relative to the external ambient temperature of each refrigeration unit in the cold storage workshop at each acquisition time within a preset historical period of the time to be controlled, providing a data foundation for subsequent analysis. Then, based on the local temporal fluctuation characteristics of each cooling temperature of each refrigeration unit, the temperature anomaly coefficient at the corresponding acquisition time is obtained, thereby helping to capture early warning information of deterioration in the cooling state and avoiding frequent adjustments due to deviations from a single target. Furthermore, the control command for each refrigeration unit is determined by combining the deviation characteristics of each cooling temperature. For each refrigeration unit whose control command is to be evaluated, the deterioration of cooling efficiency is analyzed based on the relationship between the cooling temperature difference and cooling power, and its outlier performance is assessed by combining the temporal fluctuation differences between its value and the corresponding temperature anomaly coefficients of different refrigeration units, thereby reducing the impact of poor cooling state caused by frequent environmental changes and accurately acquiring the cooling specificity coefficient. Then, based on the cooling specificity coefficient, specific refrigeration units are screened out; the control command for non-specific refrigeration units is set to no control; for each specific refrigeration unit, the control command is re-determined based on the temporal fluctuation characteristics of cooling temperature, cooling temperature difference, and cooling power. This invention is based on the fluctuation of the refrigeration temperature of the refrigeration device and the deviation from the target, avoiding frequent adjustments caused by deviation from a single target. It further compares and analyzes different refrigeration devices to evaluate outlier refrigeration performance, thereby accurately assessing the necessity of refrigeration device adjustment, and then determining the adjustment command for constant temperature control, thus improving the constant temperature control effect. Attached Figure Description

[0036] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a constant temperature control method for use in a cold storage workshop, as provided in one embodiment of the present invention. Detailed Implementation

[0038] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a constant temperature control method, system, and equipment applied in a cold storage workshop according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0040] The following description, in conjunction with the accompanying drawings, details a specific solution for a constant temperature control method, system, and equipment applied in a cold storage workshop provided by the present invention.

[0041] Please see Figure 1 The diagram illustrates a method flowchart for a constant temperature control method applied in a cold storage workshop according to an embodiment of the present invention, specifically including:

[0042] Step S1: Obtain the cooling power, cooling temperature, and cooling temperature difference between each refrigeration unit in the cold storage workshop and the external ambient temperature of the cold storage workshop at each acquisition time within the preset historical time period of the time to be controlled.

[0043] It should be noted that the implementation scenario targeted by the embodiments of the present invention is as follows: the cold storage workshop is a cold storage workshop in which multiple adjustable power refrigeration devices work together to achieve a set target temperature, and the refrigeration devices are controlled by an existing refrigeration control system; the refrigeration devices are distributed in different locations within the cold storage workshop, and the refrigeration air outlet of each refrigeration device is located inside the cold storage workshop.

[0044] This invention analyzes the cooling status of each refrigeration device to determine the control command for each refrigeration device, thereby avoiding malfunctions in the refrigeration control system of the cold storage workshop under complex operating conditions (such as frequent goods entering and leaving or drastic changes in environmental heat load) due to factors such as local temperature fluctuations in the workshop and the decrease in refrigeration efficiency but temporary normal temperature. At the same time, it can individually control the corresponding refrigeration devices in areas with poor refrigeration effect, avoiding over-control caused by the synchronous operation of all refrigeration devices.

[0045] Specifically, when the control command is "0" (no control), the refrigeration device maintains its original refrigeration power and continues to refrigerate (not stops refrigerating); when the control command is "1" (control), the refrigeration control system starts to control the refrigeration power of the refrigeration device to ensure a constant temperature in the cold storage room. The control of the refrigeration power of the refrigeration device based on the refrigeration control system is a well-known technology. The specific control process will not be described in detail in the embodiments of this invention. Only the judgment of the control command will be analyzed and described.

[0046] In one embodiment of the present invention, a refrigeration control system is used to periodically judge and adjust the refrigeration control device in the cold storage workshop; wherein, the cycle can be set to 30 minutes, and the judgment and adjustment are performed once every 30 minutes, or the implementer can adjust the cycle himself; the preset historical period is the historical 30 minutes of the time to be adjusted, corresponding to one cycle.

[0047] It should be noted that the analysis method for judging the control command of the refrigeration device is the same for each time when it needs to be controlled. Here, we will only take any time when it needs to be controlled as an example for analysis and description, and will not repeat it.

[0048] Temperature sensors at the cooling outlet of the refrigeration unit are used to monitor the cooling temperature of the refrigeration unit (characterizing the local temperature of the refrigeration unit within the cold storage workshop, thus helping to assess the cooling status or effect of the refrigeration unit; temperature sensors can also be deployed at the center or multiple points in the area where the refrigeration unit is located within the workshop for data collection); power acquisition devices such as smart meters at the power input of the refrigeration unit are used to monitor the cooling power of the refrigeration unit; and temperature sensors outside the cold storage workshop are used to monitor the ambient temperature outside the workshop, and the cooling temperature difference between the cooling temperature and the ambient temperature outside the cold storage workshop at the same time is calculated (the difference between the ambient temperature outside the workshop and the cooling temperature is used to characterize the heat load that the refrigeration unit is resisting, preparing for subsequent analysis of the cooling status in conjunction with the cooling power).

[0049] In this embodiment, the acquisition frequency of the aforementioned sensors and acquisition devices is set to 1Hz, and synchronous acquisition must be ensured for subsequent analysis. In other embodiments, the implementer may adjust the acquisition frequency according to the actual situation, but the acquired data at different acquisition frequencies must be resampled to ensure timing alignment.

[0050] At the time of adjustment, for each refrigeration unit in the cold storage workshop, the time-series data of refrigeration temperature, refrigeration power, and refrigeration temperature difference collected in the preset historical period are uploaded to the edge server in the cold storage workshop via wired or wireless means for data cleaning and standardization, such as removing duplicate values ​​or filling in missing values. After that, the dimensions of each type of data (refrigeration temperature, refrigeration power, and refrigeration temperature difference) are removed and mapped to a preset range, such as 0-10, for subsequent anomaly analysis. Data cleaning and standardization are well-known technical means and will not be described in detail.

[0051] Step S2: For each refrigeration device, based on the local temporal fluctuation characteristics of each refrigeration temperature, the temperature anomaly coefficient at the corresponding acquisition time is obtained, and combined with the deviation characteristics of each refrigeration temperature, the control command of the refrigeration device is determined. The refrigeration device performs constant temperature control based on the control command; wherein, the control command includes control, no control, and pending judgment.

[0052] It should be noted that the control command determination scheme is consistent for each refrigeration unit. Here, we will only take any one refrigeration unit as an example for analysis and description, and will not go into details again.

[0053] For refrigeration equipment, when its cooling temperature deviates from the set target low temperature value, it indicates that the local cooling effect may be poor, and the refrigeration control system needs to be activated to maintain a constant temperature to restore the target low temperature state. However, the transportation of goods or the entry and exit of personnel in the workshop may cause slight fluctuations in the cooling temperature. Judging based on a single degree of deviation may lead to frequent adjustments to the refrigeration equipment, and there is also a certain lag in the adjustment based on a single degree of deviation.

[0054] Therefore, in this embodiment of the invention, the temperature anomaly coefficient at the corresponding acquisition time is obtained based on the local temporal fluctuation characteristics of each cooling temperature in the refrigeration device within a preset historical period. The temperature anomaly coefficient is based on the temporal change of the cooling temperature and helps to assess whether the cooling temperature is fluctuating drastically or gradually increasing. This helps to capture the precursor information of the deterioration of the cooling state in advance. Furthermore, by combining the deviation characteristics of each cooling temperature, the abnormal situation of the cooling state is comprehensively evaluated to determine the control command of the refrigeration device, so that the refrigeration device can perform constant temperature control based on the control command.

[0055] Preferably, in one embodiment of the present invention, considering that the temporal variation trend of the cooling temperature can help assess whether the cooling temperature is in a continuously rising and deteriorating state, and that the Loess-based seasonal-trend decomposition algorithm (STL) can help separate the trend term, periodic term, and residual term of the time series, the trend term can help assess the temporal variation trend, and the envelope of the periodic term can help assess whether the fluctuation of the cooling temperature is showing a sharp increasing trend, thereby helping to assess temperature anomalies; therefore, the method for obtaining the temperature anomaly coefficient includes:

[0056] For each cooling temperature, a time analysis window of a preset length is constructed, and the temperature time series of the cooling temperature within the time analysis window is fitted. The temperature time series is then decomposed using STL to obtain the trend term and periodic term.

[0057] Based on the changing trends of the upper and lower envelopes of the periodic term, as well as the changing trend of the trend term, the temperature anomaly coefficient corresponding to the acquisition time for each cooling temperature is obtained.

[0058] As an example, firstly, within a preset historical time period, the acquisition time corresponding to each cooling temperature of the refrigeration device is taken as the end point of the window. A time analysis window of a preset length, such as 30, is constructed in the reverse direction of the time sequence. The cooling temperatures within the time analysis window are sorted according to the time sequence to construct a temperature time series. Then, the temperature time series is further decomposed by STL to obtain the trend term and periodic term. STL decomposition is a well-known technique and will not be elaborated further.

[0059] Then, the upper and lower envelopes of the periodic term are obtained. Specifically, autocorrelation analysis can be performed on the periodic term to obtain its period length. The periodic term is then periodically segmented, and the maximum and minimum values ​​within each periodic segment are determined. The least squares fitted curve of the maximum values ​​within all periodic segments is used as the upper envelope. The slope of the upper envelope is calculated based on the two-point formula corresponding to the first and last data points. Similarly, the lower envelope slope is obtained, and the slope of the trend term is calculated. In other embodiments, the implementer can also directly use a temperature time series instead of the periodic term for envelope analysis.

[0060] The difference between the upper envelope slope and the lower envelope slope is further normalized and mapped, for example, onto the ReLU function, to obtain the fluctuation convergence parameter. When the upper envelope slope is less than 0, the lower envelope slope is greater than 0, and the difference is less than 0, it indicates that the fluctuation is gradually converging. Otherwise, it indicates that the fluctuation continues to deteriorate, and the larger the normalized mapping result is.

[0061] Finally, the absolute value of the trend term slope is taken, and then the absolute value of the trend term slope is weighted and fused with the fluctuation convergence parameter. The normalized value of the weighted fusion result is used as the temperature anomaly coefficient corresponding to the collection time of the cooling temperature. In this embodiment, the weights are 0.6 and 0.4 respectively, and the sum of the weights is 1. The implementer can also adjust them as needed.

[0062] The normalization method used is max-min normalization, which is based on the weighted fusion results of all collection times within a preset historical period. Implementers can also use the method of dividing by the maximum weighted fusion result for normalization. Both are existing technologies and will not be elaborated further.

[0063] After determining the temperature anomaly coefficient at each data acquisition moment, the control commands for the refrigeration device can be determined by further combining the deviation characteristics of each refrigeration temperature.

[0064] Preferably, in one embodiment of the present invention, considering that the larger the temperature anomaly coefficient at the time of sampling, and the more synchronously it deviates from the set target low temperature value, it indicates that the temperature may have already shown a poor cooling state at the corresponding sampling time, and the more sampling times of this kind there are, the greater the necessity for cooling regulation; conversely, it indicates that the cooling state in the refrigeration room has not been affected, and cooling regulation is not required; when only the temperature anomaly coefficient is large or only the temperature deviates from the target, further analysis is needed to accurately determine the regulation command; based on this, the method for obtaining the regulation command includes:

[0065] A first anomaly label is assigned to each acquisition moment based on the temperature anomaly coefficient, and a second anomaly label is assigned to each acquisition moment based on the deviation of the cooling temperature from the preset target temperature.

[0066] The acquisition time that simultaneously has the first abnormal label and the second abnormal label is regarded as the abnormal time, the acquisition time that does not simultaneously have the first abnormal label and the second abnormal label is regarded as the normal time, and the remaining acquisition time is regarded as the ambiguous time.

[0067] Calculate the percentage of abnormal moments, normal moments, and ambiguous moments within a preset historical period. When the percentage of abnormal moments is greater than the preset percentage, the control instruction is to control. When the percentage of normal moments is greater than the preset percentage, the control instruction is not to control. When the percentage of ambiguous moments is greater than the preset percentage, the control instruction is to be determined. The preset percentage is greater than 0.5.

[0068] As an example, firstly, a temperature anomaly threshold is set, for example, 0.3, and a first anomaly label is assigned to the collection time when the temperature anomaly coefficient is greater than 0.3; then, a preset target temperature fluctuation range is set, for example, the fluctuation does not exceed 5%, and when the cooling temperature exceeds the preset target temperature fluctuation range, a second anomaly label is assigned to the corresponding collection time.

[0069] Further, all abnormal moments, all normal moments, and all ambiguous moments are determined within a preset historical time period; ambiguous moments are those when the cooling status cannot be accurately assessed; then, the proportion of each type of moment within the preset historical time period is calculated; the preset proportion is greater than 0.5, and in this embodiment it is 0.8, but the implementer can also adjust it as needed.

[0070] When the proportion of abnormal moments is greater than 0.8, it is considered that the cooling status of the refrigeration device is mainly poor or deteriorating within the preset historical period, and the cooling temperature begins to fluctuate drastically and deviates from the set target low temperature value. It is necessary to intervene in the refrigeration device regulation. Therefore, the regulation command at the moment to be regulated is set to regulation. Similarly, when the proportion of normal moments is greater than 0.8, it can be considered that the cooling status is good and regulation is not allowed for the time being. Therefore, the regulation command is no regulation. When the proportion of ambiguous moments is greater than 0.8, it is impossible to accurately determine the current cooling status. Further analysis is required. Therefore, the regulation command is to be evaluated. The regulation command will be re-analyzed and determined later.

[0071] It should be noted that the refrigeration control system in the cold storage workshop adjusts the refrigeration power of each refrigeration unit based on the control instructions of each refrigeration unit, thereby achieving constant temperature control; the specific constant temperature control process is existing technology and can be achieved through the built-in logic instructions of the existing refrigeration control system, and will not be elaborated further.

[0072] Step S3: For each refrigeration device whose control command is to be analyzed, obtain the refrigeration specificity coefficient based on the relationship between the refrigeration temperature difference and the refrigeration power, and the time-series fluctuation difference between the coefficient and the corresponding temperature anomaly coefficient of different refrigeration devices; screen out specific refrigeration devices based on the refrigeration specificity coefficient; set the control command of non-specific refrigeration devices to no control; for each specific refrigeration device, redetermine the control command based on the time-series fluctuation characteristics of the refrigeration temperature, refrigeration temperature difference, and refrigeration power.

[0073] It should be noted that the control command for each refrigeration device to be analyzed is consistent with the control command for the device to be analyzed. Here, we will only take any one refrigeration device to be analyzed as an example for analysis and description, and will not go into details.

[0074] Considering that the larger the refrigeration temperature difference, the greater the heat load that the refrigeration device needs to overcome during heat exchange refrigeration, its refrigeration power should also be relatively larger to meet the refrigeration demand. Conversely, when the refrigeration temperature difference is smaller, the refrigeration power is relatively larger, or when the refrigeration temperature difference is larger, the refrigeration power is smaller, indicating that the refrigeration efficiency of the refrigeration device to be analyzed is more abnormal, and its regulation is more necessary.

[0075] Furthermore, considering that when the time-series variation characteristics of the temperature anomaly coefficient of the refrigeration unit to be analyzed are similar to those of the time-series variation characteristics of the temperature anomaly coefficient of other refrigeration units, it indicates that the temperature fluctuation may be caused by environmental changes such as frequent opening and closing of workshop doors, and the possibility of abnormal refrigeration efficiency is relatively low. When such situations decrease, the low temperature state of the workshop may gradually recover, and the necessity of its regulation is relatively smaller.

[0076] Based on this, the embodiments of the present invention obtain the refrigeration specificity coefficient of the refrigeration device to be analyzed by considering the relationship between the refrigeration temperature difference and refrigeration power of the refrigeration device to be analyzed within a preset historical period, as well as the temporal fluctuation difference between the refrigeration specificity coefficient and the temperature anomaly coefficient of different refrigeration devices. The refrigeration specificity coefficient is used to characterize the relative anomaly of the refrigeration efficiency of the refrigeration device to be analyzed, in order to prepare for the subsequent re-determination of control commands.

[0077] Preferably, in one embodiment of the present invention, considering the above analysis, the larger the refrigeration temperature difference, the greater the heat load that the refrigeration device needs to overcome during heat exchange refrigeration, and its refrigeration power should also be relatively greater to meet the refrigeration demand. Ideally, there is a certain linear relationship between the refrigeration temperature difference and the refrigeration power. If this linear relationship is not met, it indicates that the refrigeration efficiency is relatively abnormal. Therefore, refrigeration devices with different degrees of abnormality can be distinguished based on the refrigeration anomaly. Furthermore, by combining the similarity of the time-series changes in the temperature anomaly coefficient, the specificity or outlier behavior of the refrigeration device can be evaluated, and a refrigeration specificity coefficient can be obtained. The method for obtaining the refrigeration specificity coefficient includes:

[0078] For each refrigeration device to be assessed, refrigeration anomaly parameters are obtained based on the linear relationship between refrigeration temperature difference and refrigeration power; based on the refrigeration anomaly parameters, the refrigeration devices to be assessed are classified into high-anomaly devices and low-anomaly devices.

[0079] For each low-abnormal device, based on the temperature anomalous coefficients of the low-abnormal device and other low-abnormal devices, and the temporal fluctuation differences within a preset historical period, a low-abnormality specific parameter is obtained; the low-abnormality specific parameter and the cooling anomalous parameter are fused to obtain the cooling specific coefficient of the corresponding low-abnormal device.

[0080] For each high-anomaly device, based on the temperature anomaly coefficients corresponding to the high-anomaly device and each low-anomaly device, and the temporal fluctuation differences within a preset historical period, high-anomaly specific parameters are obtained; the high-anomaly specific parameters and cooling anomaly parameters are fused to obtain the cooling specific coefficient of the corresponding high-anomaly device.

[0081] In a preferred embodiment of the present invention, considering that the coefficient of determination can help evaluate the goodness of fit of the linear fitting model, the cooling power and cooling temperature difference of the (to be assessed) refrigeration device within a preset historical period can be linearly fitted to obtain a cooling power change curve, thereby helping to evaluate the linear relationship. The larger the cooling power change curve, the greater the cooling power required to eliminate the unit temperature difference, and the lower the cooling efficiency. The coefficient of determination r can help evaluate the linear relationship. When r is larger and closer to 1, it indicates that the working state of the refrigeration device is stable and the possibility of abnormality is smaller. Conversely, when r is smaller and closer to 0, the cooling state is more abnormal. Therefore, the refrigeration abnormality parameters can be evaluated by combining the coefficient of determination and the slope of the cooling power change curve. The method for obtaining the refrigeration abnormality parameters includes:

[0082] Linear fitting is performed on the cooling power and cooling temperature difference of the refrigeration device within a preset historical period to obtain the coefficient of determination and the cooling power change curve during the linear fitting process.

[0083] Based on the negative correlation mapping result of the determination coefficient and the slope of the cooling power change curve, as well as the fitting deviation of the cooling power at each acquisition time, the cooling anomaly parameters are obtained.

[0084] As an example, first consider the temperature difference in cooling. As the independent variable, cooling power As the dependent variable, a linear fit is then performed, and the linear fit function is denoted as... ,in The linear fitting relationship is characterized to obtain the coefficient of determination and the cooling power change curve; linear fitting and obtaining the coefficient of determination are well-known techniques and will not be elaborated further.

[0085] Then, the coefficient of determination r is negatively correlated and normalized, specifically by mapping it to 1-x, where x is the independent variable, and the negative correlation normalization result is 1-r. Then, the slope kf of the cooling power change curve (also the linear fitting slope) is calculated based on the two-point formula, and the slope is mapped to the sigmoid function to normalize and adjust the range.

[0086] The normalized mapping result of the slope kf is further fused with the negative correlation normalized result 1-r of the determinant coefficient, such as by multiplication, and the product is used as the first anomaly index. The fitting error of the first cooling power is subtracted from the fitting error of the last cooling power in the cooling power change curve to measure the change of the fitting error. The difference is mapped to the normalized adjustment range of the sigmoid function to obtain the second anomaly index. Finally, the first anomaly index and the second anomaly index are fused, such as by multiplication, to obtain the cooling anomaly parameters.

[0087] After obtaining the refrigeration anomaly parameters of each refrigeration device to be assessed, the refrigeration devices to be assessed can be further classified into high-anomaly devices and low-anomaly devices based on the refrigeration anomaly parameters.

[0088] In a preferred embodiment of the present invention, a refrigeration device to be analyzed whose refrigeration specificity coefficient is greater than a preset specificity threshold is regarded as a specific refrigeration device; wherein, in this embodiment, the preset specificity threshold is the average value of the refrigeration abnormality parameters of all refrigeration devices to be analyzed, and the implementer may also adjust it himself.

[0089] In other embodiments, the implementer may also cluster the refrigeration devices to be assessed into two clusters based on clustering. The cluster with the larger refrigeration anomaly parameter at the center of the cluster is the cluster corresponding to the high-anomaly devices, and the other cluster is the low-anomaly devices. This will not be elaborated further.

[0090] Furthermore, the temperature anomaly coefficients of each low-anomaly device within a preset historical period are sorted in time to construct a time series. Then, the sequence difference between the time series of temperature anomaly coefficients of each low-anomaly device i and the other (non-i) low-anomaly devices is calculated. The mean of the negative correlation normalization results of all sequence differences is used as the low-anomaly specific parameter. Finally, the low-anomaly specific parameter is multiplied and fused with the refrigeration anomaly parameter to obtain the refrigeration specific coefficient of the corresponding low-anomaly device i.

[0091] Similarly, the refrigeration specificity coefficient of each high-anomaly device can be calculated.

[0092] Specifically, sequence differences can be represented by DTW distance, Euclidean norm, or Pearson correlation coefficient; negative correlation normalization can be achieved by mapping to an exponential function exp(-x) with the natural constant e as the base, or by first performing linear normalization and then performing negative correlation normalization by subtracting the linear normalization value from the constant 1; these are all well-known techniques and will not be elaborated further.

[0093] After obtaining the refrigeration specificity coefficient of each refrigeration device to be evaluated, specific refrigeration devices can be further screened based on the refrigeration specificity coefficient.

[0094] Specifically, the average value of the refrigeration specificity coefficient of all refrigeration devices to be evaluated is used as the screening threshold. Refrigeration devices to be evaluated that are greater than the screening threshold are considered as specific refrigeration devices; the remaining refrigeration devices to be evaluated are considered as non-specific refrigeration devices.

[0095] Specialized refrigeration devices are those whose refrigeration efficiency declines and whose abnormal temperature changes are more atypical and unique compared to other refrigeration devices. Therefore, the need for regulation is relatively greater for these devices. Thus, the regulation command for non-specialized refrigeration devices is set to no regulation.

[0096] For each specialized refrigeration device, the control commands are further determined based on the temporal fluctuation characteristics of the refrigeration temperature, refrigeration temperature difference, and refrigeration power.

[0097] Preferably, in one embodiment of the present invention, considering the relative fluctuations in the cooling temperature, cooling temperature difference, or cooling power of the integrated special cooling device can help assess the deterioration trend of the cooling state, thereby helping to redetermine the control command; while a cooling device with a relatively normal cooling state, i.e., a control command of no control, can provide a normal fluctuation reference, thereby helping to assess the relative fluctuation deviation; based on this, redetermining the control command includes:

[0098] Cooling temperature, cooling temperature difference, and cooling power are all taken as target indicators; based on the fluctuation characteristics of each target indicator of the refrigeration device with all control commands set to non-control within a preset historical period, the reference fluctuation range of each target indicator is determined.

[0099] For each special refrigeration device, normal parameters are obtained based on the fluctuation deviation of each target indicator relative to the corresponding reference fluctuation range within a preset historical period. The control command of special refrigeration devices with normal parameters greater than the preset normal threshold is set to no control, and the control command of special refrigeration devices with normal parameters less than or equal to the preset normal threshold is set to control.

[0100] As an example, taking any special refrigeration device as an example, in order to facilitate the analysis of the deterioration of the refrigeration state of the special refrigeration device from the perspectives of refrigeration temperature, refrigeration temperature difference and refrigeration power, we first take refrigeration temperature, refrigeration temperature difference and refrigeration power as target indicators; then we screen out all refrigeration devices with no control command as reference devices, and then determine the reference fluctuation range of each target indicator in turn.

[0101] Taking the cooling temperature difference as an example, at each acquisition time within a preset historical period, the range corresponding to the extreme values ​​of the cooling temperature differences of all reference devices is taken as the reference fluctuation range of the cooling temperature difference. Alternatively, the implementer can take the average value of the cooling temperature differences of all reference devices as the temperature difference benchmark dT, calculate the standard deviation of the cooling temperature difference as the fluctuation value a, and adjust the temperature difference benchmark up and down by the fluctuation value to obtain the reference fluctuation range [dT-a, dT+a]. Similarly, the reference fluctuation range of cooling temperature and cooling power at each acquisition time is obtained.

[0102] In a preferred embodiment of the present invention, considering that for each target indicator, the lower the frequency of the target indicator data deviating from the reference fluctuation range, the smaller the fluctuation deviation, or the decreasing trend, it indicates that it is more similar to the cooling state of the reference device, and the greater the possibility that the cooling state is normal; the method for obtaining the normal parameters includes:

[0103] Within a preset historical period, a first normal parameter is obtained based on the frequency of fluctuation deviation of each target indicator relative to the corresponding reference fluctuation range, and a second normal parameter is obtained based on the deviation of each target indicator relative to the corresponding reference fluctuation range and the temporal trend of the deviation. The first normal parameter and the second normal parameter are then combined to obtain the normal parameter.

[0104] As an example, within a preset historical period, for each target indicator, the frequency of fluctuation deviation of the target indicator relative to the corresponding reference fluctuation range is first counted, and the fluctuation deviation of the target indicator relative to the corresponding reference fluctuation range at each collection time is further calculated. The temporal change slope of the fluctuation deviation is then calculated based on a two-point method.

[0105] Specifically, when the target indicator is lower than the lower limit of the reference fluctuation range, the fluctuation deviation is the absolute value of the difference between the target indicator and the lower limit; when the target indicator is higher than the upper limit of the reference fluctuation range, the fluctuation deviation is the absolute value of the difference between the target indicator and the upper limit; when the target indicator is within the reference fluctuation range, the fluctuation deviation is 0.

[0106] Then, the frequency of fluctuation deviation, the slope of time series change, and the mean of fluctuation deviation within a preset historical period are negatively correlated and normalized respectively. The negative correlation normalization results of the three are then fused, such as by multiplication, and the product is used as the normal sub-parameter of each target indicator. The normal sub-parameters of the three target indicators are then averaged to obtain the normal parameters.

[0107] Among them, negative correlation normalization is performed by mapping the fluctuation deviation frequency to 1-x, negative correlation normalization is performed by mapping the time series change slope to the exponential function exp(-x) with the natural constant e as the base, and negative correlation normalization is performed by mapping the mean of the fluctuation deviation within the preset historical period to the exponential function exp(-x) with the natural constant e as the base; implementers may also use other negative correlation normalization methods according to the actual situation, which will not be elaborated further.

[0108] Ultimately, the preset normal threshold is set to 0.9, which can also be adjusted by the implementer; the control command for special refrigeration devices with normal parameters greater than the preset normal threshold is set to no control, and the control command for special refrigeration devices with normal parameters less than or equal to the preset normal threshold is set to control; that is, the redefined control command only includes control and no control.

[0109] Based on the same inventive concept, one embodiment of the present invention also proposes a constant temperature control system for use in a cold storage workshop, the system including a cold storage monitoring module 101 and a control judgment module 102.

[0110] Refrigeration monitoring module 101: used to acquire the refrigeration power of each refrigeration unit in the refrigeration workshop at each acquisition time, the refrigeration temperature of the corresponding refrigeration area, and the refrigeration temperature difference between the refrigeration temperature and the external ambient temperature of the refrigeration workshop within a preset historical period of time to be controlled.

[0111] The control judgment module 102 is used to, for each refrigeration device, obtain the temperature anomaly coefficient at the corresponding acquisition time based on the local temporal fluctuation characteristics of each refrigeration temperature, and determine the control command of the refrigeration device in combination with the deviation characteristics of each refrigeration temperature. The refrigeration device performs constant temperature control based on the control command. The control command includes control, no control, and pending judgment. For each refrigeration device whose control command is pending judgment, the refrigeration specificity coefficient is obtained based on the relationship between the refrigeration temperature difference and the refrigeration power, and the temporal fluctuation difference between it and the corresponding temperature anomaly coefficient of different refrigeration devices. Specific refrigeration devices are screened based on the refrigeration specificity coefficient. The control command of non-specific refrigeration devices is set to no control. For each specific refrigeration device, the control command is re-determined based on the temporal fluctuation characteristics of the refrigeration temperature, refrigeration temperature difference, and refrigeration power.

[0112] The freezing monitoring module 101 is used to perform the above step S1, and the regulation and judgment module 102 is used to perform the above steps S2-S3, which will not be described in detail.

[0113] Based on the same inventive concept, one embodiment of the present invention also proposes a constant temperature control device for use in a cold storage workshop. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the constant temperature control method for use in a cold storage workshop described in steps S1-S3 above.

[0114] In summary, this invention first obtains the refrigeration power, refrigeration temperature, and refrigeration temperature difference relative to the external ambient temperature of each refrigeration unit in the cold storage workshop at each sampling time within a preset historical period of the time to be regulated. Then, based on the local temporal fluctuation characteristics of each refrigeration temperature of each refrigeration unit, the temperature anomaly coefficient at the corresponding sampling time is obtained. Combined with the deviation characteristics of each refrigeration temperature, the regulation command for each refrigeration unit is determined, and the refrigeration unit performs constant temperature control based on the regulation command. Specifically, for each refrigeration unit whose regulation command is to be evaluated, a refrigeration specificity coefficient is obtained based on the relationship between the refrigeration temperature difference and the refrigeration power, and the temporal fluctuation difference between it and the corresponding temperature anomaly coefficient of different refrigeration units. Specific refrigeration units are screened based on the refrigeration specificity coefficient. The regulation command for non-specific refrigeration units is set to no regulation. For each specific refrigeration unit, the regulation command is re-determined based on the temporal fluctuation characteristics of the refrigeration temperature, refrigeration temperature difference, and refrigeration power. This invention is based on the fluctuation of the refrigeration temperature of the refrigeration device and the deviation from the target, avoiding frequent adjustments caused by deviation from a single target. It further compares and analyzes different refrigeration devices to evaluate outlier refrigeration performance, thereby accurately assessing the necessity of refrigeration device adjustment, and then determining the adjustment command for constant temperature control, thus improving the constant temperature control effect.

[0115] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A constant temperature control method applied to cold storage workshops, characterized in that, The method includes: Within a preset historical time period for the time to be controlled, the refrigeration power, refrigeration temperature, and refrigeration temperature difference between the refrigeration temperature and the external ambient temperature of each refrigeration unit in the cold storage workshop at each acquisition time are obtained. For each refrigeration device, based on the local temporal fluctuation characteristics of each refrigeration temperature, the temperature anomaly coefficient at the corresponding acquisition time is obtained, and combined with the deviation characteristics of each refrigeration temperature, the control command of the refrigeration device is determined. The refrigeration device performs constant temperature control based on the control command; wherein, the control command includes control, no control, and pending judgment. For each refrigeration device whose control command is to be evaluated, a refrigeration specificity coefficient is obtained based on the relationship between the refrigeration temperature difference and the refrigeration power, and the temporal fluctuation difference between the coefficient of temperature anomaly corresponding to different refrigeration devices; specific refrigeration devices are screened based on the refrigeration specificity coefficient; the control command of non-specific refrigeration devices is set to no control; for each specific refrigeration device, the control command is re-determined based on the temporal fluctuation characteristics of the refrigeration temperature, the refrigeration temperature difference, and the refrigeration power. The method for obtaining the temperature anomaly coefficient includes: For each of the cooling temperatures, a time analysis window of a preset length is constructed, and the temperature time series of the cooling temperatures within the time analysis window is fitted. The temperature time series is then decomposed using STL to obtain the trend term and the period term. Based on the changing trends of the upper and lower envelopes of the periodic term and the changing trend of the trend term, the temperature anomaly coefficient corresponding to each cooling temperature at the acquisition time is obtained.

2. The constant temperature control method for cold storage workshops according to claim 1, characterized in that, The method for obtaining the control command includes: A first anomaly label is assigned to each acquisition moment based on the temperature anomaly coefficient, and a second anomaly label is assigned to each acquisition moment based on the deviation of the cooling temperature from the preset target temperature. The acquisition time that simultaneously has the first abnormal label and the second abnormal label is regarded as the abnormal time, the acquisition time that does not simultaneously have the first abnormal label and the second abnormal label is regarded as the normal time, and the remaining acquisition time is regarded as the ambiguous time. Calculate the percentage of abnormal moments, normal moments, and ambiguous moments within a preset historical period. When the percentage of abnormal moments is greater than the preset percentage, the control instruction is to control. When the percentage of normal moments is greater than the preset percentage, the control instruction is not to control. When the percentage of ambiguous moments is greater than the preset percentage, the control instruction is to be determined. The preset percentage is greater than 0.

5.

3. The constant temperature control method for cold storage workshops according to claim 1, characterized in that, The method for obtaining the refrigeration specificity coefficient includes: For each refrigeration device to be assessed, refrigeration anomaly parameters are obtained based on the linear relationship between the refrigeration temperature difference and the refrigeration power; based on the refrigeration anomaly parameters, the refrigeration devices to be assessed are classified into high-anomaly devices and low-anomaly devices. For each low-abnormal device, based on the temperature anomalous coefficient corresponding to the low-abnormal device and other low-abnormal devices, and the temporal fluctuation difference within a preset historical period, a low-abnormality specific parameter is obtained; the low-abnormality specific parameter and the cooling anomalous parameter are fused to obtain the cooling specific coefficient of the corresponding low-abnormal device. For each high-anomaly device, based on the temperature anomaly coefficient corresponding to the high-anomaly device and each low-anomaly device, and the temporal fluctuation difference within a preset historical period, a high-anomaly specific parameter is obtained; the high-anomaly specific parameter and the cooling anomaly parameter are fused to obtain the cooling specific coefficient of the corresponding high-anomaly device.

4. The constant temperature control method for cold storage workshops according to claim 3, characterized in that, The method for obtaining the refrigeration anomaly parameters includes: The refrigeration power and the refrigeration temperature difference of the refrigeration device are linearly fitted within a preset historical period to obtain the coefficient of determination and the refrigeration power change curve during the linear fitting process. Based on the negative correlation mapping result of the determination coefficient, the slope of the cooling power change curve, and the fitting deviation of the cooling power at each acquisition time, the cooling anomaly parameters are obtained.

5. The constant temperature control method for cold storage workshops according to claim 1, characterized in that, The method for obtaining the special cooling device includes: The refrigeration device to be analyzed whose refrigeration specificity coefficient is greater than the preset specificity threshold is regarded as a specific refrigeration device.

6. The constant temperature control method for cold storage workshops according to claim 1, characterized in that, The re-determination of control directives includes: Cooling temperature, cooling temperature difference, and cooling power are all taken as target indicators; based on the fluctuation characteristics of each target indicator of the refrigeration device with all control commands set to non-control within a preset historical period, the reference fluctuation range of each target indicator is determined. For each special refrigeration device, normal parameters are obtained based on the fluctuation deviation of each target indicator relative to the corresponding reference fluctuation range within a preset historical period. The control command of special refrigeration devices with normal parameters greater than the preset normal threshold is set to no control, and the control command of special refrigeration devices with normal parameters less than or equal to the preset normal threshold is set to control.

7. The constant temperature control method for cold storage workshops according to claim 6, characterized in that, The methods for obtaining the normal parameters include: Within a preset historical period, a first normal parameter is obtained based on the fluctuation deviation frequency of each target indicator relative to the corresponding reference fluctuation range, and a second normal parameter is obtained based on the deviation of each target indicator relative to the corresponding reference fluctuation range and the temporal trend of the deviation; the first normal parameter and the second normal parameter are then combined to obtain a normal parameter.

8. A constant temperature control system for a cold storage workshop, used to implement the constant temperature control method for a cold storage workshop as described in any one of claims 1 to 7, characterized in that, The system includes: The freezing monitoring module is used to acquire the refrigeration power, refrigeration temperature, and refrigeration temperature difference between the refrigeration temperature and the external ambient temperature of each refrigeration unit in the freezing workshop at each acquisition time within a preset historical period of the time to be controlled. The regulation and judgment module is used to, for each refrigeration device, obtain the temperature anomaly coefficient at the corresponding acquisition time based on the local temporal fluctuation characteristics of each refrigeration temperature, and determine the regulation command of the refrigeration device in combination with the deviation characteristics of each refrigeration temperature. The refrigeration device performs constant temperature control based on the regulation command. The regulation command includes regulation, no regulation, and pending judgment. For each refrigeration device whose regulation command is pending judgment, a refrigeration specificity coefficient is obtained based on the relationship between the refrigeration temperature difference and the refrigeration power, and the temporal fluctuation difference between it and the temperature anomaly coefficient corresponding to different refrigeration devices. Specific refrigeration devices are screened based on the refrigeration specificity coefficient. The regulation command of non-specific refrigeration devices is set to no regulation. For each specific refrigeration device, the regulation command is re-determined based on the temporal fluctuation characteristics of the refrigeration temperature, the refrigeration temperature difference, and the refrigeration power.

9. A temperature control device for use in a cold storage workshop, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the constant temperature control method for cold storage workshops as described in any one of claims 1 to 7.

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