PLC-based automatic temperature control system

Abstract

This invention discloses a PLC-based automatic temperature control system, belonging to the field of temperature control. The system includes: a first prediction module, a temperature prediction accuracy calculation module, a second temperature prediction value acquisition module for the next moment, and a corrected temperature prediction calculation module. Based on the accuracy of the temperature prediction and the second temperature prediction value for the next moment, the module corrects the sensor temperature prediction values ​​to obtain corrected temperature prediction values. A control module inputs the corrected temperature prediction values ​​into the PLC control system, which then regulates the temperature. Thus, by determining the importance of each sensor and the correlation between sensors based on historical and predicted temperatures, the system corrects the predicted temperature obtained using the moving average method, improving the accuracy of temperature prediction values ​​and achieving accurate temperature control within the shielded equipment room, ensuring the temperature stability of each area within the shielded equipment room.

Description

Technical Field

[0001] This invention relates to the field of air conditioning technology, and more particularly to a PLC-based automatic temperature control system. Background Technology

[0002] In this era of rapid technological advancement, data centers have become a core component of critical infrastructure such as data centers and cloud computing in large enterprises. The equipment and systems within data centers need to maintain stable operation, but data centers are threatened by many uncontrollable factors, including temperature variations, humidity fluctuations, dust, static electricity, and power outages.

[0003] To protect equipment and systems in a computer room from temperature fluctuations, a PLC-based automatic temperature control system is needed, primarily for automatic temperature control within the computer room. Currently, methods typically rely on prediction to forecast the temperature at the next moment. Based on the forecast, control commands are sent to the PLC control system, which then controls the air conditioning according to the predicted temperature, thereby controlling the temperature within the shielded computer room. However, temperature prediction is usually based on historical data, making it difficult to predict future temperature fluctuations, resulting in inaccurate predictions and consequently, inaccurate temperature control by the PLC control system. Summary of the Invention

[0004] This invention provides a PLC-based automatic temperature control system, which aims to improve the accuracy of temperature prediction values, thereby achieving accurate temperature control within the shielded room and ensuring the stability of temperature in various areas of the shielded room.

[0005] To achieve the above objectives, the present invention provides a PLC-based automatic temperature control system, the PLC-based automatic temperature control system comprising:

[0006] The first prediction module is used to predict the historical sensor temperature and historical air conditioning temperature in the shielded room using the moving average method, and obtain the predicted values ​​of sensor temperature and air conditioning temperature respectively.

[0007] The temperature prediction accuracy calculation module is used to determine the temperature prediction accuracy correction factor based on the correlation between each group of temperature sensors, and to calculate the temperature prediction accuracy based on the temperature prediction accuracy correction factor and the importance of each temperature sensor; wherein, the importance is determined by the sensor temperature prediction value and the air conditioning temperature prediction value.

[0008] The module for obtaining the second temperature prediction value at the next moment is used to determine the second temperature prediction value at the next moment based on the first temperature prediction value at the next moment from the temperature sensor and the sensor correlation weight.

[0009] The corrected temperature prediction calculation module is used to correct the sensor temperature prediction value based on the accuracy of the temperature prediction value and the second temperature prediction value at the next moment, so as to obtain the corrected temperature prediction value.

[0010] The control module is used to generate analog signals from the corrected temperature prediction values ​​of each temperature sensor, and to control the air conditioning temperature using a PLC control system based on the analog signals.

[0011] Optionally, the first prediction module includes:

[0012] The historical temperature acquisition unit is used to acquire the historical sensor temperatures collected by temperature sensors installed in various areas of the shielded equipment room at preset time intervals, and to acquire the historical air conditioning temperature K at the corresponding time.

[0013] The sensor temperature prediction unit is used to obtain the predicted sensor temperature value of each sensor based on the historical sensor temperatures collected by each sensor and by using the moving average method.

[0014] The air conditioning temperature prediction unit is used to obtain the predicted air conditioning temperature value based on the historical air conditioning temperature at various times using the moving average method.

[0015] Optionally, the module for determining the importance of each temperature sensor includes:

[0016] The historical temperature correlation calculation unit is used to determine the historical temperature correlation between the historical sensor temperature and the historical air conditioning temperature of each temperature sensor based on the difference between the historical sensor temperature and the historical air conditioning temperature at the same time.

[0017] The first importance calculation unit is used to calculate the first importance of each temperature sensor based on historical sensor temperature, historical air conditioning temperature, and historical temperature correlation.

[0018] The sensor temperature prediction correlation calculation unit is used to determine the sensor temperature prediction correlation between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time based on the difference between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time.

[0019] The second importance calculation unit is used to calculate the second importance of each temperature sensor based on the sensor temperature prediction value, the air conditioning temperature prediction value, and the correlation of the sensor temperature prediction values.

[0020] Optionally, the correlation between each group of temperature sensors includes a first correlation and a second correlation; the module for obtaining the correlation between the groups of temperature sensors includes:

[0021] The first correlation calculation unit is used to determine the first correlation relationship of each group of temperature sensors based on the difference of the historical sensor temperature of each group of temperature sensors at the same time and the difference of the historical sensor temperature average value of the corresponding group of temperature sensors. Each group of temperature sensors includes two temperature sensors.

[0022] The second correlation calculation unit is used to determine the second correlation relationship of each group of temperature sensors based on the difference in the predicted sensor temperature values ​​of each group of temperature sensors at the same time and the difference in the average value of the predicted sensor temperature values ​​of the corresponding group of temperature sensors.

[0023] Optionally, the temperature prediction accuracy calculation module includes:

[0024] The accuracy correction factor calculation unit is used to determine the first absolute value of the difference between the second correlation relationship and the first correlation relationship between each group of temperature sensors, sum the first absolute values ​​of each group of temperature sensors, and determine the first normalized value of the summation result as the accuracy correction factor of the temperature prediction value.

[0025] The first product acquisition unit is used to determine the second absolute value of the difference between the second importance and the first importance of each temperature sensor, sum all the obtained absolute values, and calculate the second normalized value of the summation result and the first product of the accuracy correction factor of the temperature prediction value.

[0026] The temperature prediction accuracy calculation unit is used to determine the temperature prediction accuracy by the difference between the constant 1 and the first product.

[0027] Optionally, the module for obtaining the predicted value of the first temperature at the next moment includes:

[0028] The unit for obtaining the first temperature prediction value at the next moment is used to obtain the first temperature prediction value at the next moment of the temperature sensor based on the current temperature of the temperature sensor, the temperature at the previous moment, and the average of adjacent temperatures.

[0029] Optionally, the unit for obtaining the first temperature prediction value at the next moment includes:

[0030] The first difference calculation subunit is used to calculate the first difference between the current temperature and the previous temperature of the temperature sensor.

[0031] The second difference mean calculation subunit is used to obtain the historical sensor temperature at each previous time and the corresponding second difference value at the next adjacent time, and calculate the second difference mean value based on each second difference value.

[0032] The subunit for calculating the first temperature prediction value at the next moment is used to determine the sum of the current temperature, the first difference, and the average of the second difference as the first temperature prediction value at the next moment for the corresponding temperature sensor.

[0033] Optionally, the corrected temperature prediction calculation module includes:

[0034] The sensor correlation weight determination unit is used to determine the temperature correlation of each group of temperature sensors based on the reliability of the correlation relationship and the first correlation relationship, and to determine the sensor correlation weight of each group of temperature sensors.

[0035] Optionally, the corrected temperature prediction calculation model further includes:

[0036] The correlation reliability acquisition unit is used to determine the correlation reliability based on the first correlation relationship and the mean of the first correlation relationship of each group of temperature sensors at each time.

[0037] Optionally, the corrected temperature prediction calculation module includes:

[0038] The first product acquisition unit is used to acquire the first product of the accuracy of the temperature prediction value and the sensor temperature prediction value;

[0039] The second product acquisition unit is used to calculate the difference between the constant 1 and the accuracy of the temperature prediction value, and to obtain the second product of the difference and the second temperature prediction value at the next moment.

[0040] The corrected temperature prediction value determination unit is used to determine the sum of the first product and the second product as the corrected temperature prediction value.

[0041] Compared to existing technologies, this invention proposes a PLC-based automatic temperature control system, comprising: a first prediction module, used to predict historical sensor temperatures and historical air conditioning temperatures in a shielded equipment room using a moving average method, obtaining predicted sensor temperatures and predicted air conditioning temperatures respectively; a temperature prediction accuracy calculation module, used to determine a temperature prediction accuracy correction factor based on the correlation between various groups of temperature sensors, and calculate the accuracy of the temperature prediction based on the temperature prediction accuracy correction factor and the importance of each temperature sensor; a second temperature prediction value acquisition module for the next moment, used to determine the second temperature prediction value for the next moment based on the first temperature prediction value of the temperature sensor for the next moment and the sensor correlation weight; a corrected temperature prediction calculation module, used to correct the sensor temperature prediction values ​​based on the accuracy of the temperature prediction and the second temperature prediction value for the next moment, obtaining a corrected temperature prediction value; and a control module, used to generate analog signals from the corrected temperature prediction values ​​of each temperature sensor, and control the air conditioning temperature using the PLC control system based on the analog signals. In this way, the importance of each sensor and the correlation between sensors are determined based on historical and predicted temperatures. Then, the predicted temperature obtained based on the moving average method is corrected. The temperature in the shielded room is controlled based on the corrected temperature prediction value, which improves the accuracy of the temperature prediction value, realizes accurate temperature control in the shielded room, and ensures the temperature stability of each area in the shielded room. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention;

[0043] Figure 2 This is a first detailed schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention;

[0044] Figure 3 This is a second detailed schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention;

[0045] Figure 4 This is another schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention.

[0046] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0048] Please refer to Figure 1 , Figure 1This is a schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention.

[0049] like Figure 1 As shown, one embodiment of the present invention proposes a PLC-based automatic temperature control system, the PLC-based automatic temperature control system comprising:

[0050] The first prediction module 10 is used to predict the historical sensor temperature and historical air conditioning temperature in the shielded room using the moving average method, and obtain the predicted values ​​of sensor temperature and air conditioning temperature respectively.

[0051] The moving average method typically calculates the average of adjacent data points in a time series as the predicted value. This embodiment uses the moving average method to predict the sensor temperature and air conditioning temperature in a shielded equipment room. Specifically, refer to... Figure 2 , Figure 2 This is a first detailed schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention, as shown below. Figure 2 The first prediction module 10 shown includes:

[0052] The historical temperature acquisition unit 101 is used to acquire the historical sensor temperature collected by the temperature sensors installed in various areas of the shielded equipment room at preset time intervals, and to acquire the historical air conditioning temperature K at the corresponding time.

[0053] Temperature sensors are pre-installed in appropriate areas within the shielded server room where temperature control is required. To better ensure the monitoring and control of temperature information within the shielded server room, the temperature sensors are placed close to, or even adjacent to, the server host. In this embodiment, based on experience, the number of temperature sensors 'a' is set to 10, meaning 10 temperature sensors are installed within the shielded server room.

[0054] Each temperature sensor is controlled to collect the temperature inside the shielded equipment room at preset time intervals, and the temperature collected by the temperature sensors is recorded as the historical sensor temperature T. At the corresponding moment when the historical sensor temperature is collected, the air conditioning temperature is obtained and recorded as the historical air conditioning temperature K. In this embodiment, the air conditioning temperature refers to the air conditioning set temperature, and the historical air conditioning temperature refers to the air conditioning set temperature at the corresponding moment.

[0055] The data acquisition time interval can be set as needed, for example, set to 5 minutes. The historical sensor temperatures (T), historical air conditioning temperatures (K), and corresponding acquisition times are saved sequentially.

[0056] The sensor temperature prediction unit 102 is used to obtain the predicted sensor temperature value of each sensor based on the historical sensor temperature collected by each sensor and by using the moving average method.

[0057] This embodiment uses the well-known steps of the moving average method for prediction, which will not be elaborated here. The obtained sensor temperature prediction value is recorded as Tc. It is worth noting that this embodiment is based on m consecutive historical sensor temperatures for prediction. If the number of historical sensor temperatures is recorded as n, then n-m+1 sensor temperature prediction values ​​Tc are obtained. The prediction value obtained based on the current sensor temperature and the previous m-1 historical sensor temperatures is recorded as the sensor temperature prediction value for the next moment. All other prediction values ​​are recorded as sensor temperature prediction values.

[0058] The air conditioning temperature prediction unit 103 is used to obtain the predicted air conditioning temperature value based on the historical air conditioning temperature at various times by using the moving average method.

[0059] Similarly, the predicted air conditioning temperature is obtained based on the historical air conditioning temperature at each corresponding time point using the moving average method, and the predicted air conditioning temperature is represented as Tk.

[0060] Because the moving average method yields average values, these forecasts always remain at historical levels and cannot predict future fluctuations, whether higher or lower. Therefore, temperature forecasts based on the moving average method need to be corrected.

[0061] Temperature prediction accuracy calculation module 20 is used to determine the temperature prediction accuracy correction factor η based on the correlation relationship between each group of temperature sensors, and to calculate the temperature prediction accuracy Zq based on the temperature prediction accuracy correction factor η and the importance of each temperature sensor.

[0062] Before the temperature prediction accuracy calculation module 20, the following is also included:

[0063] The historical temperature correlation calculation unit is used to determine the historical temperature correlation between the historical sensor temperature and the historical air conditioning temperature of each temperature sensor based on the difference between the historical sensor temperature and the historical air conditioning temperature at the same time.

[0064] The historical temperature correlation between the historical sensor temperature and the historical air conditioning temperature of temperature sensor s is expressed as: Then we have:

[0065]

[0066] Where n represents the number of historical sensor temperatures for each temperature sensor. This indicates that the formula within the parentheses is being normalized. This represents the historical sensor temperature value acquired by temperature sensor s at time i. This represents the historical air conditioning temperature corresponding to the i-th data collection time.

[0067] That is, the historical sensor temperature values ​​of temperature sensor s Historical air conditioning temperature at the corresponding time The smaller the difference, the greater the correlation between the historical sensor temperature and the historical air conditioning temperature. The stronger the correlation, the better. The correlation between the historical sensor temperature values ​​and the historical air conditioning temperature values ​​of each temperature sensor is obtained sequentially. For 10 temperature sensors, 10 corresponding historical temperature correlations can be obtained.

[0068] The first importance calculation unit is used to calculate based on historical sensor temperatures, historical air conditioning temperatures, and historical temperature correlations. Calculate the primary importance of each temperature sensor ;

[0069] The air conditioner is controlled by adjusting its power and fan speed based on the temperature values ​​from each temperature sensor. Therefore, the importance of each temperature sensor can be determined by analyzing the historical temperature values ​​collected by each sensor.

[0070] First, the temperature characteristic value of temperature sensor s at time i is calculated based on historical sensor temperatures and historical air conditioning temperatures, and is expressed as follows: :

[0071]

[0072] in, This represents the historical sensor temperature acquired by temperature sensor s at time i. This represents the historical sensor temperature obtained by temperature sensor w at time i, where a is the number of temperature sensors. This represents the historical air conditioning temperature at time i. This represents the difference between the historical air conditioning temperature values ​​at time i and its next adjacent time (i.e., time i+1). ).

[0073] Then, by combining the historical temperature correlation of temperature sensor s, the primary importance of the temperature sensor is determined, denoted as: :

[0074]

[0075] in, The historical temperature correlation of temperature sensor s, It is the sum of the temperature characteristic values ​​of temperature sensor s at all times, and n is the number of all times.

[0076] The sensor temperature prediction correlation calculation unit is used to determine the sensor temperature prediction correlation between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time based on the difference between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time.

[0077] The second importance calculation unit is used to calculate the second importance of each temperature sensor based on the sensor temperature prediction value, the air conditioning temperature prediction value, and the correlation of the sensor temperature prediction values.

[0078] Historical temperature correlation between historical sensor temperature and historical air conditioning temperature The primary importance of temperature sensors This is obtained based on historical sensor temperatures and historical air conditioning temperatures. In this embodiment, the correlation between the predicted sensor temperature and the predicted air conditioning temperature is calculated using the same method, based on the predicted sensor temperature and the predicted air conditioning temperature. The second importance of temperature sensor s .

[0079] In addition, prior to the temperature prediction accuracy calculation module 20, the following is also included:

[0080] The first correlation calculation unit is used to determine the first correlation relationship of each group of temperature sensors based on the difference in historical sensor temperatures of each group of temperature sensors at the same time and the difference in the average historical sensor temperatures of the corresponding group of temperature sensors. Each set of temperature sensors includes two temperature sensors;

[0081] In this embodiment, each temperature sensor involved is combined with the other temperature sensors to form a group of temperature sensors; that is, any two temperature sensors form a group. For ease of description, the temperature sensors in a group are referred to as temperature sensor s and temperature sensor l.

[0082] Based on the historical temperature acquisition time of the sensors, the difference in historical sensor temperature at the same time is first calculated for each group of temperature sensors, thus obtaining the historical sensor temperature difference for each group of temperature sensors at each time. Then, the average historical sensor temperature of each temperature sensor is calculated, and the difference in the average historical sensor temperature of each group of temperature sensors is obtained and recorded as the historical sensor temperature average difference.

[0083] Thus, the first correlation relationship of the temperature sensors, consisting of temperature sensor s and temperature sensor l, can be expressed as follows: Then we have:

[0084]

[0085] Where n represents the number of historical sensor temperatures for each temperature sensor. Since only one data point is collected at each acquisition time, n is also the number of acquisition times. This represents the historical sensor temperature acquired by temperature sensor s at time i. This represents the historical sensor temperature obtained by temperature sensor l at time i. This represents the difference in historical sensor temperatures at time i for this group of temperature sensors. This indicates the difference in the historical average temperature values ​​of this group of temperature sensors.

[0086] Thus, the smaller the difference in historical sensor temperatures and the smaller the average difference between the two temperature sensors at the same time, the stronger the primary correlation, indicating that the two corresponding temperature sensors are more correlated.

[0087] The second correlation calculation unit is used to determine the second correlation relationship of each group of temperature sensors based on the difference in the predicted sensor temperature values ​​of each group of temperature sensors at the same time, and the difference in the average value of the predicted sensor temperature values ​​of the corresponding group of temperature sensors. .

[0088] The first correlation relationship is obtained based on historical sensor temperatures. Similarly, the second correlation relationship for each group of temperature sensors is obtained based on predicted sensor temperatures. This second correlation relationship is expressed as... The calculation method for the second correlation is the same as that for the first correlation, and will not be repeated here.

[0089] Reference Figure 3 , Figure 3 This is a first detailed schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention, as shown below. Figure 3 The temperature prediction accuracy calculation module 20 shown includes:

[0090] The accuracy correction factor calculation unit 201 is used to determine the first absolute value of the difference between the second correlation relationship and the first correlation relationship among each group of temperature sensors, sum the first absolute values ​​of each group of temperature sensors, and determine the first normalized value of the summation result as the accuracy correction factor for the temperature prediction value. ;

[0091] The temperature prediction accuracy correction factor is expressed as: Then we have:

[0092]

[0093] in, Indicates the number of temperature sensors. This indicates the first correlation relationship between the corresponding group of temperature sensors, consisting of temperature sensor s and temperature sensor l. This indicates the second correlation relationship between temperature sensors s and l, which form a corresponding group of temperature sensors.

[0094] When the first correlation relationship Relationship with the second correlation The smaller the difference, the lower the accuracy correction factor for the corresponding temperature prediction value. The smaller.

[0095] The first product acquisition unit 202 is used to determine the second absolute value of the difference between the second importance and the first importance of each temperature sensor, sum all the obtained absolute values, and calculate the second normalized value of the summation result and the accuracy correction factor of the temperature prediction value. The first product;

[0096] The temperature prediction accuracy calculation unit 203 is used to determine the difference between the constant 1 and the first product as the temperature prediction accuracy. .

[0097] The accuracy of the temperature prediction is expressed as... Then we have:

[0098]

[0099] in, This indicates the primary importance of the temperature sensor s. This indicates the second importance of the temperature sensor s. The smaller the difference between the first and second importance, the higher the accuracy of the temperature prediction.

[0100] The next-moment second temperature prediction value acquisition module 30 is used to obtain the next-moment first temperature prediction value based on the temperature sensor. Correlation weights with sensors Determine the predicted second temperature value at the next moment. ;

[0101] Reference Figure 4 , Figure 4 This is another schematic diagram of an embodiment of the PLC-based automatic temperature control system of the present invention, as shown below. Figure 4 As shown, the module for obtaining the second temperature prediction value at the next moment also includes the following before the module 30:

[0102] The next moment first temperature prediction value acquisition unit 301 is used to obtain the temperature based on the current temperature of the temperature sensor. The temperature sensor's predicted value for the next moment is obtained from the previous moment's temperature and the average of adjacent temperatures. ;

[0103] Specifically, the unit 301 for obtaining the first temperature prediction value at the next moment includes:

[0104] The first difference calculation subunit is used to calculate the current temperature of the temperature sensor. The first temperature difference at the previous moment ;

[0105] The second difference mean calculation subunit is used to obtain the historical sensor temperature at each previous time point and the corresponding second difference value at the next adjacent time point, and calculate the second difference mean value based on each second difference value. ;

[0106] The next moment's first temperature prediction calculation subunit is used to calculate the current moment's temperature. First difference and the second difference mean The sum is determined as the first predicted temperature value for the corresponding temperature sensor at the next moment. .

[0107] Corrected temperature prediction calculation module 40, used to calculate the temperature prediction value based on its accuracy. The second temperature prediction value at the next moment The sensor's temperature prediction value Tc is corrected to obtain the corrected temperature prediction value. ;

[0108] Continue to refer to Figure 4 The corrected temperature prediction calculation module 40 further includes, prior to:

[0109] The correlation reliability acquisition unit 401 is used to obtain the first correlation relationship of each group of temperature sensors at each time point. The reliability of the correlation is determined by the mean of the first correlation. ;

[0110] Since the historical data obtained at different times is also different, meaning the correlation between the corresponding temperature sensors will also change, we can obtain the change in the temperature correlation between temperature sensors obtained from historical data at different time periods. This allows us to obtain the reliability of the correlation between the temperature sensors, and the reliability of the correlation between temperature sensor S and temperature sensor L can be expressed as follows: Then we have:

[0111]

[0112] in, This represents the first correlation between temperature sensor s and temperature sensor h obtained at time h and its n neighboring times. This represents the average value of the primary correlation between temperature sensor s and temperature sensor h at n time points. In other words, the smaller the difference between the primary correlation between the sensor temperatures and its corresponding average primary correlation, the more reliable the correlation between temperature sensor s and temperature sensor h.

[0113] Sensor correlation weight determination unit 402 is used for reliability determination based on correlation relationship. Relationship with first correlation Determine the temperature correlation of each group of temperature sensors and determine the sensor correlation weights for each group of temperature sensors;

[0114] The temperature correlation of each group of temperature sensors is the product of the reliability of the corresponding correlation and the first correlation. The temperature correlation of the corresponding group of temperature sensors, denoted as temperature sensor s and temperature sensor h, is expressed as follows: Then we have:

[0115]

[0116] The sensor correlation weight is the ratio of the temperature correlation of this group to the sum of all temperature correlations corresponding to this temperature sensor. The sensor correlation weight of the temperature sensor is expressed as... Then we have:

[0117]

[0118] The greater the temperature correlation, the greater the corresponding sensor correlation weight.

[0119] The predicted second temperature value at the next moment of temperature sensor s is expressed as: Then we have:

[0120]

[0121] Similarly, calculate the second temperature prediction value for each temperature sensor at the next moment.

[0122] Specifically, the temperature prediction calculation module 40 includes:

[0123] The first product acquisition unit is used to acquire the first product of the temperature prediction accuracy Zq and the sensor temperature prediction value Tc; the first product of the temperature sensor s is... .

[0124] The second product acquisition unit is used to calculate the accuracy of constant 1 with the predicted temperature value. The difference is calculated, and the difference is compared with the predicted second temperature value at the next moment. The second product; the second product of temperature sensor s is .

[0125] The corrected temperature prediction value determination unit is used to determine the sum of the first product and the second product as the corrected temperature prediction value.

[0126] The corrected temperature prediction value is expressed as Then we have:

[0127]

[0128] The corrected temperature prediction values ​​for each temperature sensor are obtained sequentially. These corrected temperature prediction values ​​are based on historical sensor temperatures, historical air conditioning temperatures, sensor temperature prediction values, and air conditioning temperature prediction values. They take into account the importance of each temperature sensor and the correlation between them, making the corrected temperature prediction values ​​more accurate and reliable.

[0129] The control module 50 is used to generate analog signals from the corrected temperature prediction values ​​of each temperature sensor, and to control the air conditioning temperature using a PLC control system based on the analog signals.

[0130] First, predict the temperature value of each temperature sensor at the next moment, calculate the corrected temperature prediction value, and then combine all the corrected temperature prediction values.

[0131] Secondly, for any region, calculate the difference between the corrected temperature prediction value and the temperature set by the PID control system for that region, and convert the difference into an analog signal using an A / D converter;

[0132] Then, the analog signal is input to the controller in the PID control system, which outputs a PWM or control signal. Based on the PWM or control signal, the compressor is driven by a solid-state relay or the fan speed is adjusted by a frequency converter to achieve temperature control in this area.

[0133] This embodiment provides a PLC-based automatic temperature control system comprising: a first prediction module, used to predict historical sensor temperatures and historical air conditioning temperatures in a shielded room using a moving average method, obtaining predicted sensor temperatures and predicted air conditioning temperatures respectively; a temperature prediction accuracy calculation module, used to determine a temperature prediction accuracy correction factor based on the correlation between each group of temperature sensors, and calculate the temperature prediction accuracy based on the temperature prediction accuracy correction factor and the importance of each temperature sensor; a next-moment second temperature prediction value acquisition module, used to determine the next-moment second temperature prediction value based on the next-moment first temperature prediction value of the temperature sensor and the sensor correlation weight; a corrected temperature prediction value calculation module, used to correct the sensor temperature prediction values ​​based on the temperature prediction accuracy and the next-moment second temperature prediction value, obtaining a corrected temperature prediction value; and a control module, used to generate analog signals from the corrected temperature prediction values ​​of each temperature sensor, and control the air conditioning temperature using the PLC control system based on the analog signals. In this way, the importance of each sensor and the correlation between sensors are determined based on historical and predicted temperatures. Then, the predicted temperature obtained based on the moving average method is corrected. The temperature in the shielded room is controlled based on the corrected temperature prediction value, which improves the accuracy of the temperature prediction value, realizes accurate temperature control in the shielded room, and ensures the temperature stability of each area in the shielded room.

[0134] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. Any equivalent structural or procedural changes made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of the present invention.

Claims

1. A PLC-based automatic temperature control system, characterized in that, The PLC-based automatic temperature control system includes: The first prediction module is used to predict the historical sensor temperature and historical air conditioning temperature in the shielded room using the moving average method, and obtain the predicted values ​​of sensor temperature and air conditioning temperature respectively. The temperature prediction accuracy calculation module is used to determine the temperature prediction accuracy correction factor based on the correlation between each group of temperature sensors, and to calculate the temperature prediction accuracy based on the temperature prediction accuracy correction factor and the importance of each temperature sensor; wherein, the importance is determined by the sensor temperature prediction value and the air conditioning temperature prediction value. The module for obtaining the second temperature prediction value at the next moment is used to determine the second temperature prediction value at the next moment based on the first temperature prediction value at the next moment from the temperature sensor and the sensor correlation weight. The corrected temperature prediction calculation module is used to correct the sensor temperature prediction value based on the accuracy of the temperature prediction value and the second temperature prediction value at the next moment, so as to obtain the corrected temperature prediction value. The control module is used to generate analog signals from the corrected temperature prediction values ​​of each temperature sensor, and to control the air conditioning temperature using a PLC control system based on the analog signals. The module for obtaining the first temperature prediction value at the next moment includes: a unit for obtaining the first temperature prediction value at the next moment, which is used to obtain the first temperature prediction value at the next moment of the temperature sensor based on the current temperature of the temperature sensor, the temperature of the previous moment, and the average of adjacent temperatures. The unit for obtaining the predicted first temperature value at the next moment includes: The first difference calculation subunit is used to calculate the first difference between the current temperature and the previous temperature of the temperature sensor. The second difference mean calculation subunit is used to obtain the historical sensor temperature at each previous time and the corresponding second difference value at the next adjacent time, and calculate the second difference mean value based on each second difference value. The subunit for calculating the first temperature prediction value at the next moment is used to determine the sum of the current temperature, the first difference, and the average of the second difference as the first temperature prediction value at the next moment for the corresponding temperature sensor.

2. The PLC-based automatic temperature control system according to claim 1, characterized in that, The first prediction module includes: The historical temperature acquisition unit is used to acquire the historical sensor temperatures collected by temperature sensors installed in various areas of the shielded equipment room at preset time intervals, and to acquire the historical air conditioning temperature K at the corresponding time. The sensor temperature prediction unit is used to obtain the predicted sensor temperature value of each sensor based on the historical sensor temperatures collected by each sensor and by using the moving average method. The air conditioning temperature prediction unit is used to obtain the predicted air conditioning temperature value based on the historical air conditioning temperature at various times using the moving average method.

3. The PLC-based automatic temperature control system according to claim 1, characterized in that, The module used to determine the importance of each temperature sensor includes: The historical temperature correlation calculation unit is used to determine the historical temperature correlation between the historical sensor temperature and the historical air conditioning temperature of each temperature sensor based on the difference between the historical sensor temperature and the historical air conditioning temperature at the same time. The first importance calculation unit is used to calculate the first importance of each temperature sensor based on historical sensor temperature, historical air conditioning temperature, and historical temperature correlation. The sensor temperature prediction correlation calculation unit is used to determine the sensor temperature prediction correlation between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time based on the difference between the sensor temperature prediction value and the air conditioner temperature prediction value at the same time. The second importance calculation unit is used to calculate the second importance of each temperature sensor based on the sensor temperature prediction value, the air conditioning temperature prediction value, and the correlation of the sensor temperature prediction values.

4. The PLC-based automatic temperature control system according to claim 3, characterized in that, The correlation relationships between the temperature sensors in each group include the first correlation relationship and the second correlation relationship; The module for obtaining the correlation relationship between the various groups of temperature sensors includes: The first correlation calculation unit is used to determine the first correlation relationship of each group of temperature sensors based on the difference of the historical sensor temperature of each group of temperature sensors at the same time and the difference of the historical sensor temperature average value of the corresponding group of temperature sensors. Each group of temperature sensors includes two temperature sensors. The second correlation calculation unit is used to determine the second correlation relationship of each group of temperature sensors based on the difference in the predicted sensor temperature values ​​of each group of temperature sensors at the same time and the difference in the average value of the predicted sensor temperature values ​​of the corresponding group of temperature sensors.

5. The PLC-based automatic temperature control system according to claim 4, characterized in that, The temperature prediction accuracy calculation module includes: The accuracy correction factor calculation unit is used to determine the first absolute value of the difference between the second correlation relationship and the first correlation relationship between each group of temperature sensors, sum the first absolute values ​​of each group of temperature sensors, and determine the first normalized value of the summation result as the accuracy correction factor of the temperature prediction value. The first product acquisition unit is used to determine the second absolute value of the difference between the second importance and the first importance of each temperature sensor, sum all the obtained absolute values, and calculate the second normalized value of the summation result and the first product of the accuracy correction factor of the temperature prediction value. The temperature prediction accuracy calculation unit is used to determine the temperature prediction accuracy by the difference between the constant 1 and the first product.

6. The PLC-based automatic temperature control system according to claim 4, characterized in that, The corrected temperature prediction calculation module includes: The sensor correlation weight determination unit is used to determine the temperature correlation of each group of temperature sensors based on the reliability of the correlation relationship and the first correlation relationship, and to determine the sensor correlation weight of each group of temperature sensors.

7. The PLC-based automatic temperature control system according to claim 6, characterized in that, The corrected temperature prediction calculation module also includes: The correlation reliability acquisition unit is used to determine the correlation reliability based on the first correlation relationship and the mean of the first correlation relationship of each group of temperature sensors at each time.

8. The PLC-based automatic temperature control system according to claim 1, characterized in that, The corrected temperature prediction calculation module includes: The first product acquisition unit is used to acquire the first product of the accuracy of the temperature prediction value and the sensor temperature prediction value; The second product acquisition unit is used to calculate the difference between the constant 1 and the accuracy of the temperature prediction value, and to obtain the second product of the difference and the second temperature prediction value at the next moment. The corrected temperature prediction value determination unit is used to determine the sum of the first product and the second product as the corrected temperature prediction value.

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