Energy management system and method for manufacturing workshop, execution equipment and medium

Abstract

The invention discloses an energy management system and method for a manufacturing workshop, execution equipment and a medium, and belongs to the technical field of general control or regulation systems. The system comprises a sensing module used for acquiring temperature data of a manufacturing workshop and temperature difference data and humidity difference data inside and outside the manufacturing workshop; the calculation unit is used for obtaining a first operation duration of the temperature control equipment based on the temperature data by using a pre-constructed first operation duration calculation formula, and obtaining a second operation duration of the wind pressure equipment based on the temperature difference data and the humidity difference data by using a pre-constructed second operation duration calculation formula; and the control unit is used for controlling start and stop of the temperature control equipment and the wind pressure equipment. According to the technical scheme, through the regression fitting operation duration calculation formula, the accurate equipment operation duration can be predicted according to the actual manufacturing workshop environment, then accurate control over equipment starting and stopping is achieved, and the energy utilization efficiency is effectively improved.

Description

Technical Field

[0001] The present application belongs to the general field of control or regulation system technology, and specifically relates to an energy management system, method, execution equipment and medium for a manufacturing workshop. Background Art

[0002] During spraying and other manufacturing operations, appropriate temperature and humidity are key factors to ensure the quality of manufacturing operations. Take the spraying workshop as an example. The heating equipment and fans must be turned on in advance. The heating equipment can make the temperature and humidity in the spraying workshop reach the ideal range for spraying operations as soon as possible. The fans can maintain a slightly positive pressure environment in the spraying workshop by supplying and exhausting air, avoiding the entry of external ash and affecting the cleanliness of the spraying workshop, laying the foundation for high-quality spraying operations.

[0003] Currently, in the actual operation of spray painting workshops, a fixed setting mode is generally used for the running time of the equipment to be turned on in advance. However, the internal and external environment of the spray painting workshop is extremely complex and changeable. If a unified running time is always used, the equipment will continue to run during the period when it is not needed, resulting in a large amount of energy waste. In addition, some spray painting workshops also rely on manual judgment to judge the running time of the equipment to be turned on in advance, but manual judgment has great limitations and low accuracy.

[0004] Therefore, how to accurately predict the equipment's early start-up operation time based on the actual environment of the workshop is an urgent problem that people in this field need to solve. Summary of the invention

[0005] The present application provides an energy management system, method, execution device and medium for a manufacturing workshop, the purpose of which is to accurately predict the equipment operating time based on the actual manufacturing workshop environment, thereby achieving precise control of equipment start and stop, and effectively improving energy utilization efficiency.

[0006] In a first aspect, the present application provides an energy management system for a manufacturing workshop, the system comprising: A sensing module is used to obtain temperature data of the manufacturing workshop, as well as temperature difference data and humidity difference data inside and outside the manufacturing workshop; a calculation unit connected to the sensing module, and configured to use a pre-constructed first operation time calculation formula to obtain a first operation time of the temperature control device based on the temperature data, and to use a pre-constructed second operation time calculation formula to obtain a second operation time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operation time calculation formula is obtained by regression fitting calculation based on historical temperature data of the manufacturing workshop, and the second operation time calculation formula is obtained by regression fitting calculation based on historical temperature difference data and historical humidity difference data of the manufacturing workshop; a control unit connected to the calculation unit, and configured to control the start and stop of the temperature control device according to the first operation duration, and control the start and stop of the wind pressure device according to the second operation duration; The temperature control device is arranged in the manufacturing workshop and connected to the control unit to perform start and stop operations; The wind pressure equipment is arranged in a manufacturing workshop and connected to the control unit for executing start and stop operations.

[0007] In a second aspect, the present application provides an energy management method for a manufacturing workshop, the method comprising: The temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop are obtained through the sensing module; Using a pre-constructed first operating time calculation formula, a first operating time of the temperature control device is obtained based on the temperature data, and using a pre-constructed second operating time calculation formula, a second operating time of the wind pressure device is obtained based on the temperature difference data and the humidity difference data; wherein the first operating time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; The start and stop of the temperature control device are controlled according to the first operation duration, and the start and stop of the wind pressure device are controlled according to the second operation duration.

[0008] In a third aspect, the present application provides an execution device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the energy management method for a manufacturing workshop as described in the second aspect.

[0009] In a fourth aspect, the present application provides a readable storage medium storing a program or instruction, which, when executed by a processor, implements the steps of the energy management method for a manufacturing workshop as described in the second aspect.

[0010] In the present application, a perception module is used to obtain temperature data of a manufacturing workshop, as well as temperature difference data and humidity difference data inside and outside the manufacturing workshop; a calculation unit is connected to the perception module, and is used to use a pre-constructed first operating time calculation formula to obtain a first operating time of a temperature control device based on the temperature data, and to use a pre-constructed second operating time calculation formula to obtain a second operating time of a wind pressure device based on the temperature difference data and the humidity difference data; wherein, the first operating time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; a control unit is connected to the calculation unit, and is used to control the start and stop of the temperature control device according to the first operating time, and to control the start and stop of the wind pressure device according to the second operating time; the temperature control device is arranged in the manufacturing workshop, connected to the control unit, and is used to perform start and stop operations; the wind pressure device is arranged in the manufacturing workshop, connected to the control unit, and is used to perform start and stop operations. The energy management system of the above-mentioned manufacturing workshop can predict the accurate equipment operating time according to the actual manufacturing workshop environment through regression fitting of the operating time calculation formula, thereby achieving precise control of equipment start and stop, and effectively improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a structural schematic diagram of the energy management system of a manufacturing workshop provided in Example 1 of the present application; Figure 2 is a line comparison chart of the actual operating time and the first operating time of the temperature control device provided in the first embodiment of the present application; Figure 3 This is a schematic diagram of the structure of the energy management system of a manufacturing workshop provided in Example 2 of the present application; Figure 4 is a schematic diagram of the structure of the energy management system of a manufacturing workshop provided in Example 3 of the present application; Figure 5 It is a flowchart of the energy management method for a manufacturing workshop provided in the fourth embodiment of the present application; Figure 6 It is a structural diagram of the execution device provided in Example 5 of the present application. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only part of the present application is shown in the accompanying drawings, but not all of the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0014] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0015] In conjunction with the accompanying drawings, the energy management system, method, execution device and medium of the manufacturing workshop provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0016] Embodiment 1 Figure 1 Schematic diagram of the structure of the energy management system of the manufacturing workshop provided in the first embodiment of the present application. Figure 1 As shown, the system comprises: The sensing module 110 is used to obtain the temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop; A calculation unit 120 is connected to the perception module and is used to use a pre-constructed first operation time calculation formula to obtain a first operation time of the temperature control device based on the temperature data, and to use a pre-constructed second operation time calculation formula to obtain a second operation time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operation time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operation time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; a control unit 130, connected to the calculation unit, for controlling the start and stop of the temperature control device according to the first operation duration, and controlling the start and stop of the wind pressure device according to the second operation duration; The temperature control device 140 is arranged in the manufacturing workshop and connected to the control unit to perform start and stop operations; The wind pressure device 150 is arranged in a manufacturing workshop and connected to the control unit for executing start and stop operations.

[0017] This application is applicable to scenarios where the temperature and humidity in a manufacturing workshop need to be pre-adjusted for production and manufacturing. Specifically, the determination of the first operating time and the second operating time and the start and stop control of the temperature control device and the wind pressure device can be performed by intelligent terminal devices, such as desktop computers, laptops, mobile phones, tablet computers, and interactive multimedia, etc., and no excessive restrictions are made here.

[0018] The sensing module 110 is used to obtain the temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop.

[0019] A manufacturing workshop may refer to a space where raw materials are transformed into finished or semi-finished products. For example, in an automobile production line, a manufacturing workshop may include a spray painting workshop.

[0020] The temperature data of the manufacturing workshop may refer to the temperature data of multiple locations in the manufacturing workshop. Wherein, temperature is a physical quantity indicating the degree of hotness or coldness of an object. The temperature data of the manufacturing workshop may be collected by temperature sensors arranged at multiple locations in the manufacturing workshop, wherein the locations include at least two, for example, six.

[0021] The temperature difference data inside and outside the manufacturing workshop may refer to the difference between the temperature data inside the manufacturing workshop and the temperature data outside the manufacturing workshop. The method of obtaining the temperature difference data inside and outside the manufacturing workshop may be to obtain temperature data through temperature sensors arranged at multiple locations in the manufacturing workshop, and calculate the average value of these temperature data as the temperature data inside the manufacturing workshop, obtain temperature data outside the manufacturing workshop through temperature sensors arranged outside the manufacturing workshop, and calculate the difference between the temperature data inside the manufacturing workshop and the temperature data outside the manufacturing workshop as the temperature difference data inside and outside the manufacturing workshop.

[0022] The humidity difference data inside and outside the manufacturing workshop may refer to the difference between the humidity data inside the manufacturing workshop and the humidity data outside the manufacturing workshop. Humidity is a physical quantity indicating the amount of water vapor in the air. The method for obtaining the humidity difference data inside and outside the manufacturing workshop may be to obtain the humidity data inside the manufacturing workshop through a humidity sensor arranged inside the manufacturing workshop, obtain the humidity data outside the manufacturing workshop through a humidity sensor arranged outside the manufacturing workshop, and calculate the difference between the humidity data inside the manufacturing workshop and the humidity data outside the manufacturing workshop as the humidity difference data inside and outside the manufacturing workshop.

[0023] After obtaining the temperature data of the manufacturing workshop, and the temperature difference data and humidity difference data inside and outside the manufacturing workshop, the temperature data, the temperature difference data, and the humidity difference data can be subjected to outlier cleaning and standardization (e.g., Z-score standardization). The outlier value range based on which the outlier cleaning is performed can be determined based on the historical maximum and minimum values ​​of the temperature data, the temperature difference data, and the humidity difference data.

[0024] The calculation unit 120 is connected to the perception module and is used to use a pre-constructed first operating time calculation formula to obtain a first operating time of the temperature control device based on the temperature data, and to use a pre-constructed second operating time calculation formula to obtain a second operating time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operating time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop.

[0025] The connection between the computing unit and the perception module can be wireless or wired, and no further restrictions are made here.

[0026] The first operating time of the temperature control device may refer to the predicted time that the temperature control device should run in order to make the temperature and humidity in the manufacturing workshop meet the set requirements. Correspondingly, the first operating time calculation formula is a calculation formula that can determine the first operating time of the temperature control device based on the temperature data of the manufacturing workshop currently obtained. Substitute the currently obtained temperature data of the manufacturing workshop into the first operating time calculation formula, and the calculation result is the first operating time of the temperature control device. Among them, if the calculated first operating time of the temperature control device is a negative value, the first operating time of the temperature control device is determined to be 0 (that is, the temperature control device starts running immediately).

[0027] Among them, the first running time calculation formula can be obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop. Specifically, by inputting the historical temperature data of the manufacturing workshop and the corresponding historical running time into the linear regression model, the linear regression model can output the first running time calculation formula. Among them, the historical temperature data can refer to the temperature data of the manufacturing workshop before the current time point; the corresponding historical running time can refer to the actual running time of the temperature control equipment based on the historical temperature data in order to make the temperature and humidity in the manufacturing workshop meet the set requirements; the linear regression model is a statistical model used to study the linear relationship between variables, trying to find a linear function to best describe the relationship between the independent variable and the dependent variable. In this solution, the linear regression model can be fitted using the least squares method.

[0028] As an example, historical temperature data and the corresponding historical operating time are shown in the following table (Table 1):

[0029] Table 1 Among them, GB_PA3.DYL.T1 is the historical temperature data collected by the temperature sensor at the first position in the manufacturing workshop, GB_PA3.DYL.T2 is the historical temperature data collected by the temperature sensor at the second position in the manufacturing workshop, GB_PA3.DYL.T3 is the historical temperature data collected by the temperature sensor at the third position in the manufacturing workshop, GB_PA3.DYL.T4 is the historical temperature data collected by the temperature sensor at the fourth position in the manufacturing workshop, GB_PA3.DYL.T5 is the historical temperature data collected by the temperature sensor at the fifth position in the manufacturing workshop, GB_PA3.DYL.T6 is the temperature data collected by the temperature sensor at the sixth position in the manufacturing workshop, and GB_PA3.DYL.Time is the corresponding historical operating time.

[0030] The historical temperature data and the corresponding historical operating time in Table 1 are input into the linear regression model, and the linear regression model outputs the first operating time calculation formula.

[0031] After the linear regression model is output, an F test can be performed to obtain the P value and VIF value. Among them, the F test is a hypothesis test method widely used in statistics, which is used to test whether two or more population variances are equal, or to test whether the joint effect of independent variables on the dependent variable in the linear regression model is significant. Among them, the P value is a probability value used in the hypothesis test to determine whether to reject the null hypothesis; in the context of the F test, the P value is the probability that the observed F statistic is greater than or equal to the actual calculated F value under the premise that the null hypothesis (that is, the regression coefficients of all independent variables are 0, which means that the independent variables as a whole have no significant effect on the dependent variable) is established; if the P value is <0.05, the null hypothesis is rejected, and it is believed that at least one of the regression coefficients of the independent variables is not 0, that is, the regression model as a whole is significant, and the independent variables have significant explanatory power for the dependent variable; if the P value is >=0.05, the null hypothesis cannot be rejected, which means that the regression model as a whole is not significant, and the independent variables may not have a significant effect on the dependent variable. Among them, the VIF value is used to measure the degree of collinearity between independent variables in the multiple linear regression model. For each independent variable, the calculation of its VIF value is based on the linear relationship between the independent variable and other independent variables; the larger the VIF value, the stronger the linear relationship between the independent variable and other independent variables, and the more serious the collinearity problem; it is generally believed that when the VIF value is <10, the degree of collinearity between independent variables is within an acceptable range and will not have a serious impact on the estimation and inference of the regression model; when the VIF value is >=10, it indicates that there is a more serious collinearity problem, which may lead to inaccurate and unstable estimation of the regression coefficient and unreliable results of the hypothesis test.

[0032] Therefore, when the output result of the linear regression model satisfies the P value<0.05 and the VIF value<10, the output result can be determined as the first running time calculation formula.

[0033] Figure 2 1 is a line comparison chart of the actual operating time and the first operating time of the temperature control device provided in the first embodiment of the present application. Figure 2 As shown, the solid line is the actual running time broken line, and the dotted line is the first running time broken line. The actual running time broken line and the first running time broken line are highly overlapped, indicating that the first running time calculated according to the first running time calculation formula fits the actual running time and has high accuracy.

[0034] The second operating time of the wind pressure device may refer to the predicted time that the wind pressure device should run in order to make the temperature and humidity in the manufacturing workshop reach the set requirements. Correspondingly, the second operating time calculation formula is a calculation formula that can determine the second operating time of the wind pressure device based on the currently acquired temperature difference data and humidity difference data inside and outside the manufacturing workshop. Substitute the currently acquired temperature difference data and humidity difference data inside and outside the manufacturing workshop into the second operating time calculation formula, and the calculation result is the second operating time of the wind pressure device. Among them, if the calculated second operating time of the wind pressure device is a negative value, the second operating time of the wind pressure device is determined to be 0 (that is, the wind pressure device starts running immediately).

[0035] Among them, the second operating time calculation formula can be obtained by regression fitting calculation based on the historical temperature difference data and historical humidity difference data of the manufacturing workshop. Specifically, by inputting the historical temperature difference data and historical humidity difference data of the manufacturing workshop and the corresponding historical operating time into the linear regression model, the linear regression model can output the second operating time calculation formula. Among them, the historical temperature difference data can refer to the temperature difference data inside and outside the manufacturing workshop before the current time point, and the historical humidity difference data can refer to the humidity difference data inside and outside the manufacturing workshop before the current time point; the corresponding historical operating time can refer to the actual operating time of the wind pressure equipment based on the historical temperature difference data and the historical humidity difference data in order to make the temperature and humidity in the manufacturing workshop reach the set requirements.

[0036] As an example, the historical temperature difference data and the historical humidity difference data and the corresponding historical operation time are shown in the following table (Table 2):

[0037] Table 2 in, The historical temperature difference data collected by temperature sensors inside and outside the manufacturing workshop, It is the historical humidity difference data collected by the humidity sensors inside and outside the manufacturing workshop, and Time is the corresponding historical operating time.

[0038] The historical temperature difference data and the historical humidity difference data in Table 2 above and the corresponding historical operating time are input into the linear regression model, and the linear regression model outputs a second operating time calculation formula.

[0039] After the linear regression model is output, an F test can be performed to obtain a P value and a VIF value. When the output result of the linear regression model satisfies the P value < 0.05 and the VIF value < 10, the output result is determined as the second running time calculation formula.

[0040] The control unit 130 is connected to the calculation unit, and is used to control the start and stop of the temperature control device according to the first operating time, and to control the start and stop of the wind pressure device according to the second operating time.

[0041] The connection between the computing unit and the control unit may be wireless or wired, and no further limitation is made here.

[0042] The control unit can generate control instructions for the temperature control device according to the first operating duration to control the start and stop of the temperature control device; the control unit can generate control instructions for the wind pressure device according to the second operating duration to control the start and stop of the wind pressure device.

[0043] The temperature control device 140 is arranged in the manufacturing workshop and connected to the control unit for performing start and stop operations.

[0044] The connection between the temperature control device and the control unit can be wireless or wired, and no further restrictions are made here.

[0045] The temperature control device may be a device for controlling the ambient temperature, such as an electric pulse furnace, which utilizes the high temperature of the electric arc generated between the electrode and the charge to perform heating treatment.

[0046] The wind pressure device 150 is arranged in a manufacturing workshop and connected to the control unit for executing start and stop operations.

[0047] The connection between the wind pressure device and the control unit can be wireless or wired, and no further restrictions are made here.

[0048] Wind pressure equipment can be a device used to control the flow of gas in an environment, such as a fan, wherein the fan is a device that relies on input mechanical energy to increase gas pressure and discharge gas.

[0049] In an embodiment of the present application, a perception module is used to obtain temperature data of a manufacturing workshop, as well as temperature difference data and humidity difference data inside and outside the manufacturing workshop; a calculation unit is connected to the perception module, and is used to use a pre-constructed first operating time calculation formula to obtain a first operating time of a temperature control device based on the temperature data, and to use a pre-constructed second operating time calculation formula to obtain a second operating time of a wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operating time calculation formula is obtained by regression fitting calculation based on historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on historical temperature difference data and historical humidity difference data of the manufacturing workshop; a control unit is connected to the calculation unit, and is used to control the start and stop of the temperature control device according to the first operating time, and to control the start and stop of the wind pressure device according to the second operating time; the temperature control device is arranged in the manufacturing workshop, connected to the control unit, and is used to perform start and stop operations; the wind pressure device is arranged in the manufacturing workshop, connected to the control unit, and is used to perform start and stop operations. The energy management system of the above-mentioned manufacturing workshop can predict the accurate equipment operating time according to the actual manufacturing workshop environment through regression fitting of the operating time calculation formula, thereby achieving precise control of equipment start and stop, and effectively improving energy utilization efficiency.

[0050] Embodiment 2 Figure 3 It is a structural diagram of the energy management system of the manufacturing workshop provided in the second embodiment of the present application. This scheme makes a better improvement on the above embodiment, and the specific improvement is as follows: the temperature data is collected by temperature sensors arranged at at least two positions; the pre-constructed first operation time calculation formula includes the corresponding first regression coefficient and the first constant; the temperature difference data is collected by the temperature sensors arranged inside and outside the manufacturing workshop, and the humidity difference data is collected by the humidity sensors arranged inside and outside the manufacturing workshop; the pre-constructed second operation time calculation formula includes the corresponding second regression coefficient and the second constant; accordingly, the calculation unit is specifically used to: multiply the temperature data with the corresponding first regression coefficient to obtain multiple summation factors; sum the multiple summation factors and the first constant to obtain the first operation time; accordingly, the calculation unit is specifically used to: multiply the temperature difference data and the humidity difference data with the corresponding second regression coefficient to obtain two summation factors; sum the two summation factors and the second constant to obtain the second operation time.

[0051] like Figure 3 As shown, the system comprises: The sensing module 310 is used to obtain the temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop; A calculation unit 320 is connected to the perception module, and is used to use a pre-constructed first operation time calculation formula to obtain a first operation time of the temperature control device based on the temperature data, and to use a pre-constructed second operation time calculation formula to obtain a second operation time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operation time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operation time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; a control unit 330, connected to the calculation unit, configured to control the start and stop of the temperature control device according to the first operation duration, and control the start and stop of the wind pressure device according to the second operation duration; The temperature control device 340 is arranged in the manufacturing workshop and connected to the control unit to perform start and stop operations; The wind pressure device 350 is arranged in the manufacturing workshop and connected to the control unit for executing start and stop operations.

[0052] The calculation unit 320 is specifically used for: Multiplying the temperature data by the corresponding first regression coefficient to obtain a plurality of summing factors; Summing the plurality of the summing factors and the first constant to obtain a first running time; The calculation unit 320 is specifically used for: Multiplying the temperature difference data and the humidity difference data with the corresponding second regression coefficient to obtain two addition factors; The two addition factors and the second constant are summed to obtain a second running time.

[0053] The first running time calculation formula includes the corresponding first regression coefficient and the first constant. The number of the first regression coefficients is consistent with the number of temperature data (i.e., the number of temperature sensors set), and the number of temperature data (i.e., the number of temperature sensors set) can be three, four, five, six, seven or more. The number of temperature data (i.e., the number of temperature sensors set) can be set according to actual needs, and no excessive restrictions are made here.

[0054] As an example, six temperature sensors are installed in the manufacturing workshop to collect temperature data T1, T2, T3, T4, T5 and T6, and the corresponding first regression coefficients include a1, a2, a3, a4, a5 and a6. It can be understood that in this example, the addition factor in the first running time calculation formula is , , , , as well as , the first running time calculation formula is: ; Wherein, C1 is the first constant, a1, a2, a3, a4, a5 and a6 are the first regression coefficients corresponding to T1, T2, T3, T4, T5 and T6 respectively.

[0055] If the first operation time calculation formula is pre-built according to the historical temperature data and the corresponding historical operation time in Table 1 above, the first operation time calculation formula can be obtained as follows: first operation time = 15.44-18.132×T1+4.922×T2+32.187×T3-5.493×T4+6.657×T5+4.297×T6. Among them, C1 can represent the first operation time of the temperature control device under the reference environmental conditions, for example, 15.44 minutes; taking a1 as an example, a1 is equal to -18.132, indicating that for every 1 degree Celsius increase in T1, the corresponding first operation time will be shortened by 18.132 minutes; taking a2 as an example, a2 is equal to 4.922, indicating that for every 1 degree Celsius increase in T2, the corresponding first operation time will be extended by 4.922 minutes.

[0056] It is understandable that, depending on the historical temperature data used and the corresponding historical operating hours, the first regression coefficient and the first constant in the first operating hour calculation formula obtained by regression fitting may also be different, for example, C1 equals 10.5, a1 equals -4.5, etc.

[0057] The second running time calculation formula includes the corresponding second regression coefficient and second constant. The temperature sensors inside and outside the manufacturing workshop collect temperature difference data , humidity sensors inside and outside the manufacturing workshop collect humidity difference data , the corresponding second regression coefficients include b1 and b2.

[0058] It can be understood that the addition factor in the second running time calculation formula is and , the second running time calculation formula is: ; Wherein, C2 is the second constant. As an example, if the second operation time calculation formula is pre-built according to the historical temperature difference data and historical humidity difference data in Table 2 and the corresponding historical operation time, the second operation time calculation formula can be obtained as follows: .

[0059] Among them, C2 can represent the second operation time of the wind pressure equipment under the reference environmental conditions, such as -8.72 minutes (start operation immediately); taking b1 as an example, b1 is equal to -16.5, which means For every 1 degree Celsius increase, the corresponding second running time will be shortened by 16.5 minutes; taking b2 as an example, b2 is equal to 3.9, which means For every 1% increase, the corresponding second run time will be extended by 3.9 minutes.

[0060] It can be understood that, depending on the historical temperature difference data and historical humidity difference data used and the corresponding historical operating hours, the second regression coefficient and the second constant in the second operating hour calculation formula obtained by regression fitting may also be different, for example, C2 equals -7.493, b1 equals -20.495, etc.

[0061] The advantage of this arrangement of the present scheme is that the pre-constructed first operating time calculation formula includes the corresponding first regression coefficient and the first constant, and the pre-constructed second operating time calculation formula includes the corresponding second regression coefficient and the second constant. The reasonable operating time of the temperature control equipment and the wind pressure equipment can be more accurately calculated dynamically based on the temperature, temperature difference and humidity difference data collected in real time in the manufacturing workshop, thereby achieving precise control of the start and stop of the equipment to achieve the purpose of saving energy.

[0062] In this technical solution, optionally, the computing unit is further used for: After the temperature control device stops, adjusting a first regression coefficient in the first operating time calculation formula according to an actual operating time of the temperature control device and the first operating time; After the wind pressure device stops, the second regression coefficient in the second operating time calculation formula is adjusted according to the actual operating time of the wind pressure device and the second operating time.

[0063] The actual operating time of the temperature control device may refer to the time from when the temperature control device starts to operate to when the temperature and humidity in the manufacturing workshop reach the set requirements. According to the actual operating time of the temperature control device and the first operating time, the first regression coefficient in the first operating time calculation formula is adjusted. The first error ratio can be determined according to the actual operating time of the temperature control device and the first operating time, and the first adjustment coefficient can be determined according to the first error ratio when the first error ratio does not exceed the error ratio threshold. When the first error ratio exceeds the error ratio threshold, the first adjustment coefficient is determined according to the actual operating time of the temperature control device and the first preset maximum operating time, and the first regression coefficient in the first operating time calculation formula is multiplied by the first adjustment coefficient to obtain the adjusted first regression coefficient.

[0064] The actual operating time of the wind pressure equipment may refer to the time from when the wind pressure equipment starts to operate to when the temperature and humidity in the manufacturing workshop reach the set requirements. According to the actual operating time of the wind pressure equipment and the second operating time, the second regression coefficient in the second operating time calculation formula is adjusted. The second error ratio is determined according to the actual operating time of the wind pressure equipment and the second operating time, and the second adjustment coefficient is determined according to the second error ratio when the second error ratio does not exceed the error ratio threshold. When the second error ratio exceeds the error ratio threshold, the second adjustment coefficient is determined according to the actual operating time of the wind pressure equipment and the second preset maximum operating time, and the second regression coefficient in the second operating time calculation formula is multiplied by the second adjustment coefficient to obtain the adjusted second regression coefficient.

[0065] In this technical solution, optionally, the computing unit is further used for: After the temperature control device stops, determining a first error ratio according to the actual operating time of the temperature control device and the first operating time, and after the wind pressure device stops, determining a second error ratio according to the actual operating time of the wind pressure device and the second operating time; When the first error ratio does not exceed the error ratio threshold, determining a first adjustment coefficient according to the first error ratio, and multiplying the first regression coefficient in the first running time calculation formula by the first adjustment coefficient to obtain an adjusted first regression coefficient; and / or, When the second error ratio does not exceed the error ratio threshold, a second adjustment coefficient is determined according to the second error ratio, and the second regression coefficient in the second running time calculation formula is multiplied by the second adjustment coefficient to obtain an adjusted second regression coefficient.

[0066] The error ratio is a relative indicator to measure the degree of deviation between the actual value and the theoretical value. The first error ratio is determined according to the actual operating time of the temperature control device and the first operating time, and can be calculated according to the following formula: The first error ratio is determined according to the actual operating time of the wind pressure device and the second operating time, and can be calculated according to the following formula: If the first running time and / or the second running time is a negative number, a value of 0 is substituted into the calculation formula.

[0067] The ratio threshold may be a preset upper limit of the error ratio indicating whether the first error ratio and / or the second error ratio is within an acceptable range, such as 20%. If the first error ratio and / or the second error ratio does not exceed the error ratio threshold, it indicates that the first error ratio and / or the second error ratio is of reference value for adjusting the first regression coefficient and / or the second regression coefficient.

[0068] The first adjustment coefficient can be determined according to the first error ratio by subtracting the first error ratio from 1 to obtain the first adjustment coefficient; the second adjustment coefficient can be determined according to the second error ratio by subtracting the second error ratio from 1 to obtain the second adjustment coefficient.

[0069] As an example, the actual operating time of the temperature control equipment is 58 minutes, and the first operating time is 56 minutes. The first error ratio can be calculated to be 3.6%, and the first regression coefficient can be further calculated to be 0.964. Then a3=32.187 multiplied by the first regression coefficient can obtain the adjusted a3=31.03.

[0070] The advantage of setting up this scheme is that, by determining the first / second adjustment coefficient according to the first / second error ratio when the first / second error ratio does not exceed the error ratio threshold, the first / second regression coefficient in the first / second running time calculation formula is multiplied by the first / second adjustment coefficient to obtain the adjusted first / second regression coefficient. When the error ratio is within an acceptable range, appropriate adjustments can be made to avoid excessive adjustments due to minor errors, so that the running time calculation formula can adapt to the dynamic changes of factors such as the manufacturing workshop environment and equipment status, and can remain relatively stable.

[0071] In this technical solution, optionally, the computing unit is further used for: After the temperature control device stops, determining a first error ratio according to the actual operating time of the temperature control device and the first operating time, and after the wind pressure device stops, determining a second error ratio according to the actual operating time of the wind pressure device and the second operating time; When the first error ratio exceeds the error ratio threshold, determining a first adjustment coefficient according to the actual operating time of the temperature control device and the first preset maximum operating time, and multiplying the first regression coefficient in the first operating time calculation formula by the first adjustment coefficient to obtain an adjusted first regression coefficient; and / or, When the second error ratio exceeds the error ratio threshold, the second adjustment coefficient is determined according to the actual operating time of the wind pressure equipment and the second preset maximum operating time, and the second regression coefficient in the second operating time calculation formula is multiplied by the second adjustment coefficient to obtain the adjusted second regression coefficient.

[0072] If the first error ratio and / or the second error ratio exceeds the error ratio threshold, it means that the first error ratio and / or the second error ratio are not of reference value for the adjustment of the first regression coefficient and / or the second regression coefficient. At this time, the first preset maximum operating time and the second preset maximum operating time need to be introduced. Among them, the first preset maximum operating time may refer to the upper limit of the operating time of the temperature control device, and the second preset maximum operating time may refer to the upper limit of the operating time of the wind pressure device.

[0073] The first adjustment coefficient can be determined according to the actual operating time of the temperature control device and the first operating time. The ratio of the actual operating time of the temperature control device to the first preset maximum operating time can be calculated, and the ratio is added to 1 to obtain the first adjustment coefficient; the second adjustment coefficient can be determined according to the actual operating time of the wind pressure device and the second operating time. The ratio of the actual operating time of the wind pressure device to the second preset maximum operating time can be calculated, and the ratio is added to 1 to obtain the second adjustment coefficient.

[0074] As an example, the actual operating time of the wind pressure equipment is 65 minutes, the second operating time is -139 minutes, and the second preset maximum operating time is 120 minutes. The second error ratio can be calculated to be 100%. Further, the second regression coefficient can be calculated to be 1.05 based on the actual operating time of the wind pressure equipment and the second preset maximum operating time. Then b2=4.297 multiplied by the second regression coefficient can obtain the adjusted b2=4.51.

[0075] The advantage of such a setting of the present scheme is that, when the first / second error ratio exceeds the error ratio threshold, the first / second adjustment coefficients are determined according to the actual operating time and the first / second preset maximum operating time, and the first / second regression coefficients in the first / second operating time calculation formulas are multiplied by the first / second adjustment coefficients to obtain the adjusted first / second regression coefficients. When the operating time prediction result deviates too greatly from the actual situation, the operating time calculation formula can be effectively corrected and optimized with the help of the preset upper limit of the equipment operating time.

[0076] Optionally, after obtaining the actual operating time, the first operating time and the second operating time for a preset number of times, a regression fitting calculation can be re-performed based on the obtained temperature data, temperature difference data and humidity difference data, as well as the actual operating time, the first operating time and the second operating time to obtain new first operating time calculation formulas and second operating time calculation formulas.

[0077] The advantage of setting up this scheme is that by adjusting the first / second regression coefficients in the first / second operating time calculation formula according to the actual operating time and the first / second operating time, the regression coefficients in the operating time calculation formula can be dynamically adjusted according to the actual operating conditions of the equipment, thereby continuously improving the prediction accuracy of the equipment operating time.

[0078] Embodiment 3 Figure 4 It is a structural diagram of the energy management system of a manufacturing workshop provided in Example 3 of the present application. This solution makes better improvements to the above embodiments, and the specific improvements are as follows: the calculation unit is also used to: execute a temperature data calibration strategy when the temperature data is not within the first preset temperature range; and / or, after the temperature control device stops, when the time difference between the actual operating time of the temperature control device and the first operating time exceeds the preset time difference threshold, and the temperature data is not within the second preset temperature range, execute the temperature data calibration strategy; wherein the second preset temperature range is a subset of the first preset temperature range.

[0079] like Figure 4 As shown, the system comprises: The sensing module 410 is used to obtain the temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop; A calculation unit 420 is connected to the perception module and is used to use a pre-constructed first operation time calculation formula to obtain a first operation time of the temperature control device based on the temperature data, and to use a pre-constructed second operation time calculation formula to obtain a second operation time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operation time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operation time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; a control unit 430, connected to the calculation unit, configured to control the start and stop of the temperature control device according to the first operation duration, and control the start and stop of the wind pressure device according to the second operation duration; The temperature control device 440 is arranged in the manufacturing workshop and connected to the control unit to perform start and stop operations; The wind pressure device 450 is arranged in the manufacturing workshop and is connected to the control unit for executing start and stop operations.

[0080] The computing unit 420 is further configured to: When the temperature data is not within the first preset temperature range, executing a temperature data calibration strategy; and / or, After the temperature control device stops, if the time difference between the actual operating time of the temperature control device and the first operating time exceeds a preset time difference threshold, and the temperature data is not within a second preset temperature range, a temperature data calibration strategy is executed; wherein the second preset temperature range is a subset of the first preset temperature range.

[0081] The first preset temperature range may be a pre-set temperature range indicating whether the temperature data is obviously abnormal. It is understandable that if the temperature data is not within the first preset range, it indicates that the temperature data is obviously abnormal.

[0082] The preset time difference threshold may be a preset upper limit of the time difference, for example 5 minutes, indicating whether the time difference between the actual operating time of the temperature control device and the first operating time is within an acceptable range. The second preset temperature range may be a preset temperature range indicating whether the temperature data may cause an error in the calculation result of the first operating time. The second preset temperature range may be set according to the minimum and maximum values ​​in the historical temperature data. It is understandable that the second preset temperature range should be a subset of the first preset temperature range. If the time difference between the actual operating time of the temperature control device and the first operating time exceeds the preset time difference threshold, and the temperature data is not within the second preset temperature range, it means that the error in the first operating time is caused by temperature data that is not within the second preset temperature range, and the temperature data is abnormal.

[0083] The temperature data calibration strategy includes obtaining historical temperature data of the location where the temperature data is located, and updating and calibrating the temperature data according to the historical temperature data, or obtaining temperature data of a location adjacent to the location where the temperature data is located, and updating and calibrating the temperature data according to the temperature data of the adjacent location.

[0084] In this technical solution, optionally, the computing unit is further used for: Acquire historical temperature data of the location where the temperature data is located, and update and calibrate the temperature data according to the historical temperature data; or, Acquire temperature data of a location adjacent to the location where the temperature data is located, and update and calibrate the temperature data according to the temperature data of the adjacent location.

[0085] The historical temperature data at the location of the temperature data may refer to the temperature data collected by the temperature sensor that collects the temperature data before the current time point. The method of updating the calibrated temperature data according to the historical temperature data may be to replace the abnormal temperature data with the average value of the historical temperature data.

[0086] The temperature data at the position adjacent to the temperature data may refer to the temperature data collected by two temperature sensors adjacent to the temperature sensor collecting the temperature data. For example, the temperature data at the adjacent positions of the abnormal temperature data T3 (collected by the temperature sensor at the third position in the manufacturing workshop) are T2 (collected by the temperature sensor at the second position in the manufacturing workshop) and T4 (collected by the temperature sensor at the fourth position in the manufacturing workshop). The method of updating the calibrated temperature data according to the temperature data at the adjacent positions may be to replace the abnormal temperature data with the average value of the temperature data at the adjacent positions.

[0087] The benefit of this arrangement of the present scheme is that by obtaining the historical temperature data / adjacent location temperature data of the temperature data location and updating the calibration temperature data based on the historical temperature data / adjacent location temperature data, the calibrated temperature data can be made more in line with the actual temperature conditions of the manufacturing workshop, thereby ensuring that the first operating time calculated based on these temperature data is more reasonable and accurate.

[0088] The benefit of such a setting of the present scheme is that by executing the temperature data calibration strategy when the temperature data is not within the first preset temperature range and / or when the time difference between the actual operating time of the temperature control device and the first operating time exceeds the preset time difference threshold, and the temperature data is not within the second preset temperature range, abnormal values ​​in the temperature data of the manufacturing workshop can be discovered and corrected in time, thereby ensuring the accuracy and reliability of the temperature data.

[0089] Embodiment 4 Figure 5 Schematic diagram of the energy management method for a manufacturing workshop provided in Example 4 of the present application. Figure 5 As shown, the method includes: S501, obtaining temperature data of the manufacturing workshop, as well as temperature difference data and humidity difference data inside and outside the manufacturing workshop through a sensing module; S502, using a pre-constructed first operating time calculation formula to obtain a first operating time of the temperature control device based on the temperature data, and using a pre-constructed second operating time calculation formula to obtain a second operating time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operating time calculation formula is obtained by regression fitting calculation based on historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on historical temperature difference data and historical humidity difference data of the manufacturing workshop; S503: Control the start and stop of the temperature control device according to the first operating time, and control the start and stop of the wind pressure device according to the second operating time.

[0090] In an embodiment of the present application, the temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop are obtained through the perception module; a pre-constructed first operating time calculation formula is used to obtain the first operating time of the temperature control device based on the temperature data, and a pre-constructed second operating time calculation formula is used to obtain the second operating time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein, the first operating time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; the start and stop of the temperature control device is controlled according to the first operating time, and the start and stop of the wind pressure device is controlled according to the second operating time. The above-mentioned energy management method for the manufacturing workshop can predict the accurate equipment operating time according to the actual manufacturing workshop environment through the regression fitting operating time calculation formula, thereby realizing precise control of the start and stop of the equipment and effectively improving energy utilization efficiency.

[0091] The energy management method for a manufacturing workshop provided in the embodiment of the present application corresponds to the energy management system for a manufacturing workshop provided in the above embodiment, and has the same functional modules and beneficial effects. To avoid repetition, they will not be described here.

[0092] Embodiment 5 like Figure 6 As shown, an embodiment of the present application also provides an execution device 600, including a processor 601, a memory 602, and a program or instruction stored in the memory 602 and executable on the processor 601. When the program or instruction is executed by the processor 601, each process of the energy management method embodiment of the above-mentioned manufacturing workshop is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0093] It should be noted that the execution device in the embodiment of the present application includes a mobile execution device and a non-mobile execution device.

[0094] Embodiment 6 An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the energy management method embodiment of the above-mentioned manufacturing workshop is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0095] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0096] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0098] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0099] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. An energy management system for a manufacturing workshop, characterized in that: The system comprises: A sensing module is used to obtain temperature data of the manufacturing workshop, as well as temperature difference data and humidity difference data inside and outside the manufacturing workshop; a calculation unit connected to the sensing module, and configured to use a pre-constructed first operation time calculation formula to obtain a first operation time of the temperature control device based on the temperature data, and to use a pre-constructed second operation time calculation formula to obtain a second operation time of the wind pressure device based on the temperature difference data and the humidity difference data; wherein the first operation time calculation formula is obtained by regression fitting calculation based on historical temperature data of the manufacturing workshop, and the second operation time calculation formula is obtained by regression fitting calculation based on historical temperature difference data and historical humidity difference data of the manufacturing workshop; a control unit connected to the calculation unit, and configured to control the start and stop of the temperature control device according to the first operation duration, and control the start and stop of the wind pressure device according to the second operation duration; The temperature control device is arranged in the manufacturing workshop and connected to the control unit to perform start and stop operations; The wind pressure equipment is arranged in a manufacturing workshop and connected to the control unit for executing start and stop operations.

2. The energy management system for a manufacturing workshop according to claim 1, characterized in that: The temperature data is collected by temperature sensors arranged at at least two positions; the pre-constructed first operation time calculation formula includes a corresponding first regression coefficient and a first constant; the temperature difference data is collected by temperature sensors arranged inside and outside the manufacturing workshop, and the humidity difference data is collected by humidity sensors arranged inside and outside the manufacturing workshop; the pre-constructed second operation time calculation formula includes a corresponding second regression coefficient and a second constant; Accordingly, the computing unit is specifically used for: Multiplying the temperature data by the corresponding first regression coefficient to obtain a plurality of summing factors; Summing the plurality of the summing factors and the first constant to obtain a first running time; Accordingly, the computing unit is specifically used for: Multiplying the temperature difference data and the humidity difference data with the corresponding second regression coefficient to obtain two addition factors; The two addition factors and the second constant are summed to obtain a second running time.

3. The energy management system for a manufacturing workshop according to claim 2, characterized in that: The computing unit is further used for: After the temperature control device stops, adjusting a first regression coefficient in the first operating time calculation formula according to an actual operating time of the temperature control device and the first operating time; After the wind pressure device stops, the second regression coefficient in the second operating time calculation formula is adjusted according to the actual operating time of the wind pressure device and the second operating time.

4. The energy management system for a manufacturing workshop according to claim 3, characterized in that: The computing unit is further used for: After the temperature control device stops, determining a first error ratio according to the actual operating time of the temperature control device and the first operating time, and after the wind pressure device stops, determining a second error ratio according to the actual operating time of the wind pressure device and the second operating time; When the first error ratio does not exceed the error ratio threshold, determining a first adjustment coefficient according to the first error ratio, and multiplying the first regression coefficient in the first running time calculation formula by the first adjustment coefficient to obtain an adjusted first regression coefficient; and / or, When the second error ratio does not exceed the error ratio threshold, a second adjustment coefficient is determined according to the second error ratio, and the second regression coefficient in the second running time calculation formula is multiplied by the second adjustment coefficient to obtain an adjusted second regression coefficient.

5. The energy management system for a manufacturing workshop according to claim 3, characterized in that: The computing unit is further used for: After the temperature control device stops, determining a first error ratio according to the actual operating time of the temperature control device and the first operating time, and after the wind pressure device stops, determining a second error ratio according to the actual operating time of the wind pressure device and the second operating time; When the first error ratio exceeds the error ratio threshold, determining a first adjustment coefficient according to the actual operating time of the temperature control device and the first preset maximum operating time, and multiplying the first regression coefficient in the first operating time calculation formula by the first adjustment coefficient to obtain an adjusted first regression coefficient; and / or, When the second error ratio exceeds the error ratio threshold, the second adjustment coefficient is determined according to the actual operating time of the wind pressure equipment and the second preset maximum operating time, and the second regression coefficient in the second operating time calculation formula is multiplied by the second adjustment coefficient to obtain the adjusted second regression coefficient.

6. The energy management system for a manufacturing workshop according to claim 1, characterized in that: The computing unit is further used for: When the temperature data is not within the first preset temperature range, executing a temperature data calibration strategy; and / or, After the temperature control device stops, if the time difference between the actual operating time of the temperature control device and the first operating time exceeds a preset time difference threshold, and the temperature data is not within a second preset temperature range, a temperature data calibration strategy is executed; wherein the second preset temperature range is a subset of the first preset temperature range.

7. The energy management system for a manufacturing workshop according to claim 6, characterized in that: The computing unit is further used for: Acquire historical temperature data of the location where the temperature data is located, and update and calibrate the temperature data according to the historical temperature data; or, Acquire temperature data of a location adjacent to the location where the temperature data is located, and update and calibrate the temperature data according to the temperature data of the adjacent location.

8. An energy management method for a manufacturing workshop, characterized in that: The method comprises: The temperature data of the manufacturing workshop, as well as the temperature difference data and humidity difference data inside and outside the manufacturing workshop are obtained through the sensing module; Using a pre-constructed first operating time calculation formula, a first operating time of the temperature control device is obtained based on the temperature data, and using a pre-constructed second operating time calculation formula, a second operating time of the wind pressure device is obtained based on the temperature difference data and the humidity difference data; wherein the first operating time calculation formula is obtained by regression fitting calculation based on the historical temperature data of the manufacturing workshop, and the second operating time calculation formula is obtained by regression fitting calculation based on the historical temperature difference data and the historical humidity difference data of the manufacturing workshop; The start and stop of the temperature control device are controlled according to the first operation duration, and the start and stop of the wind pressure device are controlled according to the second operation duration.

9. An execution device, characterized in that: It comprises a processor, a memory and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the energy management method for a manufacturing workshop as claimed in claim 8.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the energy management method for a manufacturing workshop as described in claim 8 are implemented.

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