Factory energy consumption management system and method based on big data

Abstract

The present invention discloses a factory energy consumption management system and method based on big data, and specifically relates to the technical field of factory energy consumption management, including: step 1: N groups of historical energy consumption data collected in the factory production area; N is an integer greater than zero; step 2: calculating the equipment evaluation coefficient, part quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to the N groups of historical energy consumption data; step 3: calculating the energy consumption control coefficient corresponding to the N groups of historical energy consumption data; step 4: setting an energy consumption mapping table, collecting energy consumption control data in real time, calculating the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtaining the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table; the present invention calculates the energy consumption loss coefficient of the spraying equipment, optimizes the operating parameters of the spraying equipment, reduces energy consumption loss, reduces production costs, and dynamically adjusts the energy consumption of the spraying equipment to achieve more efficient energy consumption utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of factory energy consumption management, and more specifically, to a factory energy consumption management system and method based on big data. Background Art

[0002] Factory energy consumption refers to the total amount of various energy consumed in the factory production process, including but not limited to electricity, heat, gas and fuel oil. With the rapid development of Industry 4.0 and intelligent manufacturing, the production efficiency and production capacity of factories have been significantly improved. However, the energy consumption problem has become more and more prominent. Factory energy consumption management has become a focus of major companies and scientific research institutions. Traditional factory energy consumption management usually relies on manual statistics and experience judgment, which is not only inefficient, but also difficult to accurately grasp energy consumption data, and cannot achieve refined management and optimized control. The existing production task optimization plan is optimized by intelligently calculating the abnormal energy consumption depreciation of a certain equipment, the maintenance parameters of a certain equipment or the damage and aging rate of the equipment, the real-time line loss power value of the factory, the energy consumption output ratio of a certain equipment, and the ratio of factory equipment. The above technical effects are achieved, so that the system can help the factory to manage the factory accurately and efficiently. For example, the Chinese patent application with publication number CN117908484A discloses an energy consumption detection and analysis system for a smart factory. Although the above method realizes the management of factory energy consumption, the inventor has found through research and application of the above method and the prior art that the above method and the prior art have at least the following defects:

[0003] However, the above-mentioned technology and the existing technology do not take into account the different energy consumption values ​​required for parts in different production stages, which makes it impossible for the equipment to accurately process parts during operation. In addition, due to changes in the production stage, the energy consumption value may not match the required energy consumption value, resulting in poor part quality.

[0004] To this end, the present invention provides a factory energy consumption management system and method based on big data. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a factory energy consumption management system and method based on big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, in a first aspect, the present invention provides the following technical solution: a factory energy consumption management method based on big data, comprising:

[0007] Step 1: N groups of historical energy consumption data collected in the factory production area; the historical energy consumption data include equipment energy consumption data, automobile parts data, spraying material data and spraying environment data; N is an integer greater than zero;

[0008] Step 2: Calculate the equipment evaluation coefficient, parts quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data;

[0009] Step 3: Calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data;

[0010] Step 4: Set up an energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table;

[0011] Step 5: Calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to be adjusted to the operating energy consumption value, and set the target spraying quality data;

[0012] Step 6: Preset the threshold of the equipment evaluation coefficient, collect the spraying quality data in real time during the spraying process of automobile parts, calculate the corresponding equipment evaluation coefficient, analyze the calculated equipment evaluation coefficient, and determine whether to generate an abnormal energy consumption instruction; if an abnormal energy consumption instruction is generated, calculate a new operating energy consumption value based on the current spraying quality data, and control the energy consumption value of the spraying equipment to the new operating energy consumption value;

[0013] Step 7: Calculate the target equipment evaluation coefficient based on the target spraying quality data, compare and analyze the calculated equipment evaluation coefficient with the target equipment evaluation coefficient to determine whether the spraying of automobile parts is completed. If the spraying is completed, control the spraying equipment to stop running.

[0014] Furthermore, the equipment energy consumption data includes spraying power, compressed air power and drying power of the spraying equipment; the calculation method of the equipment evaluation coefficient corresponding to N groups of historical energy consumption data includes:

[0015] Sp n =(Wp n +Wy n +Wh n )×Ys;

[0016] Where, Sp n Indicates the equipment evaluation coefficient corresponding to the nth group of historical energy consumption data, Wp n Indicates the spraying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wy n Indicates the compressed air power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wh n It represents the drying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Ys is the operating time of the spraying equipment, n is the group number of the historical energy consumption data, n=1, 2, ..., N.

[0017] Further, the automobile parts data includes automobile parts area and spraying quality data; the spraying quality data includes spraying flatness and spraying thickness;

[0018] The calculation method of the part quality coefficient corresponding to N groups of historical energy consumption data includes:

[0019]

[0020] In the formula, Lp n is the part mass coefficient, Lm n is the area of ​​automobile parts, Pz n is the spraying flatness, Ph n is the spraying thickness, Pn n is the spraying viscosity, β1 and β2 are preset weight factors, and both β1 and β2 are greater than zero.

[0021] Further, the spraying material data includes paint flow rate and mixing ratio data;

[0022] The calculation methods for material loss coefficients corresponding to N groups of historical energy consumption data include:

[0023] W n =Yq n ×γ1+Hb n ×γ2

[0024] Where Wn n is the material loss coefficient, Yq n is the paint flow rate, Hb n is the mixed ratio data, γ1 and γ2 are preset weight factors, and both γ1 and γ2 are greater than zero.

[0025] Furthermore, the spraying environment data includes spraying environment temperature, spraying environment humidity and air cleanliness;

[0026] The calculation method of the environmental assessment coefficient corresponding to N groups of historical energy consumption data includes:

[0027]

[0028] In the formula, Hj n is the environmental assessment coefficient, Hw n is the spraying environment temperature, Hs n is the spraying environment humidity, Hq n is the air cleanliness, C is the preset weight factor, and C is greater than zero.

[0029] Furthermore, the energy consumption control coefficient calculation method corresponding to N groups of historical energy consumption data includes:

[0030]

[0031] Where Hw n is the energy consumption control coefficient, θ1 is the weight factor of the equipment evaluation coefficient, θ2 is the weight factor of the parts quality coefficient, θ3 is the weight factor of the material loss coefficient, θ4 is the weight factor of the environmental evaluation coefficient, and ln(·) is the logarithmic function.

[0032] Furthermore, the energy consumption mapping table includes an energy consumption control coefficient and an energy consumption value corresponding to the energy consumption control coefficient;

[0033] The calculation method of energy loss coefficient of spraying equipment includes:

[0034]

[0035] Wherein, Sx is the energy loss coefficient, Hp is the spraying environment value, Zx is the operation cycle, μ1 and μ2 are preset weight factors, and μ1 and μ2 are both greater than zero, and Kc is a preset constant greater than zero;

[0036] The calculation method of operating energy consumption value includes:

[0037] Yxh=Ns×(Sx+1);

[0038] In the formula, Yxh is the operating energy consumption value, and Ns is the energy consumption value corresponding to the real-time energy consumption control data.

[0039] Furthermore, the method for determining whether to generate an abnormal energy consumption instruction includes:

[0040] Preset device evaluation coefficient thresholds Py1 and Py2, and Py1>Py2;

[0041] Compare and analyze the equipment evaluation coefficient with the corresponding equipment evaluation coefficient threshold;

[0042] During a spraying process, if the equipment evaluation coefficient changes to be less than or equal to Py1 or Py2 for the first time, an abnormal energy consumption instruction is generated;

[0043] If the device evaluation coefficient does not change for the first time to be less than or equal to Py1 or Py2, no energy consumption exception instruction is generated.

[0044] Further, the method for judging whether the spraying of automobile parts is finished includes:

[0045] If the equipment evaluation factor is greater than the target equipment evaluation factor, the spraying of the automobile parts continues;

[0046] If the equipment evaluation coefficient is less than or equal to the target equipment evaluation coefficient, the spraying of the automobile parts is completed.

[0047] In a second aspect, the present invention provides a factory energy consumption management system based on big data, which is used to implement the above-mentioned factory energy consumption management method based on big data, including:

[0048] Data collection module: used to collect N groups of historical energy consumption data in the factory production area; the historical energy consumption data includes equipment energy consumption data, automobile parts data, spraying material data and spraying environment data; N is an integer greater than zero;

[0049] The first calculation module is used to calculate the equipment evaluation coefficient, part quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data;

[0050] The second calculation module is used to calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data;

[0051] Mapping module: used to set the energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table;

[0052] Analysis module: used to calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to adjust to the operating energy consumption value, and set the target spraying quality data;

[0053] The first judgment module: presets the threshold of the equipment evaluation coefficient, collects the spraying quality data in real time during the spraying process of automobile parts, calculates the corresponding equipment evaluation coefficient, analyzes the calculated equipment evaluation coefficient, and determines whether to generate an abnormal energy consumption instruction; if an abnormal energy consumption instruction is generated, a new operating energy consumption value is calculated according to the current spraying quality data, and the energy consumption value of the spraying equipment is controlled to be the new operating energy consumption value;

[0054] The second judgment module: calculates the target equipment evaluation coefficient according to the target spraying quality data, compares and analyzes the calculated equipment evaluation coefficient with the target equipment evaluation coefficient, and determines whether the spraying of automobile parts is completed. If the spraying is completed, the spraying equipment is controlled to stop running.

[0055] Technical effects and advantages of the present invention:

[0056] 1. The present invention takes automobile parts spraying as an example, calculates the energy consumption control coefficient of the spraying equipment through equipment energy consumption data, automobile parts data, spraying material data and spraying environment data, and obtains the corresponding energy consumption value according to the energy consumption mapping table to perform the spraying operation. When the spraying equipment is spraying the automobile parts, the spraying quality data of the automobile parts is monitored in real time to determine whether the spraying stage of the automobile parts has changed, and the energy consumption value is intelligently adjusted according to the change of the spraying stage to ensure that the coating of the automobile parts is uniform and to avoid the poor coating quality of the automobile parts due to excessive or insufficient energy consumption of the spraying equipment.

[0057] 2. The present invention calculates the energy loss coefficient of the spraying equipment, optimizes the operating parameters of the spraying equipment, reduces energy loss, and reduces production costs. According to the energy loss coefficient and the operating energy consumption value, the energy consumption of the spraying equipment is dynamically adjusted to achieve more efficient energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a factory energy consumption management method based on big data according to Example 1;

[0059] Figure 2 This is a schematic diagram of the structure of a factory energy consumption management system based on big data according to Example 2;

[0060] Figure 3 This is a schematic diagram of an electronic device according to Embodiment 3;

[0061] Figure 4 A schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and a similar second unit may be referred to as a first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0065] Example 1

[0066] See also Figure 1 As shown, this embodiment discloses a factory energy consumption management method based on big data, including:

[0067] Step 1: N groups of historical energy consumption data collected in the factory production area; the historical energy consumption data include equipment energy consumption data, automobile parts data, spraying material data and spraying environment data; N is an integer greater than zero;

[0068] It should be noted that: the factory production area refers to the area used for spraying automobile parts; the present invention takes the spraying of automobile parts as an example to manage factory energy consumption.

[0069] The equipment energy consumption data includes the spraying power, compressed air power and drying power of the spraying equipment; it should be noted that: the spraying equipment includes a spraying system, an air compression system and a drying system, which are used to complete the spraying operation of automotive parts; the spraying power refers to the electric power required for the spraying equipment to perform the spraying operation per unit time; the compressed air power refers to the electric power required for the compressor in the spraying equipment per unit time; the drying power refers to the electric power required for the paint drying operation of automotive parts after spraying per unit time.

[0070] The automobile parts data includes automobile parts area and spraying quality data; the spraying quality data includes spraying flatness and spraying thickness. The automobile parts area is obtained by a camera installed in the spraying equipment. The camera collects automobile parts images and analyzes them to obtain the corresponding automobile parts area. The spraying flatness in the spraying quality data is monitored by a laser scanner or optical sensor installed on the spraying equipment; the spraying thickness is obtained by ultrasonic sensors, X-ray fluorescence technology or conductivity measurement; the automobile parts data will affect the energy consumption of the spraying equipment and the loss of spraying materials. The larger the spraying thickness value, the greater the corresponding energy consumption. In addition to the spraying thickness, the larger the other data values ​​in the automobile parts data, the greater the corresponding energy consumption.

[0071] The spray material data includes paint flow and mixing ratio data, the paint flow is obtained by a flow sensor installed in the spray liquid passage; the mixing ratio data is the mixing ratio of paint, fixative and diluent, which is pre-stored in the system database of the spray equipment; the spray material data will affect the spraying effect of the spray equipment on automobile parts, and a higher paint flow and a larger mixing ratio data will increase energy consumption.

[0072] The spraying environment data includes spraying environment temperature, spraying environment humidity and air cleanliness. The environment temperature data is obtained by a temperature sensor installed in the spraying equipment, the environment humidity data is obtained by a humidity sensor installed inside the spraying equipment, and the air cleanliness is obtained by an air quality sensor. The higher the spraying environment temperature and the spraying environment humidity, the lower the air cleanliness, which means that the air in the factory production area affects the uniformity and adhesion of the coating, which is not conducive to the spraying operation of spraying automotive parts.

[0073] Step 2: Calculate the equipment evaluation coefficient, parts quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data;

[0074] It should be noted that: a set of historical energy consumption data includes a set of equipment energy consumption data, a set of automobile parts data, a set of spraying material data and a set of spraying environment data; a set of equipment energy consumption data corresponds to an equipment evaluation coefficient, a set of automobile parts data corresponds to a parts quality coefficient, a set of spraying material data corresponds to a material loss coefficient, and a set of spraying environment data corresponds to an environmental evaluation coefficient;

[0075] The calculation method of the equipment evaluation coefficient corresponding to N groups of historical energy consumption data includes:

[0076] Sp n =(Wp n +Wy n +Wh n )×Ys;

[0077] Where, Sp n Indicates the equipment evaluation coefficient corresponding to the nth group of historical energy consumption data, Wp n Indicates the spraying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wy n Indicates the compressed air power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wh n represents the drying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Ys is the operating time of the spraying equipment, n is the group number of the historical energy consumption data, n=1, 2, ..., N;

[0078] The calculation method of the part quality coefficient corresponding to N groups of historical energy consumption data includes:

[0079]

[0080] In the formula, Lp n is the part mass coefficient, Lm n is the area of ​​automobile parts, Pz n is the spraying flatness, Ph n is the spraying thickness, Pn n is the spraying viscosity, ρ1 and ρ2 are preset weight factors, which are set by technicians in this field according to actual needs and experience, and both ρ1 and β2 are greater than zero;

[0081] The calculation methods for material loss coefficients corresponding to N groups of historical energy consumption data include:

[0082] W n =Yq n ×Y1+Hb n ×γ2

[0083] Where Wn n is the material loss coefficient, Yq n is the paint flow rate, Hb n is the mixed ratio data, Y1 and γ2 are preset weight factors, which are set by technicians in this field according to actual needs and experience, and both γ1 and γ2 are greater than zero;

[0084] The calculation method of the environmental assessment coefficient corresponding to N groups of historical energy consumption data includes:

[0085]

[0086] In the formula, Hj n is the environmental assessment coefficient, Hw n is the spraying environment temperature, Hs n is the spraying environment humidity, Hq n is the air cleanliness, C is the preset weight factor, and C is greater than zero.

[0087] Step 3: Calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data;

[0088] The calculation method of energy consumption control coefficient corresponding to N groups of historical energy consumption data includes:

[0089]

[0090] Where Hw n is the energy consumption control coefficient, θ1 is the weight factor of the equipment evaluation coefficient, θ2 is the weight factor of the parts quality coefficient, θ3 is the weight factor of the material loss coefficient, θ4 is the weight factor of the environmental evaluation coefficient, and ln(·) is the logarithmic function;

[0091] It should be noted that: the formulas in step 2 are substituted into the formulas in step 3. The preset weight factors in the formulas in step 3 are collected by technical personnel in this field by collecting multiple groups of comprehensive parameters, and setting corresponding weights for each group of comprehensive parameters. The preset weights and the collected comprehensive parameters are substituted into the formulas. Any five formulas constitute a set of four-variable linear equations. The calculated weights are screened and averaged to obtain the values ​​of θ1, θ2, θ3 and θ4.

[0092] Step 4: Set up an energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table;

[0093] It should be noted that the energy consumption mapping table is stored in the database and is a two-dimensional mapping table. The energy consumption mapping table includes an energy consumption control coefficient and an energy consumption value corresponding to the energy consumption control coefficient. According to the calculated energy consumption control coefficient, the corresponding energy consumption value can be obtained through the energy consumption mapping table.

[0094] The method for setting the energy consumption mapping table is as follows: under an experimental environment, the staff calculates the energy consumption control coefficients corresponding to N groups of historical energy consumption data through the above step 3, sets different energy consumption values ​​for one energy consumption control coefficient in turn and performs spraying processing, and records the spraying effects of automobile parts in turn, and uses the energy consumption value corresponding to the best spraying effect as the energy consumption value corresponding to the group of energy consumption control coefficients, and obtains the nth energy consumption value corresponding to the nth energy consumption control coefficient in turn according to the process; sets the energy consumption mapping table with the nth energy consumption control coefficient and the nth energy consumption value corresponding to the nth energy consumption control coefficient.

[0095] Step 5: Calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to be adjusted to the operating energy consumption value, and set the target spraying quality data;

[0096] The calculation method of energy loss coefficient of spraying equipment includes:

[0097]

[0098] Wherein, Sx is the energy loss coefficient, Hp is the spraying environment value, Zx is the operation cycle, μ1 and μ2 are preset weight factors, and μ1 and μ2 are both greater than zero, and Kc is a preset constant greater than zero;

[0099] The preset weight factors and preset constants in the formula are collected by a technician in this field for multiple sets of comprehensive parameters, and corresponding weights and constants are set for each set of comprehensive parameters. The preset weights and constants and the collected comprehensive parameters are substituted into the formula, and any three formulas constitute a three-variable linear equation system. The calculated weights and constants are screened and averaged to obtain the values ​​of μ1, μ2 and Kc;

[0100] It should be noted that the spraying environment value is a pre-assignment of the operating environment of the spraying equipment. A lower value is assigned to a poor operating environment, and a higher value is assigned to a better operating environment. A better operating environment includes, for example, suitable temperature, moderate humidity, and high air cleanliness.

[0101] It should be understood that during the use of the spraying equipment, due to the influence of the operating environment and the operating cycle, the energy consumption will gradually increase, resulting in a decrease in the spraying quality of automobile parts. The energy consumption value between the spraying equipment and the automobile parts will be less than the set energy consumption value, that is, the larger the spraying environment value, the better the operating environment of the spraying equipment, the smaller the energy loss coefficient, and the longer the operating cycle of the spraying equipment, the greater the energy loss coefficient;

[0102] The calculation method of operating energy consumption value includes:

[0103] Yxh=Ns×(Sx+1);

[0104] In the formula, Yxh is the operating energy consumption value, and Ns is the energy consumption value corresponding to the real-time energy consumption control data;

[0105] The target spraying quality data is the spraying quality data that the automobile parts need to achieve after the spraying process. The target spraying quality data is input into the spraying equipment in advance by the staff.

[0106] Step 6: Preset the threshold of the equipment evaluation coefficient, collect the spraying quality data in real time during the spraying process of automobile parts, calculate the corresponding equipment evaluation coefficient, analyze the calculated equipment evaluation coefficient, and determine whether to generate an abnormal energy consumption instruction; if an abnormal energy consumption instruction is generated, calculate a new operating energy consumption value based on the current spraying quality data, and control the energy consumption value of the spraying equipment to the new operating energy consumption value;

[0107] The collection interval of real-time spraying quality data during the spraying process of automobile parts is T seconds. The collection interval is set by the staff based on the spraying quality data before spraying and the target spraying quality data. The purpose of setting the collection interval is to reduce the redundancy of real-time spraying quality data and to effectively detect the spraying quality data.

[0108] The method for determining whether to generate an abnormal energy consumption instruction includes:

[0109] Preset device evaluation coefficient thresholds Py1 and Py2, and Py1>Py2;

[0110] Compare and analyze the equipment evaluation coefficient with the corresponding equipment evaluation coefficient threshold;

[0111] During a spraying process, if the equipment evaluation coefficient changes to less than or equal to Py1 or Py2 for the first time, an abnormal energy consumption instruction is generated, indicating that the spraying quality data has reached the requirements of the current spraying stage, the spraying stage has changed, and the next stage of spraying processing is required, so the energy consumption value needs to be changed;

[0112] If the equipment evaluation coefficient does not change for the first time to be less than or equal to Py1 or Py2, no abnormal energy consumption instruction is generated, indicating that the spraying quality data does not meet the requirements of the current spraying stage, the spraying stage has not changed, and there is no need to change the energy consumption value;

[0113] It should be noted that, according to the spraying quality data, the automobile parts spraying process is divided into three spraying stages: rough spraying, semi-fine spraying and fine spraying. The goal of the rough spraying stage is to quickly form a base coating on the surface of the parts; the goal of the semi-fine spraying stage is to further correct and optimize the coating quality on the base coating. The coating quality is improved compared with the rough spraying stage, but further coating treatment and trimming may still be required; the goal of the fine spraying stage is to achieve the final high-quality coating surface; the energy consumption values ​​corresponding to different spraying stages are different, but the energy consumption values ​​corresponding to the same spraying stage are the same; therefore, the equipment evaluation coefficient threshold includes two thresholds for judging whether the spraying stage has changed; for example, the energy consumption values ​​corresponding to the rough spraying stage are all 1, the energy consumption values ​​corresponding to the semi-fine spraying stage are all 2, and the energy consumption values ​​corresponding to the fine spraying stage are all 3;

[0114] It should be understood that the threshold value of the equipment evaluation coefficient is set by the staff according to the spraying quality data before spraying and the target spraying quality data, and different spraying quality data before spraying and target spraying quality data correspond to different equipment evaluation coefficient threshold values.

[0115] Step 7: Calculate the target equipment evaluation coefficient according to the target spraying quality data, compare and analyze the calculated equipment evaluation coefficient with the target equipment evaluation coefficient, and judge whether the spraying of automobile parts is completed. If the spraying is completed, control the spraying equipment to stop running;

[0116] Methods for judging whether the spraying of automobile parts is completed include:

[0117] If the equipment evaluation coefficient is greater than the target equipment evaluation coefficient, the spraying of the automobile parts continues, indicating that the spraying quality data of the automobile parts does not meet the requirements at this time and the spraying process needs to be continued;

[0118] If the equipment evaluation coefficient is less than or equal to the target equipment evaluation coefficient, the spraying of the automobile parts is finished; this means that the spraying quality data of the automobile parts has met the requirements and there is no need to continue the spraying process;

[0119] It should be noted that the calculation method of the target equipment evaluation coefficient is consistent with the calculation method of the equipment evaluation coefficient; the spraying equipment no longer sprays automotive parts, but completes the spraying operation for the spraying equipment, and there is no energy consumption management at this time.

[0120] This embodiment calculates the energy consumption control coefficient of the spraying equipment through the equipment energy consumption data, automobile parts data, spraying material data and spraying environment data, and obtains the corresponding energy consumption value according to the energy consumption mapping table to perform the spraying operation. When the spraying equipment is spraying automobile parts, the spraying quality data of the automobile parts is monitored in real time to determine whether the spraying stage of the automobile parts has changed, and the energy consumption value is intelligently adjusted according to the change of the spraying stage to ensure that the coating of the automobile parts is uniform and to avoid poor coating quality of the automobile parts due to excessive or insufficient energy consumption of the spraying equipment.

[0121] Example 2

[0122] See also Figure 2 As shown, this embodiment provides a factory energy consumption management system based on big data, including:

[0123] Data collection module: used to collect N groups of historical energy consumption data in the factory production area; the historical energy consumption data includes equipment energy consumption data, automobile parts data, spraying material data and spraying environment data; N is an integer greater than zero;

[0124] The first calculation module is used to calculate the equipment evaluation coefficient, part quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data;

[0125] The second calculation module is used to calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data;

[0126] Mapping module: used to set the energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table;

[0127] Analysis module: used to calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to adjust to the operating energy consumption value, and set the target spraying quality data;

[0128] The first judgment module: presets the threshold of the equipment evaluation coefficient, collects the spraying quality data in real time during the spraying process of automobile parts, calculates the corresponding equipment evaluation coefficient, analyzes the calculated equipment evaluation coefficient, and determines whether to generate an abnormal energy consumption instruction; if an abnormal energy consumption instruction is generated, a new operating energy consumption value is calculated according to the current spraying quality data, and the energy consumption value of the spraying equipment is controlled to be the new operating energy consumption value;

[0129] The second judgment module: calculates the target equipment evaluation coefficient according to the target spraying quality data, compares and analyzes the calculated equipment evaluation coefficient with the target equipment evaluation coefficient, and determines whether the spraying of automobile parts is completed. If the spraying is completed, the spraying equipment is controlled to stop running.

[0130] Example 3

[0131] See also Figure 3 As shown, this embodiment provides an electronic device 500. The electronic device 500 may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by one or more processors, the factory energy consumption management method based on big data as described above may be executed.

[0132] The system according to the embodiment of the present application can also be used Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the factory energy consumption management method based on big data provided by the present application. Furthermore, the electronic device 500 may also include a user interface 508. Of course, Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown.

[0133] Example 4

[0134] See also Figure 4 As shown, this embodiment provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the factory energy consumption management method based on big data of embodiment 1.

[0135] The formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The weight factors in the formula and the preset thresholds in the analysis process are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data; the size of the weight factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the weight factor depends on the amount of sample data and the corresponding processing coefficient initially set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0137] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A factory energy consumption management method based on big data, characterized in that: include: Step 1: Collect N sets of historical energy consumption data in the factory production area; N is an integer greater than zero; Step 2: Calculate the equipment evaluation coefficient, parts quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data; The historical energy consumption data includes equipment energy consumption data, automobile parts data, spray material data and spray environment data; the equipment energy consumption data includes spray power, compressed air power and drying power of the spray equipment; the calculation method of the equipment evaluation coefficient corresponding to N groups of historical energy consumption data includes: Sp n =(Wp n +Wy n +Wh n )×Ys; Where, Sp n Indicates the equipment evaluation coefficient corresponding to the nth group of historical energy consumption data, Wp n Indicates the spraying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wy n Indicates the compressed air power of the spraying equipment corresponding to the nth group of historical energy consumption data, Wh n Indicates the drying power of the spraying equipment corresponding to the nth group of historical energy consumption data, Ys is the operating time of the spraying equipment, n is the group number of the historical energy consumption data, n = 1, 2, ..., N; The automobile parts data include automobile parts area and spraying quality data; the spraying quality data include spraying flatness, spraying thickness and spraying viscosity; The calculation method of the part quality coefficient corresponding to N groups of historical energy consumption data includes: In the formula, Lp n is the part mass coefficient, Lm n is the area of ​​automobile parts, Pz n is the spraying flatness, Ph n is the spraying thickness, Pn n is the spraying viscosity, β1 and β2 are preset weight factors, and both β1 and β2 are greater than zero; The spraying material data includes paint flow rate and mixing ratio data; The calculation methods for material loss coefficients corresponding to N groups of historical energy consumption data include: Wn n =Yq n ×γ1+Hb n ×γ2; Where Wn n is the material loss coefficient, Yq n is the paint flow rate, Hb n is the mixed ratio data, γ1 and γ2 are preset weight factors, and both γ1 and γ2 are greater than zero; The spraying environment data includes spraying environment temperature, spraying environment humidity and air cleanliness; The calculation method of the environmental assessment coefficient corresponding to N groups of historical energy consumption data includes: In the formula, Hj n is the environmental assessment coefficient, Hw n is the spraying environment temperature, Hs n is the spraying environment humidity, Hq n is the air cleanliness, C is the preset weight factor, and C is greater than zero; Step 3: Calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data; The calculation method of energy consumption control coefficient corresponding to N groups of historical energy consumption data includes: In the formula, Hw n is the energy consumption control coefficient, Sp n Indicates the equipment evaluation coefficient corresponding to the nth group of historical energy consumption data, Lp n is the part quality factor, Wn n is the material loss coefficient, Hj n is the environmental assessment coefficient, θ1 represents the weight factor of the equipment assessment coefficient, θ2 represents the weight factor of the parts quality coefficient, θ3 represents the weight factor of the material loss coefficient, θ4 represents the weight factor of the environmental assessment coefficient, and ln(·) represents the logarithmic function; Step 4: Set up an energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table; Step 5: Calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to be adjusted to the operating energy consumption value, and set the target spraying quality data; The energy consumption mapping table includes an energy consumption control coefficient and an energy consumption value corresponding to the energy consumption control coefficient; The calculation method of energy loss coefficient of spraying equipment includes: Wherein, Sx is the energy loss coefficient, Hp is the spraying environment value, Zx is the operation cycle, μ1 and μ2 are preset weight factors, and μ1 and μ2 are both greater than zero, and Kc is a preset constant greater than zero; The calculation method of operating energy consumption value includes: Yxh=Ns×(Sx+1); In the formula, Yxh is the operating energy consumption value, Ns is the energy consumption value corresponding to the real-time energy consumption control data, and Sx is the energy loss coefficient; Step 6: Preset the threshold of the equipment evaluation coefficient, collect the spraying quality data in real time during the spraying process of automobile parts, calculate the corresponding equipment evaluation coefficient, analyze the calculated equipment evaluation coefficient, and determine whether to generate an abnormal energy consumption instruction; if an abnormal energy consumption instruction is generated, calculate a new operating energy consumption value based on the current spraying quality data, and control the energy consumption value of the spraying equipment to the new operating energy consumption value; Step 7: Calculate the target equipment evaluation coefficient based on the target spraying quality data, compare and analyze the calculated equipment evaluation coefficient with the target equipment evaluation coefficient to determine whether the spraying of automobile parts is completed. If the spraying is completed, control the spraying equipment to stop running.

2. The factory energy consumption management method based on big data according to claim 1 is characterized in that: The method for determining whether to generate an abnormal energy consumption instruction includes: Preset device evaluation coefficient thresholds Py1 and Py2, and Py1>Py2; Compare and analyze the equipment evaluation coefficient with the corresponding equipment evaluation coefficient threshold; During a spraying process, if the equipment evaluation coefficient changes to be less than or equal to Py1 for the first time, or changes to be less than or equal to Py2 for the first time, an abnormal energy consumption instruction is generated; If the device evaluation coefficient changes to be less than or equal to Py1 for a non-first time, or changes to be less than or equal to Py2 for a non-first time, no energy consumption exception instruction is generated.

3. The factory energy consumption management method based on big data according to claim 2 is characterized in that: Methods for judging whether the spraying of automobile parts is completed include: If the equipment evaluation factor is greater than the target equipment evaluation factor, the spraying of the automobile parts continues; If the equipment evaluation coefficient is less than or equal to the target equipment evaluation coefficient, the spraying of the automobile parts is completed.

4. A plant energy consumption management system based on big data, used to implement the plant energy consumption management method based on big data as claimed in any one of claims 1 to 3, characterized in that: include: Data collection module: used to collect N groups of historical energy consumption data in the factory production area; N is an integer greater than zero; The first calculation module is used to calculate the equipment evaluation coefficient, part quality coefficient, material loss coefficient and environmental evaluation coefficient corresponding to N groups of historical energy consumption data; The second calculation module is used to calculate the energy consumption control coefficient corresponding to N groups of historical energy consumption data; Mapping module: used to set the energy consumption mapping table, collect energy consumption control data in real time, calculate the energy consumption control coefficient corresponding to the real-time energy consumption control data, and obtain the corresponding energy consumption value according to the energy consumption control coefficient and the energy consumption mapping table; Analysis module: used to calculate the energy loss coefficient of the spraying equipment, calculate the operating energy consumption value according to the energy loss coefficient of the spraying equipment, control the energy consumption value of the spraying equipment to adjust to the operating energy consumption value, and set the target spraying quality data; The first judgment module: presets the threshold of the equipment evaluation coefficient, collects the spraying quality data in real time during the spraying process of automobile parts, calculates the corresponding equipment evaluation coefficient, analyzes the calculated equipment evaluation coefficient, and determines whether to generate an abnormal energy consumption instruction; If an abnormal energy consumption instruction is generated, a new operating energy consumption value is calculated based on the current spraying quality data, and the energy consumption value of the spraying equipment is controlled to be the new operating energy consumption value; The second judgment module: calculates the target equipment evaluation coefficient according to the target spraying quality data, compares and analyzes the calculated equipment evaluation coefficient with the target equipment evaluation coefficient, and determines whether the spraying of automobile parts is completed. If the spraying is completed, the spraying equipment is controlled to stop running.

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