Automatic treatment system and method for by-products generated in production and processing of spare and accessory parts of generator set

Abstract

The invention discloses an automatic processing system and method for by-products of parts and components of a generator set, and relates to the technical field of part and component machining. Sorting data and recycling data of metal excess materials in the part and component machining process are obtained; the sorting data is crusher parameter data; the relationship between the sorting data and metal oxidation and the relationship between metal oxidation and recovery data are analyzed, an optimization model of the recovery data is established, an optimal crushing granularity set value is found, and the metal excess materials are recovered; the relation between the crushing granularity and the metal recovery rate and the metal purity is established through metal oxidation, cooperative control is conducted on metal oxidation and the crushing granularity, the recovery rate and the purity are balanced, the optimal metal crushing granularity is determined, the metal recovery amount is increased, and remarkable production and environmental benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of spare parts processing, and specifically to an automated processing system and method for by-products of generating set spare parts production and processing. Background Art

[0002] With the increasing production and processing of generating set spare parts, the problem of by-products has gradually emerged. Generating set spare parts include metal components, and there may be metal scraps during the production process. In the process of metal scrap recycling, the crushing particle size of the crusher directly affects the recovery rate and metal purity. The traditional method aims to minimize the oxidation of metals. However, pursuing purity may lead to incomplete sorting, resulting in a decrease in the metal recovery rate and losses. Therefore, how to utilize the phenomenon of metal oxidation to improve the balance between metal scrap purity and recovery rate has become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of the present invention is to provide an automated processing system and method for by-products of generating set spare parts production and processing to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An automated processing system for by-products of generating set spare parts production and processing, including a data collection module, a data storage module, a data analysis module, and a recycling module; the output end of the data collection module is connected to the input ends of the data storage module and the data analysis module, and is used to obtain the recycling data and sorting data of metal scraps; the output end of the data storage module is connected to the input end of the data analysis module, and is used to store the historical sorting data and recycling data of metal scraps; the output end of the data analysis module is connected to the input end of the recycling module, and is used to analyze the influence of temperature and actual crushing particle size on the recycling data to find the optimal crushing particle size setting value; the recycling module is used to control the parameters of the crusher and sort and recycle the metal scraps.

[0005] The data collection module further includes a temperature detection unit, a purity analysis unit, and a particle size analysis unit; the temperature detection unit is used to detect the temperature of the crusher during the sorting process; the purity analysis unit is used to detect the purity of the recycled metal scraps; the particle size analysis unit is used to obtain the particle size data of the fragments generated after the metal passes through the crusher.

[0006] The data analysis module further includes a calculation unit, a regression unit, and an optimization unit; the calculation unit is used to establish a relational expression among purity, crushing particle size, and temperature, and determine the influence of crushing particle size and temperature on purity by solving the calculation; the regression unit is used to generate a regression model between the set value of crushing particle size and temperature, and generate a regression model between the set value of crushing particle size and the actual crushing particle size frequency; the optimization unit is used to calculate the optimization target under a specified set value of crushing particle size and determine the optimal set value of crushing particle size.

[0007] The recycling module further includes an input unit, a sorting unit, and a storage unit; the input unit is used to access the metal scraps during the processing of the generator set; the sorting unit is used to sort and recycle the metal scraps; the storage unit is used to store the recycled metal scraps.

[0008] To achieve the above object, the present invention provides the following technical solution: an automatic processing method for by-products of manufacturing generator set spare parts, including the following steps: Obtain the sorting data and recycling data of metal scraps during the processing of spare parts; the sorting data is the crusher parameter data. Analyze the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data, establish an optimization model for recycling data, find the optimal set value of crushing particle size, and recycle the metal scraps.

[0009] Specifically, the steps of analyzing the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data further include the following steps: Obtain the historical crusher parameter data during the processing of spare parts, including the set value of crushing particle size of the crusher and the crushing result of the metal scraps; the crushing result of the metal scraps is obtained by screening and separating the crushed samples, and the proportion of each particle size interval is statistically calculated to obtain the quantity of the metal scraps in each particle size interval after crushing under the set crushing particle size; the particle size interval is set according to the accuracy of the crusher; obtain the temperature of the crusher during operation under the set crushing particle size through a temperature sensor; obtain the recycling data detected after sorting the metal scraps, and the recycling data includes purity and recovery rate; analyze the recycling data and purity under the set value of crushing particle size of the same crusher to obtain the influence of the actual crushing particle size on the recycling data; analyze the recycling data and purity under the set value of crushing particle size of different crushers to obtain the influence of the crusher temperature on the recycling data.

[0010] Specifically, the steps of analyzing the recycling data and purity under the set value of crushing particle size of the same crusher to obtain the influence of the actual crushing particle size on the recycling data further include the following steps: Let D represent the set value of the crushing particle size of the crusher. Then, from historical data, multiple purity data under the set value D of the crushing particle size can be obtained. Let Pur1, Pur2, …, Purn represent the purity data of the 1st, 2nd, …, nth times respectively; establish the relationship among purity, actual crushing particle size, and temperature; Pur i =T×∑ j=1 n (W ij ×V j ), where n represents the number of set particle size intervals, T represents the influence of temperature on purity, W ij represents the proportion of the jth crushing particle size interval during the ith crushing under the set value D of the crushing particle size, V j represents the influence of the jth actual crushing particle size on purity, Pur i represents the purity during the ith crushing under the set value D of the crushing particle size; Pur i is obtained through purity analysis of the recycled metal scraps, W ij is obtained by screening and sorting the samples after crushing and then statistically obtaining the proportion of the actual crushing particle size; Simultaneously establish a system of equations for the relationships among purity, actual crushing particle size, and temperature under the same set value D of the crushing particle size: {Pur i =T×∑ j=1 n (W ij ×V j )}. By dividing the two relationships, the left side of the equation is a known quantity, and the right side is the influence of the actual crushing particle size on purity, and the influence of temperature on purity can be eliminated to obtain the relationship between V j ; unify all V j into one variable, and simultaneously establish a system of equations for the relationships among purity, actual crushing particle size, and temperature under different set values D of the crushing particle size: {Pur i =T×∑ j=1 n (W ij ×V j )}. Unify all V j on the right side of the relationship into one. By dividing the two relationships, the influence of different temperatures on purity can be obtained, and the influence of temperature on purity is also unified into one variable; take two relationships among purity, actual crushing particle size, and temperature for calculation, and the unified influence of temperature on purity and the influence of the actual crushing particle size on purity can be obtained, and then determine the influence of all temperatures on purity and the influence of the actual crushing particle size on purity according to the unified relationship. The treatment method for the recovery rate is the same as that for purity, and the influence of all temperatures on the recovery rate and the influence of the actual crushing particle size on the recovery rate are obtained.

[0011] Optionally, after multiple statistics at the set value D of the crushing particle size, the proportion of the actual crushing particle size is obtained, and the frequency of each actual crushing particle size at the set value D of the crushing particle size is obtained by taking the average value. The frequencies of the actual crushing particle sizes at different set values of the particle size are obtained, and a regression model of n actual crushing particle size frequencies is established; the input is the set value of the crushing particle size, the output is the frequency of the actual crushing particle size, and the regression parameters are solved by substituting the set value of the crushing particle size and the frequency of the actual crushing particle size. Obtain the temperature at the set value of the crushing particle size and the influence of the temperature on the purity. Take the temperatures at different set values of the crushing particle size as the input and the influence of the temperature on the purity as the output, and train the regression model of the temperature; obtain the influence of the specified temperature on the purity through the regression model. Similarly, train the regression model to obtain the influence of the specified temperature on the recovery rate.

[0012] Specifically, the steps for establishing the optimization model of the recovery data and finding the optimal set value of the crushing particle size further include the following: Establish the objective function of the crushing parameters M(x) = P(x) × H(x), where P(x) represents the purity when the set value of the crushing particle size is x, H(x) represents the recovery rate when the crushing particle size is x, x represents the set value of the crushing particle size of the crusher, and M(x) represents the optimization objective when the crushing particle size is x.

[0013] Determine the crushing range [a, b] of the crusher. a represents the lower limit of the crushing range, and b represents the upper limit of the crushing range. Within the crushing range of the crusher, according to the accuracy of the crusher, set the step size step. Starting from the set value a of the crushing particle size of the crusher, according to the influence of the temperature and the actual crushing particle size on the purity when the crushing particle size is a, obtain the purity under the actual crushing particle size; use the regression model of n actual crushing particle size frequencies to obtain the frequencies of n actual crushing particle sizes when the set value of the crushing particle size is a, and use the frequencies as weights to perform weighted summation to obtain the purity data of the recovered metal when the crushing particle size is a. The analysis method of the recovery rate is the same as that of the purity. In the same way, obtain the recovery rate data of the recovered metal when the crushing particle size is a; based on the purity and the recovery rate, obtain the optimization objective when the set value of the crushing particle size is a; increase the step size on the basis of a to obtain the optimization objectives at different set values of the crushing particle size; select the one with the highest score from all the optimization objectives as the optimal set value of the crushing particle size.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: By establishing the relationship between the crushing particle size and the metal recovery rate and metal purity through metal oxidation, synergistically controlling the metal oxidation and the crushing particle size, balancing the recovery rate and the purity, determining the optimal metal crushing particle size, and increasing the recovery amount of the metal, which has significant production and environmental benefits. Description of the Drawings

[0015] Figure 1This is a schematic structural diagram of an automatic processing system for by-products of generator set spare parts in the production process of the present invention. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment: As Figure 1 shown, the present invention provides a technical solution, an automatic processing system for by-products of generator set spare parts, including a data collection module, a data storage module, a data analysis module and a recycling module; the output end of the data collection module is connected to the input ends of the data storage module and the data analysis module, and is used to obtain the recycling data and sorting data of metal scraps; the output end of the data storage module is connected to the input end of the data analysis module, and is used to store the historical sorting data and recycling data of metal scraps; the output end of the data analysis module is connected to the input end of the recycling module, and is used to analyze the influence of temperature and actual crushing particle size on the recycling data to find the optimal crushing particle size setting value; the recycling module is used to control the parameters of the crusher and sort and recycle the metal scraps.

[0018] The data collection module further includes a temperature detection unit, a purity analysis unit and a particle size analysis unit; the temperature detection unit is used to detect the temperature of the crusher during the sorting process; the purity analysis unit is used to detect the purity of the recycled metal scraps; the particle size analysis unit is used to obtain the particle size data of the fragments generated after the metal passes through the crusher.

[0019] The data analysis module further includes a calculation unit, a regression unit and an optimization unit; the calculation unit is used to establish a relationship formula between purity, crushing particle size and temperature, and determine the influence of crushing particle size and temperature on purity through solution calculation; the regression unit is used to generate a regression model between the crushing particle size setting value and temperature, and generate a regression model between the crushing particle size setting value and the actual crushing particle size frequency; the optimization unit is used to calculate the optimization target under a specified crushing particle size setting value and determine the optimal crushing particle size setting value.

[0020] The recycling module further includes an input unit, a sorting unit and a storage unit; the input unit is used to access the metal scraps in the generator set processing process; the sorting unit is used to sort and recycle the metal scraps; the storage unit is used to store the recycled metal scraps.

[0021] Embodiment: The present invention provides a technical solution, an automatic processing method for by-products of generating set spare parts, including the following steps: Obtain the sorting data and recycling data of metal scraps during the processing of spare parts; the sorting data is the crusher parameter data; Analyze the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data, establish an optimization model for recycling data, find the optimal crushing particle size setting value, and perform recycling treatment on the metal scraps.

[0022] The analysis of the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data further includes the following steps: Obtain the historical crusher parameter data during the processing of spare parts, including the crushing particle size setting value of the crusher and the crushing result of the metal scraps; the crushing result of the metal scraps is obtained by screening and separating the crushed samples, counting the proportion of each particle size interval, and obtaining the quantity of the metal scraps after crushing in each particle size interval under the set crushing particle size; the particle size interval is set according to the accuracy of the crusher; obtain the temperature of the crusher during operation under the set crushing particle size through a temperature sensor; obtain the recycling data detected after the sorting of the metal scraps, and the recycling data includes purity and recovery rate; analyze the recycling data and purity under the same crushing particle size setting value of the crusher to obtain the influence of the actual crushing particle size on the recycling data; analyze the recycling data and purity under different crushing particle size setting values of the crusher to obtain the influence of the crusher temperature on the recycling data.

[0023] Due to the influence of differences in the characteristics of metal scraps, wear of crusher tools, fluctuations in industrial parameters, impurities, etc., the crushing error of the metal scraps after crushing is inevitable, and usually fluctuates around the crushing particle size setting value of the crusher. Divide the crushing particle size into several particle size intervals according to the fluctuation data of the historical crushing particle size and the accuracy of the crusher; The spare parts of the generator include metal parts, so metal waste will be generated during the processing, and these metal wastes can be recycled and reused, which involves sorting, smelting and testing steps; the sorting step is used to separate different types of metals and impurities, and send the separated metals to subsequent processing for reuse; in the sorting process, the metal is crushed. The surface area of ​​the metal will increase during crushing, and the contact area with the air will also increase. The metal can be exposed to more oxygen, which increases the degree of oxidation of the metal, thereby reducing the purity of the recovered metal. Therefore, the purity of the metal will be affected by the crushing particle size; at the same time, the machine will generate heat when sorting the metal, and the heat generated will promote the reaction between oxygen and metal and increase the degree of oxidation; and the heat generated by the machine during sorting is related to the set crushing particle size. The smaller the crushing particle size, the more energy is required and the more heat the machine generates. Therefore, the temperature of the machine will also affect the purity of the metal.

[0024] The analysis of the recovery data and purity under the same crusher crushing particle size setting value to obtain the influence of the actual crushing particle size on the recovery data also includes the following steps: Let D represent the crushing particle size setting value of the crusher. From the historical data, we can obtain multiple purity data under the crushing particle size setting value D. Let Pur1, Pur2, ..., Purn represent the purity data of the 1st, 2nd, ..., nth times respectively. Establish the relationship between purity, actual crushing particle size and temperature. Pur i =T×∑ j=1 n (W ij ×V j ), n represents the number of particle size intervals set, T represents the effect of temperature on purity, W ij V represents the proportion of the jth crushing size interval during the i-th crushing under the crushing size setting value D. j Indicates the influence of the jth actual crushing particle size on purity, Pur i Pur represents the purity of the i-th crushing under the crushing particle size setting value D; i The purity of the recovered metal residues is analyzed, W ij By screening and selecting the crushed samples, the actual crushing particle size ratio is obtained through statistics; The relationship between purity, actual crushing size and temperature under the same crushing size setting value D is combined to establish the equation group: {Pur i =T×∑ j=1 n (W ij ×V j )}, by dividing the two equations, the left side of the equation is the known quantity, and the right side of the equation is the effect of the actual crushing particle size on purity, which can eliminate the effect of temperature on purity and obtain Vj The relationship between j All are unified into one variable, and the relationship between purity, actual crushing size and temperature under different crushing size setting values ​​is established to establish an equation group: {Pur i =T×∑ j=1 n (W ij ×V j )}, replace V on the right side of the relationship j All are unified into one. By dividing the two relationship equations, we can get the influence of different temperatures on purity, and unify the influence of temperature on purity into one variable. Taking the relationship equation between the two purities, the actual crushing particle size and the temperature for calculation, we can get the influence of the unified temperature on purity and the influence of the actual crushing particle size on purity, and then determine the influence of all temperatures on purity and the influence of the actual crushing particle size on purity according to the unified relationship.

[0025] Metal oxidation caused by increased surface area and temperature will reduce the purity of the recovered metal; however, on the other hand, metal sorting usually relies on differences in physical properties, such as density, magnetism, conductivity, etc. Oxidation changes the properties of the metal surface and affects the sorting effect. For example, metal oxides may have different conductivity or magnetism, making them easier to separate during sorting. Metallic iron is oxidized to produce ferroferric oxide with strong magnetism. If other metals are not magnetic, the recovery rate of metallic iron sorting can be improved.

[0026] The actual crushing particle size is expanded into an interval, and one actual crushing particle size corresponds to one crushing particle size interval. A single crushing particle size setting value may not involve all particle size intervals. For example, if the crushing particle size setting value is 10mm, even if there is an error, metal fragments with a crushing particle size in the crushing particle size interval of [2,2.5]mm will not be generated. Therefore, recovery data under multiple setting values ​​are required.

[0027] After multiple statistics under the crushing particle size setting value D, the proportion of actual crushing particle sizes is obtained, and the average value is taken to obtain the frequency of each actual crushing particle size under the crushing particle size setting value D, and the frequency of actual crushing particle sizes under different particle size setting values ​​is obtained, and a regression model of n actual crushing particle size frequencies is established; the input is the crushing particle size setting value, and the output is the frequency of the actual crushing particle size. Substitute the crushing particle size setting value and the frequency of the actual crushing particle size to solve the regression parameters; The temperature under the crushing particle size setting value and the influence of temperature on purity are obtained. The temperature under different crushing particle size setting values ​​is used as input, and the influence of temperature on purity is used as output to train the temperature regression model; the influence of the specified temperature on purity is obtained through the regression model.

[0028] The purpose of this step is to obtain the crushing result of the metal scraps after passing through the crusher under a specified crushing particle size setting value. Due to the error of the crusher, different crushing particle size distributions may be obtained under the same setting value. Therefore, the average value of the proportion of each actual crushing particle size obtained after multiple statistics is taken to obtain the frequency of the actual crushing particle size under the setting value, and the sum of the frequencies is 1. After obtaining the frequency, since the influence of temperature on the recovery data and the influence of the actual crushing particle size on the recovery data are known, the recovery data under the actual crushing particle size can be obtained. Combining the frequency of the actual crushing particle size under the setting value, the recovery data under the setting value can be obtained.

[0029] Each actual crushing particle size corresponds to a regression model. For example, for the first actual crushing particle size, the crushing particle size setting values D1, D2, D3, and D4 are all related to the first actual crushing particle size. "Related" means that the particle size interval can appear under the crushing particle size setting value. For example, if the crushing particle size setting value is 10 mm and the crushing particle size interval corresponding to the actual crushing particle size is [2, 2.5] mm, then the crushing particle size setting value is not related to the actual crushing particle size. Even with errors, the situation of the actual crushing particle size will not occur. When the crushing particle size setting value is 2 mm, it is related to the actual crushing particle size. Using the setting values related to the actual crushing particle size as inputs and the frequency as the output, taking the crushing particle size setting value D1 and the corresponding frequency as a set of inputs and outputs, the crushing particle size setting value D2 and the corresponding frequency as a set of inputs and outputs, and so on, the parameters of the regression model are trained. A total of n regression parameters need to be trained. After obtaining the parameters, only by inputting the crushing particle size setting value, the actual crushing particle size data under the setting value can be obtained.

[0030] The steps for establishing an optimized model for the recovery data to find the optimal crushing particle size setting value also include the following: Establish an objective function for the crushing parameters M(x) = P(x) × H(x), where P(x) represents the purity when the crushing particle size setting value is x, H(x) represents the recovery rate when the crushing particle size is x, x represents the crushing particle size setting value of the crusher, and M(x) represents the optimization objective when the crushing particle size is x.

[0031] The product of the recovery rate and the mass of the metal scraps gives the total recovered mass, and the product of the total mass and the purity gives the actual mass of the recovered metal. In the case where the crushing particle size setting value is x1, the purity of the recovered metal is 98% and the recovery rate is 90%. Considering the promoting effect of oxidation on separation, the crushing particle size setting value is appropriately increased, resulting in the purity of the recovered metal decreasing to 97.5% and the recovery rate rising to 95%. Since 97.5% × 95% is greater than 98% × 90%, more metal can be recovered, and the utilization rate of the metal scraps can be further improved.

[0032] The steps for establishing an optimized model of the recovery data and determining the optimal crushing parameters further include the following: Determine the crushing range [a, b] of the crusher, where a represents the lower limit of the crushing range and b represents the upper limit of the crushing range. Within the crushing range of the crusher, set the step size step according to the accuracy of the crusher. Starting from the set value a of the crushing particle size of the crusher, based on the influence of the temperature and the actual crushing particle size on the purity when the crushing particle size is a, obtain the purity at the actual crushing particle size; use the regression model of the frequencies of n actual crushing particle sizes to obtain the frequencies of n actual crushing particle sizes with the set value of the crushing particle size being a, and use the frequencies as weights to perform weighted summation to obtain the metal purity data recovered when the crushing particle size is a; The analysis method of the recovery rate is the same as that of the purity. Obtain the metal recovery rate data recovered when the crushing particle size is a in the same way; based on the purity and the recovery rate, obtain the optimization objective when the set value of the crushing particle size is a; increase the step size on the basis of a to obtain the optimization objectives under different set values of the crushing particle size; from all the optimization objectives, select the one with the highest score as the optimal set value of the crushing particle size.

[0033] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An automated processing method for by-products of manufacturing generator set spare parts, characterized in that, It includes the following steps: Obtain the sorting data and recycling data of metal scraps during the processing of spare parts; the sorting data is the crusher parameter data; Analyze the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data, establish an optimization model for the recycling data, find the optimal crushing particle size setting value, and perform recycling processing on the metal scraps.

2. The automated processing method for by-products of manufacturing spare parts of a generator set according to claim 1, wherein The analysis of the relationship between the sorting data and metal oxidation, and the relationship between metal oxidation and recycling data further includes the following steps: Obtain the historical crusher parameter data during the processing of spare parts, including the crushing particle size setting value of the crusher and the crushing result of the metal scraps; among them, the crushing result of the metal scraps is obtained by screening and separating the crushed samples, and the proportion of each particle size interval is counted to obtain the quantity of the metal scraps after crushing in each particle size interval under the set crushing particle size; the particle size interval is set according to the accuracy of the crusher; obtain the temperature of the crusher during operation under the set crushing particle size through a temperature sensor; obtain the recycling data detected after the sorting of the metal scraps, and the recycling data includes purity and recovery rate; analyze the recycling data and purity under the crushing particle size setting value of the same crusher to obtain the influence of the actual crushing particle size on the recycling data; analyze the recycling data and purity under the crushing particle size setting value of different crushers to obtain the influence of the crusher temperature on the recycling data.

3. The automated processing method for by-products of manufacturing generator set spare parts according to claim 2, wherein, The analysis of the recycling data and purity under the crushing particle size setting value of the same crusher to obtain the influence of the actual crushing particle size on the recycling data further includes the following steps: Let D denote the crushing particle size setting value of the crusher. Then, from historical data, multiple purity data at the crushing particle size setting value D can be obtained. Let Pur1, Pur2, …, Purn denote the purity data for the 1st, 2nd, …, nth times respectively. Establish the relationship among purity, actual crushing particle size, and temperature; Pur i =T×∑ j=1 n (W ij ×V j ), where n represents the number of set particle size intervals, T represents the influence of temperature on purity, W ij represents the proportion of the jth crushing particle size interval during the ith crushing at the crushing particle size setting value D, V j represents the influence of the jth actual crushing particle size on purity, Pur i represents the purity during the ith crushing at the crushing particle size setting value D; Pur i is obtained by analyzing the purity of the recycled metal scraps, and W ij is obtained by screening and separating the samples after crushing and then statistically calculating the proportion of the actual crushing particle size; The relationship between purity, actual crushing size and temperature under the same crushing size setting value D is combined to establish the equation group: {Pur i =T×∑ j=1 n (W ij ×V j )}, by dividing the two equations, the left side of the equation is the known quantity, and the right side of the equation is the effect of the actual crushing particle size on purity, which can eliminate the effect of temperature on purity and obtain V j The relationship between j All are unified into one variable, and the relationship between purity, actual crushing size and temperature under different crushing size setting values ​​is established to establish an equation group: {Pur i =T×∑ j=1 n (W ij ×V j )}, replace V on the right side of the relationship j All are unified into one. By dividing the two relationship equations, we can get the influence of different temperatures on purity, and unify the influence of temperature on purity into one variable. Taking the relationship equation between the two purities, the actual crushing particle size and the temperature for calculation, we can get the influence of the unified temperature on purity and the influence of the actual crushing particle size on purity, and then determine the influence of all temperatures on purity and the influence of the actual crushing particle size on purity according to the unified relationship.

4. The automated processing method for by-products of manufacturing spare parts of a generator set according to claim 3, characterized in that, It further includes the following steps: After multiple statistics under the crushing particle size setting value D, obtain the proportion of the actual crushing particle size, take the average value to obtain the frequency of each actual crushing particle size under the crushing particle size setting value D, obtain the frequency of the actual crushing particle size under different particle size setting values, and establish a regression model of n actual crushing particle size frequencies; the input is the crushing particle size setting value, the output is the frequency of the actual crushing particle size, and substitute the crushing particle size setting value and the frequency of the actual crushing particle size to solve the regression parameters; Obtain the temperature under the crushing particle size setting value and the influence of the temperature on the purity, use the temperature under different crushing particle size setting values as the input and the influence of the temperature on the purity as the output to train the regression model of the temperature; obtain the influence of the specified temperature on the purity through the regression model.

5. The automatic processing method for by-products of manufacturing spare parts of a generator set according to claim 4, characterized in that, The establishment of the optimization model for the recycling data and finding the optimal crushing particle size setting value further includes the following steps: Establish the objective function of the crushing parameters M(x)=P(x)×H(x), where P(x) represents the purity when the crushing particle size setting value is x, H(x) represents the recovery rate when the crushing particle size is x, x represents the crushing particle size setting value of the crusher, and M(x) represents the optimization objective when the crushing particle size is x.

6. The automated processing method for by-products of manufacturing spare parts of a generator set according to claim 4, wherein, The establishment of the optimization model for the recycling data and determining the best crushing parameters further includes the following steps: Determine the crushing range [a, b] of the crusher. Here, a represents the lower limit of the crushing range, and b represents the upper limit of the crushing range. Within the crushing range of the crusher, set the step size step according to the accuracy of the crusher. Starting from the set value a of the crushing particle size of the crusher, based on the influence of the temperature and the actual crushing particle size on the purity when the crushing particle size is a, obtain the purity at the actual crushing particle size; use the regression model of the frequencies of n actual crushing particle sizes to obtain the frequencies of the n actual crushing particle sizes with the set value of the crushing particle size being a, and use the frequencies as weights to perform weighted summation to obtain the metal purity data recovered when the crushing particle size is a; The analysis method of the recovery rate is the same as that of the purity. In the same way, obtain the metal recovery rate data recovered when the crushing particle size is a; based on the purity and the recovery rate, obtain the optimization objective when the set value of the crushing particle size is a; on the basis of a, increase the step size to obtain the optimization objectives under different set values of the crushing particle size; from all the optimization objectives, select the one with the highest score as the optimal set value of the crushing particle size.

7. An automated processing system for by-products of generating set spare parts, characterized in that, It includes: a data collection module, a data storage module, a data analysis module, and a recovery module; the output end of the data collection module is connected to the input ends of the data storage module and the data analysis module respectively, and is used to obtain the recovery data and sorting data of the metal scraps; the output end of the data storage module is connected to the input end of the data analysis module, and is used to store the historical sorting data and recovery data of the metal scraps; the output end of the data analysis module is connected to the input end of the recovery module, and is used to analyze the influence of the temperature and the actual crushing particle size on the recovery data to find the optimal set value of the crushing particle size; the recovery module is used to control the parameters of the crusher and perform sorting and recovery on the metal scraps.

8. The by-product automatic processing system for manufacturing spare parts of a generator set according to claim 7, wherein The data collection module further includes a temperature detection unit, a purity analysis unit, and a particle size analysis unit; the temperature detection unit is used to detect the temperature of the crusher during the sorting process; the purity analysis unit is used to detect the purity of the recovered metal scraps; the particle size analysis unit is used to obtain the particle size data of the fragments generated after the metal passes through the crusher.

9. The by-product automatic processing system for manufacturing spare parts of a generator set according to claim 8, characterized in that, The data analysis module further includes a calculation unit, a regression unit, and an optimization unit; the calculation unit is used to establish the relationship formula among the purity, the crushing particle size, and the temperature, and determine the influence of the crushing particle size and the temperature on the purity through solution and calculation; the regression unit is used to generate the regression model between the set value of the crushing particle size and the temperature, and generate the regression model between the set value of the crushing particle size and the frequencies of the actual crushing particle sizes; the optimization unit is used to calculate the optimization objective under the specified set value of the crushing particle size and determine the optimal set value of the crushing particle size.

10. The by-product automatic processing system for producing and processing spare parts of a generator set according to claim 9, characterized in that, The recovery module further includes an input unit, a sorting unit, and a storage unit; the input unit is used to connect the metal scraps in the processing of the generator set; the sorting unit is used to perform sorting and recovery on the metal scraps; the storage unit is used to store the recovered metal scraps.

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