Automatic processing system and method for by-products of production and processing of generator set spare parts

Abstract

The application discloses an automatic processing system and method for by-products of generator unit spare parts production and processing, relates to the technical field of spare part processing, and obtains sorting data and recovery data of metal excess materials in the spare part processing process; the sorting data is crusher parameter data; the relationship between the sorting data and metal oxidation and the relationship between the metal oxidation and the recovery data are analyzed, an optimization model of the recovery data is established, an optimal crushing granularity setting value is found, and the metal excess materials are subjected to recovery treatment; the relationship between the crushing granularity and the metal recovery rate and the metal purity is established through the metal oxidation, the metal oxidation and the crushing granularity are subjected to collaborative control, the recovery rate and the purity are balanced, the optimal metal crushing granularity is determined, and the recovery amount of the metal is improved, so that remarkable production and environmental benefits are achieved.

Description

Technical Field

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

[0002] With the increasing production and processing of generator set parts, the problem of byproducts has gradually emerged. Generator set parts include metal components, and the production process may produce metal waste. During the recycling of metal waste, the crusher's crushing particle size directly affects the recovery rate and metal purity. Traditional methods aim to minimize metal oxidation. However, the pursuit of purity may lead to incomplete sorting, resulting in reduced metal recovery rate and loss. Therefore, how to utilize the phenomenon of metal oxidation to improve the balance between metal waste purity and recovery rate has become a pressing issue. Summary of the Invention

[0003] The object of the present invention is to provide an automated processing system and method for by-products of the production and processing of generator set parts, so as to solve the problems raised in the prior art.

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an automated processing system for by-products of the production and processing of generator set spare parts, comprising 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 interconnected with the input end of the data storage module and the data analysis module, for obtaining the recycling data and sorting data of the metal waste; the output end of the data storage module is interconnected with the input end of the data analysis module, for storing the historical sorting data and recycling data of the metal waste; the output end of the data analysis module is interconnected with the input end of the recycling module, for analyzing the influence of temperature and actual crushing particle size on the recycling data, and finding 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 waste.

[0005] The data collection module also 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 residue; 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 also includes a calculation unit, a regression unit and an optimization unit; the calculation unit is used to establish a relationship between 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 produce a regression model between the crushing particle size setting value and temperature, and 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 the specified crushing particle size setting value and determine the optimal crushing particle size setting value.

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

[0008] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for automatically processing by-products of the production and processing of generator set parts, comprising the following steps:

[0009] Obtaining sorting data and recycling data of metal waste during spare parts processing; the sorting data is crusher parameter data;

[0010] Analyze the relationship between 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 recycle the metal residue.

[0011] Specifically, the analysis of the relationship between the sorting data and the metal oxidation data, and the relationship between the metal oxidation data and the recovery data further includes the following steps:

[0012] Obtain historical crusher parameter data during spare parts processing, including the crushing particle size setting value of the crusher and the crushing results of the metal residue; the crushing results of the metal residue are obtained by screening and sorting the crushed samples, counting the proportion of each particle size interval, and obtaining the number of metal residues in each particle size interval after crushing at the set crushing particle size; the particle size interval is set according to the accuracy of the crusher; the temperature of the crusher when it is working at the set crushing particle size is obtained through a temperature sensor; the recovery data obtained after the metal residue is sorted and tested, the recovery data including purity and recovery rate; the recovery data and purity under the crushing particle size setting value of the same crusher are analyzed to obtain the influence of the actual crushing particle size on the recovery data; the recovery data and purity under the crushing particle size setting value of different crushers are analyzed to obtain the influence of the crusher temperature on the recovery data.

[0013] Specifically, the analysis of the recovery 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 recovery data also includes the following steps:

[0014] 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 time 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 It represents the proportion of the jth crushing size interval during the i-th crushing under the crushing size setting value D, V 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 was analyzed, W ij By screening and selecting the crushed samples, the actual crushing particle size ratio is obtained through statistics;

[0015] The relationship between purity, actual crushing size and temperature under the same crushing size setting value D is established 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 the purity, which can eliminate the effect of temperature on the purity and obtain V j The relationship between V j All are unified into one variable, and the relationship between purity, actual crushing particle size and temperature under different crushing particle size setting values ​​is established to establish the equation group: {Pur i =T×∑ j=1 n (W ij ×V j )}, replace V on the right side of the relationship j Unifying everything into one, dividing the two relationships, we can determine the effect of different temperatures on purity, and unifying the effect of temperature on purity into one variable. Taking the relationship between the two purities, the actual crushing particle size, and temperature, we can calculate the unified effect of temperature on purity and the effect of actual crushing particle size on purity. Then, using the unified relationship, we can determine the overall effect of temperature on purity and the effect of actual crushing particle size on purity. The recovery rate is processed in the same way as the purity, determining the overall effect of temperature on recovery and the effect of actual crushing particle size on recovery.

[0016] Optionally, the proportion of actual crushed particle sizes obtained after multiple statistics under the crushing particle size setting value D is averaged to obtain the frequency of each actual crushed particle size under the crushing particle size setting value D, the frequency of the actual crushed particle sizes under different particle size setting values ​​is obtained, and a regression model of n actual crushed particle size frequencies is established; the input is the crushing particle size setting value, and the output is the frequency of the actual crushed particle size. The crushing particle size setting value and the frequency of the actual crushed particle size are substituted into the regression model to solve the regression parameter;

[0017] The temperature at each crushing particle size setting and its effect on purity were obtained. Using the temperature at each crushing particle size setting as input and the effect of temperature on purity as output, a temperature regression model was trained. The effect of a given temperature on purity was determined through the regression model. Similarly, the effect of a given temperature on recovery was determined through the training of the regression model.

[0018] Specifically, the process of establishing an optimization model for recycling data and finding the optimal crushing particle size setting value further includes the following steps:

[0019] Establish the objective function of 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 target when the crushing particle size is x.

[0020] Determine the crushing range of the crusher [a, b], 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 crusher's accuracy. Starting from the crushing particle size setting value a of the crusher, the purity at the actual crushing particle size is obtained based on the influence of temperature and actual crushing particle size on purity when the crushing particle size is a. Use a regression model of n actual crushing particle size frequencies to obtain the frequencies of n actual crushing particle sizes when the crushing particle size setting value is a. Use the frequencies as weights for weighted summation to obtain the metal purity data recovered when the crushing particle size is a.

[0021] The analysis method for recovery rate is the same as that for purity. The metal recovery rate data when the crushing particle size is a is obtained in the same way. The optimization target when the crushing particle size setting value is a is obtained based on the purity and recovery rate. The step size is increased on the basis of a to obtain the optimization targets under different crushing particle size setting values. From all the optimization targets, the one with the highest score is selected as the optimal crushing particle size setting value.

[0022] Compared with the existing technology, the beneficial effects of the present invention are: establishing the relationship between crushing particle size and metal recovery rate and metal purity through metal oxidation, synergistically controlling metal oxidation and crushing particle size, balancing recovery rate and purity, determining the optimal metal crushing particle size, and increasing metal recovery, with significant production and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The figure is a schematic structural diagram of the automated processing system for by-products of producing and processing generator set parts according to the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0025] Example: Figure 1 As shown, the present invention provides a technical solution, an automated processing system for by-products of the production and processing 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 interconnected with the input end of the data storage module and the data analysis module, and is used to obtain the recycling data and sorting data of the metal waste; the output end of the data storage module is interconnected with the input end of the data analysis module, and is used to store the historical sorting data and recycling data of the metal waste; the output end of the data analysis module is interconnected with 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, and 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 waste.

[0026] The data collection module also 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 residue; the particle size analysis unit is used to obtain the particle size data of the fragments generated after the metal passes through the crusher.

[0027] The data analysis module also includes a calculation unit, a regression unit and an optimization unit; the calculation unit is used to establish a relationship between 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 produce a regression model between the crushing particle size setting value and temperature, and 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 the specified crushing particle size setting value and determine the optimal crushing particle size setting value.

[0028] The recycling module also includes an input unit, a sorting unit and a storage unit; the input unit is used to access the metal waste during the processing of the generator set; the sorting unit is used to sort and recycle the metal waste; and the storage unit is used to store the recycled metal waste.

[0029] Embodiment: The present invention provides a technical solution, a method for automatically processing by-products of the production and processing of generator set parts, comprising the following steps:

[0030] Obtaining sorting data and recycling data of metal waste during spare parts processing; the sorting data is crusher parameter data;

[0031] Analyze the relationship between 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 recycle the metal residue.

[0032] The analysis of the relationship between the sorting data and the metal oxidation, and the relationship between the metal oxidation and the recovery data further comprises the following steps:

[0033] Obtain historical crusher parameter data during spare parts processing, including the crushing particle size setting value of the crusher and the crushing results of the metal residue; the crushing results of the metal residue are obtained by screening and sorting the crushed samples, counting the proportion of each particle size interval, and obtaining the number of metal residues in each particle size interval after crushing at the set crushing particle size; the particle size interval is set according to the accuracy of the crusher; the temperature of the crusher when it is working at the set crushing particle size is obtained through a temperature sensor; the recovery data obtained after the metal residue is sorted and tested, the recovery data including purity and recovery rate; the recovery data and purity under the crushing particle size setting value of the same crusher are analyzed to obtain the influence of the actual crushing particle size on the recovery data; the recovery data and purity under the crushing particle size setting value of different crushers are analyzed to obtain the influence of the crusher temperature on the recovery data.

[0034] Due to the influence of differences in metal residue characteristics, crusher tool wear, fluctuations in industrial parameters and impurities, crushing errors are inevitable after the metal residue is crushed. Usually, the crushing particle size fluctuates around the crusher's crushing particle size setting value. The crushing particle size is divided into several particle size intervals based on historical crushing particle size fluctuation data and crusher accuracy.

[0035] The spare parts of the generator include metal components, so metal waste is 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 metal to subsequent processing for reuse; in the sorting process, the metal is crushed. During crushing, the surface area of ​​the metal increases, and the contact area with the air also increases. The metal can be exposed to more oxygen, which increases the degree of oxidation of the metal, and thus reduces 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.

[0036] 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:

[0037] 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 time 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 It represents the proportion of the jth crushing size interval during the i-th crushing under the crushing size setting value D, V 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 was analyzed, W ij By screening and selecting the crushed samples, the actual crushing particle size ratio is obtained through statistics;

[0038] The relationship between purity, actual crushing size and temperature under the same crushing size setting value D is established 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 the purity, which can eliminate the effect of temperature on the purity and obtain V j The relationship between V j All are unified into one variable, and the relationship between purity, actual crushing particle size and temperature under different crushing particle size setting values ​​is established to establish the 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 expressions, we can get the influence of different temperatures on purity, and unify the influence of temperature on purity into one variable. Take the relationship expression between the two purities, actual crushing particle size and temperature for calculation, and we can get the influence of unified temperature on purity and the influence of actual crushing particle size on purity. Then determine the influence of all temperatures on purity and the influence of actual crushing particle size on purity according to the unified relationship.

[0039] Metal oxidation caused by increased surface area and temperature can reduce the purity of recovered metals. On the other hand, metal sorting typically relies on differences in physical properties, such as density, magnetism, and conductivity. Oxidation changes the properties of the metal surface, affecting the sorting effect. For example, metal oxides may have different electrical or magnetic properties, making them easier to separate during sorting. Iron oxide, when oxidized, produces highly magnetic ferroferric oxide. If other metals are non-magnetic, this can improve the recovery rate of iron metal sorting.

[0040] The actual crushing particle size is expanded into intervals, 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.

[0041] After multiple statistics under the crushing particle size setting value D, the actual crushing particle size ratio 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. The frequency of the actual crushing particle size 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. The crushing particle size setting value and the actual crushing particle size frequency are substituted into the regression model to solve the regression parameters;

[0042] 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.

[0043] The purpose of this step is to obtain the crushing result of the metal residue after passing through the crusher under the 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, so the average value of the actual crushing particle size proportions 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, and combined with the frequency of the actual crushing particle size under the setting value, the recovery data under the setting value can be obtained.

[0044] 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 range can appear under the crushing particle size setting value. For example, if the crushing particle size setting value is 10mm, 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 independent of the actual crushing particle size. Even if there is an error, the actual crushing particle size will not appear. When the crushing particle size setting value is 2mm, it is related to the actual crushing particle size. The setting value related to the actual crushing particle size is used as input and the frequency as output. The crushing particle size setting value D1 and the corresponding frequency are used as a set of input and output, and the crushing particle size setting value D2 and the corresponding frequency are used as a set of input and output. And so on. To train the parameters of the regression model, a total of n regression parameters need to be trained. After obtaining the parameters, you only need to input the crushing particle size setting value to obtain the actual crushing particle size data under the setting value.

[0045] The process of establishing an optimization model for recycling data and finding the optimal crushing particle size setting value further comprises the following steps:

[0046] Establish the objective function of 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 target when the crushing particle size is x.

[0047] The product of the recovery rate and the mass of the residual metal gives the total mass recovered, and the product of the total mass and the purity gives the actual mass of metal recovered. When the crushing particle size setting is x1, the purity of the recovered metal is 98% and the recovery rate is 90%. Considering the effect of oxidation on separation, increasing the crushing particle size setting appropriately reduces the purity of the recovered metal to 97.5% and increases the recovery rate to 95%. Since 97.5% × 95% is greater than 98% × 90%, more metal can be recovered, further improving the utilization rate of the residual metal.

[0048] The establishment of an optimization model for recycling data and determination of optimal crushing parameters further comprises the following steps:

[0049] Determine the crushing range of the crusher [a, b], 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 crusher's accuracy. Starting from the crushing particle size setting value a of the crusher, the purity at the actual crushing particle size is obtained based on the influence of temperature and actual crushing particle size on purity when the crushing particle size is a. Use a regression model of n actual crushing particle size frequencies to obtain the frequencies of n actual crushing particle sizes when the crushing particle size setting value is a. Use the frequencies as weights for weighted summation to obtain the metal purity data recovered when the crushing particle size is a.

[0050] The analysis method for recovery rate is the same as that for purity. The metal recovery rate data when the crushing particle size is a is obtained in the same way. The optimization target when the crushing particle size setting value is a is obtained based on the purity and recovery rate. The step size is increased on the basis of a to obtain the optimization targets under different crushing particle size setting values. From all the optimization targets, the one with the highest score is selected as the optimal crushing particle size setting value.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. An automated method for processing by-products from the production and processing of generator set parts, characterized in that: The following steps are involved: Obtaining sorting data and recycling data of metal waste during spare parts processing; the sorting data is crusher parameter data; Analyze the relationship between sorting data and metal oxidation, and between metal oxidation and recycling data, establish an optimization model for recycling data, find the optimal crushing particle size setting value, and recycle the metal residue; The relationship between the analysis and sorting data and metal oxidation, and the relationship between metal oxidation and recovery data include: obtaining historical crusher parameter data during spare parts processing, including the crushing particle size setting value of the crusher and the crushing result of the metal residue; wherein the crushing result of the metal residue is obtained by screening and sorting the crushed samples, counting the proportion of each particle size interval, and obtaining the number of metal residues in each particle size interval after crushing at the set crushing particle size; wherein the particle size interval is set according to the accuracy of the crusher; obtaining the temperature of the crusher when it is working at the set crushing particle size through a temperature sensor; obtaining the recovery data obtained by testing after the metal residue is sorted, and the recovery data includes purity and recovery rate; analyzing the recovery 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 recovery data; 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 includes: 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 time 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 It represents the proportion of the jth crushing size interval during the i-th crushing under the crushing size setting value D, V 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 was analyzed, W ij The crushed samples are screened and sorted, and the actual percentage of crushed particle size is obtained through statistics.

2. The method for automatically processing by-products of the production and processing of generator set parts according to claim 1, characterized in that: The analysis of the relationship between the sorting data and the metal oxidation, and the relationship between the metal oxidation and the recovery data further comprises the following steps: The recovery data and purity under different crusher crushing particle size setting values ​​were analyzed to obtain the influence of crusher temperature on the recovery data.

3. The method for automatically processing by-products of the production and processing of generator set parts according to claim 2, characterized in that: 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: The relationship between purity, actual crushing size and temperature under the same crushing size setting value D is established 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 the purity, which can eliminate the effect of temperature on the purity and obtain V j The relationship between V j All are unified into one variable, and the relationship between purity, actual crushing particle size and temperature under different crushing particle size setting values ​​is established to establish the 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 expressions, we can get the influence of different temperatures on purity, and unify the influence of temperature on purity into one variable. Take the relationship expression between the two purities, actual crushing particle size and temperature for calculation, and we can get the influence of unified temperature on purity and the influence of actual crushing particle size on purity. Then determine the influence of all temperatures on purity and the influence of actual crushing particle size on purity according to the unified relationship.

4. The method for automatically processing by-products of the production and processing of generator set parts according to claim 3, characterized in that: The following steps are also included: After multiple statistics under the crushing particle size setting value D, the actual crushing particle size ratio 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. The frequency of the actual crushing particle size 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. The crushing particle size setting value and the actual crushing particle size frequency are substituted into the regression model 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.

5. The method for automatically processing by-products of the production and processing of generator set parts according to claim 4, characterized in that: The process of establishing an optimization model for recycling data and finding the optimal crushing particle size setting value further comprises the following steps: Establish the objective function of 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 target when the crushing particle size is x.

6. The method for automatically processing by-products of the production and processing of generator set parts according to claim 4, characterized in that: The establishment of an optimization model for recycling data and determination of optimal crushing parameters further comprises the following steps: Determine the crushing range of the crusher [a, b], 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 crusher's accuracy. Starting from the crushing particle size setting value a of the crusher, the purity at the actual crushing particle size is obtained based on the influence of temperature and actual crushing particle size on purity when the crushing particle size is a. Use a regression model of n actual crushing particle size frequencies to obtain the frequencies of n actual crushing particle sizes when the crushing particle size setting value is a. Use the frequencies as weights for weighted summation to obtain the metal purity data recovered when the crushing particle size is a. The analysis method for recovery rate is the same as that for purity. The metal recovery rate data when the crushing particle size is a is obtained in the same way. The optimization target when the crushing particle size setting value is a is obtained based on the purity and recovery rate. The step size is increased on the basis of a to obtain the optimization targets under different crushing particle size setting values. From all the optimization targets, the one with the highest score is selected as the optimal crushing particle size setting value.

7. An automated processing system for by-products of the production and processing of generator set parts, applied to the automated processing method for by-products of the production and processing of generator set parts according to any one of claims 1 to 6, characterized in that: include: Data collection module, data storage module, data analysis module and recovery module; The output end of the data collection module is interconnected with the input end of the data storage module and the data analysis module, and is used to obtain the recovery data and sorting data of the metal waste; the output end of the data storage module is interconnected with the input end of the data analysis module, and is used to store the historical sorting data and recovery data of the metal waste; the output end of the data analysis module is interconnected with the input end of the recovery module, and is used to analyze the influence of temperature and actual crushing particle size on the recovery data and find the optimal crushing particle size setting value; the recovery module is used to control the parameters of the crusher and sort and recycle the metal waste.

8. The automated processing system for by-products of the production and processing of generator set parts according to claim 7, characterized in that: The data collection module also 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 residue; 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 automated processing system for by-products of the production and processing of generator set parts according to claim 8, characterized in that: The data analysis module also includes a calculation unit, a regression unit and an optimization unit; the calculation unit is used to establish a relationship between 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 produce a regression model between the crushing particle size setting value and temperature, and 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 the specified crushing particle size setting value and determine the optimal crushing particle size setting value.

10. The automated processing system for by-products of the production and processing of generator set parts according to claim 9, characterized in that: The recycling module also includes an input unit, a sorting unit and a storage unit; the input unit is used to access the metal waste during the processing of the generator set; the sorting unit is used to sort and recycle the metal waste; and the storage unit is used to store the recycled metal waste.

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