Comprehensive evaluation method and system for waste lead plaster wet regeneration cleaning technology

Abstract

The invention belongs to the technical field of comprehensive evaluation, and particularly relates to a comprehensive evaluation method and system for a waste lead plaster wet regeneration cleaning technology, and the method comprises the steps: obtaining an evaluation result related to the processing flow efficiency, an evaluation result related to the environmental protection measure implementation effect, an evaluation result related to the product income, and an evaluation result related to the product quality; selecting one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the speculation operation data of the equipment at the speculation time; generating a difference speculation algorithm based on a plurality of equipment operation data differences; in the case of performing estimation processing using a final estimation algorithm, final facility estimated operation data at the estimation time is calculated from the facility estimated operation data at the estimation time and a difference between the facility estimated operation data at the estimation time, evaluation results relating to facility operation stability are generated, and all the evaluation results are integrated. According to the invention, an intelligent evaluation method can be provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of comprehensive evaluation, and particularly relates to a comprehensive evaluation method and system for the wet regeneration cleaning technology of waste lead paste. Background Art

[0002] With the increasing emphasis on environmental protection and resource recycling, the wet regeneration technology is used to extract lead from waste lead paste and make lead products. This can not only solve the environmental pollution problem but also create economic benefits. However, there is currently a lack of a comprehensive evaluation method for this technology.

[0003] Similar prior arts include a Chinese patent application with the publication number CN114168904A, which discloses an evaluation method for VOCs emission control technology in the process of processing inferior heavy oil. The evaluation method includes the following steps: S100, conduct on-site detection and experimental detection on the process of processing inferior heavy oil to obtain the VOCs emission characteristics of each sub-functional area; S200, screen out the corresponding important VOCs pollution sources in each sub-functional area based on the VOCs emission characteristics as the evaluation objects; S300, establish a framework for the evaluation index system of the evaluation objects; S400, establish an evaluation matrix and an evaluation model for the VOCs emission control technology in each sub-functional area of the process of processing inferior heavy oil, and select the most suitable control technology in each sub-functional area to control its VOCs emission. In addition, a similar prior art is a Chinese patent application with the publication number CN103646147A, which proposes a comprehensive evaluation method for aerospace components based on the maturity model, numerically quantifies the performance indicators of the components, and provides a judgment method for ensuring the selection of aerospace components. First, determine the concept of the maturity of aerospace components, then clearly divide the grades of the maturity of the components, give the main work content of each grade division, and then give the evaluation criteria, comprehensive scoring method, and evaluation implementation steps of the components. Finally, give the maturity improvement program of the components, providing an important theoretical basis for the continuous improvement of the product performance of aerospace components. However, the evaluation methods proposed in the above two patent applications lack a certain degree of intelligence. Summary of the Invention

[0004] The present invention generates multiple speculation algorithms and multiple candidate speculation algorithms in advance, selects the final speculation algorithm and the final candidate speculation algorithm that are more suitable for speculation processing from them, and further selects the most suitable algorithm for speculation processing from the final speculation algorithm and the final candidate speculation algorithm, so as to obtain the device speculation operation data at the speculation moment. When the device speculation operation data is generated by the final speculation algorithm, the final device speculation operation data at the speculation moment is calculated according to the device speculation operation data at the speculation moment and the difference between the device speculation operation data at the speculation moment, and then the evaluation result regarding the device operation stability is integrated with all other evaluation results. The present invention aims to provide an intelligent evaluation method.

[0005] In order to achieve the above invention object, the present invention provides the following comprehensive evaluation method for the wet regeneration cleaning technology of waste lead paste, which mainly includes the following steps:

[0006] The preparation module collects the process data set, the environmental protection related data set, the device operation data set, the cost data set, and the product test data set, and the analysis module analyzes and processes the process data set to obtain the evaluation result regarding the processing process efficiency, analyzes and processes the environmental protection related data set to obtain the evaluation result regarding the implementation effect of environmental protection measures, analyzes and processes the cost data set to obtain the evaluation result regarding the product revenue, and analyzes and processes the product test data set to obtain the evaluation result regarding the product quality;

[0007] The analysis module generates several speculation algorithms and several candidate speculation algorithms, determines the final speculation algorithm among the several speculation algorithms, determines the final candidate speculation algorithm among the several candidate speculation algorithms, and selects one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the device speculation operation data at the speculation moment;

[0008] The analysis module obtains several device actual operation data at past moments and several device speculation operation data at past moments, and for each past moment, the analysis module calculates the device operation data difference between the device actual operation data at the past moment and the device speculation operation data at the past moment, and the analysis module also generates a difference speculation algorithm based on several device operation data differences;

[0009] In the case of performing speculation processing using the final speculation algorithm, the analysis module calculates the final device speculation operation data at the speculation moment based on the device speculation operation data at the speculation moment and the difference in the device speculation operation data at the speculation moment output by the difference speculation algorithm. In the case of performing speculation processing using the final candidate speculation algorithm, the analysis module directly uses the device speculation operation data at the speculation moment as the final device speculation operation data. Subsequently, the analysis module generates an evaluation result regarding the device operation stability based on the final device speculation operation data, and the comprehensive evaluation module integrates different evaluation results to generate a comprehensive evaluation result.

[0010] As a preferred technical solution of the present invention, the process by which the analysis module generates several speculation algorithms includes: First, the analysis module determines several factors that affect the speculation result. Second, the analysis module generates several factor combinations, each factor combination consisting of different factors, and respectively generates several speculation algorithms corresponding to the several factor combinations.

[0011] As a preferred technical solution of the present invention, the process by which the analysis module generates several candidate speculation algorithms includes: The analysis module respectively generates several candidate speculation algorithms corresponding to the several factor combinations according to the several factor combinations that have been generated.

[0012] As a preferred technical solution of the present invention, the speculation algorithm is pre-trained by the analysis module using a training data set. The training data set consists of different training data, and the training data includes the device real operation data at past moments and different factor data corresponding to the past moments.

[0013] As a preferred technical solution of the present invention, the analysis module determines the final speculation algorithm among several speculation algorithms and determines the final candidate speculation algorithm among several candidate speculation algorithms, including the following steps:

[0014] The analysis module selects a speculation algorithm from several speculation algorithms, and at the same time selects a candidate speculation algorithm corresponding to the selected speculation algorithm from several candidate speculation algorithms;

[0015] The analysis module determines several past moments, and the analysis module makes the selected speculation algorithm perform several speculation processes to obtain the first device speculation operation data at several past moments, and makes the selected candidate speculation algorithm also perform several speculation processes to obtain the second device speculation operation data at several past moments;

[0016] The analysis module obtains the actual operation data of the device at a number of determined past moments, calculates the first accuracy of the selected speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the first speculative operation data of the device at a number of past moments, calculates the second accuracy of the selected candidate speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the second speculative operation data of the device at a number of past moments, and calculates the comprehensive accuracy according to the first accuracy and the second accuracy;

[0017] The analysis module determines whether there is a speculation algorithm that has not been selected. If so, it jumps to the initial steps of selecting the speculation algorithm and the candidate speculation algorithm. If not, it regards the selected speculation algorithm corresponding to the maximum comprehensive accuracy and the selected candidate speculation algorithm as the final speculation algorithm and the final candidate speculation algorithm respectively.

[0018] As a preferred technical solution of the present invention, the analysis module selects one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the speculative operation data of the device at the speculation moment, including the following steps:

[0019] The analysis module obtains the actual operation data of the device at a number of past moments within the first past time period before the current moment, and for each past moment, the analysis module obtains different factor data corresponding to the past moment, and the analysis module also performs training processing on the final speculation algorithm;

[0020] The analysis module determines a number of past moments within the second past time period before the current moment, and the analysis module makes the final speculation algorithm perform a number of speculation processes to obtain the third speculative operation data of the device at a number of past moments, and makes the final candidate speculation algorithm also perform a number of speculation processes to obtain the fourth speculative operation data of the device at a number of past moments;

[0021] The analysis module obtains the actual operation data of the device at a number of determined past moments, calculates the third accuracy of the final speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the third speculative operation data of the device at a number of past moments, and calculates the fourth accuracy of the final candidate speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the fourth speculative operation data of the device at a number of past moments;

[0022] The analysis module determines whether the third accuracy is greater than or equal to the fourth accuracy. If so, it selects the final speculation algorithm. If not, it selects the final candidate speculation algorithm, and the analysis module uses the selected algorithm for speculation processing to obtain the speculative operation data of the device at the speculation moment.

[0023] As a preferred technical solution of the present invention, the preset formula is where δ is the accuracy, k is the total number of actual operation data of the device, α i is the i-th actual operation data of the device, β i is the i-th estimated operation data of the device, and χ is the maximum actual operation data among all the actual operation data of the devices.

[0024] The present invention also provides a comprehensive evaluation system for the wet regeneration cleaning technology of waste lead paste, which mainly includes the following modules:

[0025] A preparation module, which is used to collect process data sets, environmental protection-related data sets, device operation data sets, cost data sets, and product test data sets;

[0026] An analysis module, which is used to analyze and process the process data set to obtain an evaluation result on the efficiency of the processing process, analyze and process the environmental protection-related data set to obtain an evaluation result on the implementation effect of environmental protection measures, analyze and process the cost data set to obtain an evaluation result on the product revenue, and analyze and process the product test data set to obtain an evaluation result on the product quality; at the same time, it is used to generate several speculation algorithms and several candidate speculation algorithms, determine the final speculation algorithm among the several speculation algorithms, determine the final candidate speculation algorithm among the several candidate speculation algorithms, select one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the estimated operation data of the device at the speculation moment; and it is used to obtain several actual operation data of the device at several past moments, and several estimated operation data of the device at several past moments, calculate the device operation data difference between the actual operation data of the device at the past moment and the estimated operation data of the device at the past moment for each past moment, and also generate a difference speculation algorithm based on several device operation data differences; it is also used to calculate the final estimated operation data of the device at the speculation moment according to the estimated operation data of the device at the speculation moment and the device operation data difference of the device at the speculation moment output by the difference speculation algorithm when using the final speculation algorithm for speculation processing, directly use the estimated operation data of the device at the speculation moment as the final estimated operation data when using the final candidate speculation algorithm for speculation processing, and generate an evaluation result on the device operation stability based on the final estimated operation data;

[0027] A comprehensive evaluation module, which is used to integrate different evaluation results to generate a comprehensive evaluation result.

[0028] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0029] In the present invention, first, evaluation results regarding the efficiency of the processing flow, evaluation results regarding the implementation effect of environmental protection measures, evaluation results regarding product revenue, and evaluation results regarding product quality are obtained; second, a final speculation algorithm is determined among several speculation algorithms, a final candidate speculation algorithm is determined among several candidate speculation algorithms, and one of the final speculation algorithm and the final candidate speculation algorithm is selected for speculation processing to obtain the device speculation operation data at the speculation moment; third, for each past moment, the device operation data difference between the device actual operation data at the past moment and the device speculation operation data at the past moment is calculated, and a difference speculation algorithm is generated based on several device operation data differences; finally, in the case of using the final speculation algorithm for speculation processing, the final device speculation operation data at the speculation moment is calculated according to the device speculation operation data at the speculation moment and the device speculation operation data difference at the speculation moment, and in the case of using the final candidate speculation algorithm for speculation processing, the device speculation operation data at the speculation moment is directly used as the final device speculation operation data, and then an evaluation result regarding the device operation stability is generated based on the final device speculation operation data, and different evaluation results are integrated to generate a comprehensive evaluation result. Through the present invention, not only can an intelligent, comprehensive, and scientific evaluation method be provided, but also the accuracy of the evaluation result regarding the device operation stability can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the comprehensive evaluation method for the waste lead paste wet regeneration cleaning technology of the present invention;

[0031] Figure 2 is a structural diagram of the comprehensive evaluation system for the waste lead paste wet regeneration cleaning technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0034] Referring to Figure 1 , the present invention provides a comprehensive evaluation method for the waste lead paste wet regeneration cleaning technology, which is mainly implemented by performing the following steps:

[0035] The preparation module collects process data sets, environmental protection-related data sets, equipment operation data sets, cost data sets, and product test data sets. And the analysis module analyzes and processes the process data set to obtain an evaluation result on the efficiency of the processing process, analyzes and processes the environmental protection-related data set to obtain an evaluation result on the implementation effect of environmental protection measures, analyzes and processes the cost data set to obtain an evaluation result on the product revenue, and analyzes and processes the product test data set to obtain an evaluation result on the product quality;

[0036] The analysis module generates a number of speculation algorithms and a number of candidate speculation algorithms, determines the final speculation algorithm among the number of speculation algorithms, determines the final candidate speculation algorithm among the number of candidate speculation algorithms, and selects one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the device speculation operation data at the speculation moment;

[0037] The analysis module obtains the device actual operation data at a number of past moments and the device speculation operation data at a number of past moments. And for each past moment, the analysis module calculates the device operation data difference between the device actual operation data at the past moment and the device speculation operation data at the past moment. The analysis module also generates a difference speculation algorithm based on a number of device operation data differences;

[0038] In the case of using the final speculation algorithm for speculation processing, the analysis module calculates the final device speculation operation data at the speculation moment according to the device speculation operation data at the speculation moment and the device speculation operation data difference at the speculation moment output by the difference speculation algorithm. In the case of using the final candidate speculation algorithm for speculation processing, the analysis module directly uses the device speculation operation data at the speculation moment as the final device speculation operation data. Subsequently, the analysis module generates an evaluation result on the device operation stability based on the final device speculation operation data. The comprehensive evaluation module integrates different evaluation results to generate a comprehensive evaluation result.

[0039] Specifically, the present invention proposes a scientific, comprehensive and intelligent method for comprehensively evaluating the wet regeneration cleaning technology of waste lead paste. First, the preparation module collects process data sets, environmental protection related data sets, equipment operation data sets, cost data sets, and product test data sets. Among them, the process data sets include particle size distribution data in the lead paste crushing stage, screening passing rate data in the screening stage, and fineness data after grinding in the grinding stage, etc. The environmental protection related data sets include concentration data of various pollutants in wastewater and content data of harmful gases in waste gas, etc. The cost data sets include raw material cost data, energy consumption cost data, labor cost data, lead product price data, and lead product demand data, etc. The product test data sets include purity data of lead products, impurity content data of lead products, and mechanical property data, etc. The equipment operation data sets include various operation state data of the equipment, such as temperature data, pressure data, current data, etc. when the equipment is running. On this basis, the analysis module analyzes and processes the process data sets to obtain an evaluation result on the efficiency of the processing process. The analysis module analyzes and processes the environmental protection related data sets to obtain an evaluation result on the implementation effect of environmental protection measures. The analysis module analyzes and processes the cost data sets to obtain an evaluation result on the product income. The analysis module analyzes and processes the product test data sets to obtain an evaluation result on the product quality. Existing machine learning methods in the prior art can be used to obtain the above evaluation results, which will not be elaborated here. In the following text, how to generate an evaluation result on the equipment operation stability will be mainly introduced.

[0040] Secondly, the analysis module generates a number of speculation algorithms and a number of candidate speculation algorithms in advance, determines the final speculation algorithm among the number of speculation algorithms, and determines the final candidate speculation algorithm among the number of candidate speculation algorithms. Both the final speculation algorithm and the final candidate speculation algorithm are algorithms that are relatively suitable for speculation processing. After that, the algorithm that is most suitable for speculation processing is also selected from the final speculation algorithm and the final candidate speculation algorithm, so as to obtain the equipment speculation operation data at the speculation moment.

[0041] Thirdly, the analysis module obtains a number of equipment real operation data at past moments and a number of equipment speculation operation data at past moments. Here, the equipment speculation operation data is actually output by the final speculation algorithm. Subsequently, for each past moment, the analysis module calculates the equipment operation data difference between the equipment real operation data at the past moment and the equipment speculation operation data at the past moment. The analysis module also generates a difference speculation algorithm based on a number of equipment operation data differences. The difference speculation algorithm can be generated by the prior art and will not be elaborated here.

[0042] Finally, if the final speculation algorithm is used for speculation processing, the analysis module calculates the final device speculation operation data at the speculation moment based on the device speculation operation data at the speculation moment and the difference in the device speculation operation data at the speculation moment output by the difference speculation algorithm. The specific method can be to add the device speculation operation data at the speculation moment and the difference in the device speculation operation data at the speculation moment. If the final candidate speculation algorithm is used for speculation processing, the analysis module directly uses the device speculation operation data at the speculation moment as the final device speculation operation data. Then, the analysis module generates an evaluation result regarding the device operation stability based on the final device speculation operation data. For the sake of easy understanding, the present invention takes the speculation of the temperature data of the device in the operating state as an example. When the speculated temperature data of the device during operation exceeds the threshold, it is determined that the device operation is unstable. After that, the comprehensive evaluation module integrates different evaluation results to generate a comprehensive evaluation result. Specifically, the expert evaluation method can be used to integrate different evaluation results, which will not be elaborated herein.

[0043] The above method comprehensively considers the processing flow efficiency, the implementation effect of environmental protection measures, product revenue, product quality, and device operation stability to evaluate the wet regeneration cleaning technology of waste lead paste, and can ensure the intelligence, scientificity, and comprehensiveness of the evaluation method.

[0044] Further, the process by which the analysis module generates several speculation algorithms includes: First, the analysis module determines several factors that affect the speculation result. Second, the analysis module generates several factor combinations, each factor combination consisting of different factors, and respectively generates several speculation algorithms corresponding to the several factor combinations.

[0045] Specifically, the process of generating several speculation algorithms is introduced. Before starting the introduction, it should also be noted that the present invention speculates on the temperature data of the device in the operating state. Of course, the present invention can also speculate on other data of the device in the operating state. First step, the analysis module determines several factors that affect the speculation result, such as including environmental temperature, heat dissipation conditions, workload, material characteristics, process parameters, etc. Second step, the analysis module generates several factor combinations, each factor combination consisting of different factors, that is, the total number of different factors in each factor combination is not fixed. For example, a factor combination can be "environmental temperature, heat dissipation conditions, process parameters". Then, several speculation algorithms corresponding to the several factor combinations are respectively generated. For example, a speculation algorithm corresponding to the factor combination "environmental temperature, heat dissipation conditions, process parameters" is generated, that is, this speculation algorithm speculates on the temperature data of the device in the operating state based on the environmental temperature data, heat dissipation condition data, and process parameter data.

[0046] Further, the process by which the analysis module generates a number of candidate speculation algorithms includes: the analysis module generates a number of candidate speculation algorithms corresponding to a number of factor combinations respectively according to the generated number of factor combinations.

[0047] Specifically, continue to introduce the process by which the analysis module generates a number of candidate speculation algorithms. The analysis module generates a number of candidate speculation algorithms corresponding to a number of factor combinations respectively based on the generated number of factor combinations, that is, the number of factor combinations generated when generating a number of speculation algorithms. For example, it also generates a candidate speculation algorithm corresponding to the factor combination "ambient temperature, heat dissipation condition, process parameter". It should be noted that for each speculation algorithm, there is always a corresponding candidate speculation algorithm, and their corresponding factor combinations are the same.

[0048] Further, the speculation algorithm is pre-trained by the analysis module using a training data set. The training data set consists of different training data, and the training data includes the actual operation data of the device at past moments and different factor data corresponding to the past moments.

[0049] Specifically, the generation processes of the speculation algorithm and the candidate speculation algorithm have been described above. It should also be noted that the speculation algorithm needs to be pre-trained by the analysis module using a training data set. The speculation algorithm can be, for example, a machine learning algorithm, while the candidate speculation algorithm does not need to be pre-trained by the analysis module. The candidate speculation algorithm can, for example, calculate the product of each factor data and its weight and calculate the sum of all the products. Among them, the training data includes the actual operation data of the device at past moments and different factor data corresponding to the past moments. For the sake of understanding, for example, a training data is "the actual temperature data at 9 o'clock in the morning of the previous day, and the ambient temperature data, heat dissipation condition data, and process parameter data at 9 o'clock in the morning of the previous day".

[0050] Further, the analysis module determines the final speculation algorithm among a number of speculation algorithms and determines the final candidate speculation algorithm among a number of candidate speculation algorithms, including the following steps:

[0051] The analysis module selects a speculation algorithm from a number of speculation algorithms, and at the same time selects a candidate speculation algorithm corresponding to the selected speculation algorithm from a number of candidate speculation algorithms;

[0052] The analysis module determines a number of past moments, and the analysis module makes the selected speculation algorithm perform a number of speculation processes to obtain the first device speculation operation data at a number of past moments, and makes the selected candidate speculation algorithm also perform a number of speculation processes to obtain the second device speculation operation data at a number of past moments;

[0053] The analysis module obtains the actual operation data of the device at a number of determined past moments, calculates the first accuracy of the selected speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the first speculative operation data of the device at a number of past moments, calculates the second accuracy of the selected candidate speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the second speculative operation data of the device at a number of past moments, and calculates the comprehensive accuracy according to the first accuracy and the second accuracy;

[0054] The analysis module determines whether there is a speculation algorithm that has not been selected. If so, it jumps to the initial steps of selecting the speculation algorithm and the candidate speculation algorithm. If not, the selected speculation algorithm and the selected candidate speculation algorithm corresponding to the maximum comprehensive accuracy are respectively regarded as the final speculation algorithm and the final candidate speculation algorithm.

[0055] Specifically, the process of the analysis module determining the final speculation algorithm among a number of speculation algorithms and determining the final candidate speculation algorithm among a number of candidate speculation algorithms is described. First step, the analysis module selects a speculation algorithm from a number of speculation algorithms, and at the same time selects a candidate speculation algorithm corresponding to the selected speculation algorithm from a number of candidate speculation algorithms. Second step, the analysis module determines a number of past moments, enables the selected speculation algorithm to perform a number of speculation processes to obtain the first speculative operation data of the device at a number of past moments, and at the same time enables the selected candidate speculation algorithm to also perform a number of speculation processes to obtain the second speculative operation data of the device at a number of past moments. Third step, the analysis module also obtains the actual operation data of the device at these determined past moments, calculates the first accuracy of the selected speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the first speculative operation data of the device at a number of past moments, calculates the second accuracy of the selected candidate speculation algorithm using a preset formula based on the actual operation data of the device at a number of past moments and the second speculative operation data of the device at a number of past moments. The preset formula will be described in detail below. Subsequently, the comprehensive accuracy is calculated based on the first accuracy and the second accuracy. For example, the product of the first accuracy and its weight, and the product of the second accuracy and its weight can be calculated first, and then the sum of the two products is calculated. Fourth step, the analysis module determines whether there is a speculation algorithm that has not been selected. If so, it jumps to the initial steps of selecting the speculation algorithm and the candidate speculation algorithm, that is, the first step, to select the speculation algorithm and the candidate speculation algorithm that have not been selected. If not, among all the comprehensive accuracies, the selected speculation algorithm corresponding to the maximum comprehensive accuracy and the selected candidate speculation algorithm are respectively regarded as the final speculation algorithm and the final candidate speculation algorithm.

[0056] Further, the analysis module selects one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the device speculation operation data at the speculation moment, including the following steps:

[0057] The analysis module obtains the device real operation data at several past moments within the past time period 1 before the current moment, and for each past moment, the analysis module obtains different factor data corresponding to the past moment. The analysis module also performs training processing on the final speculation algorithm.

[0058] The analysis module determines several past moments within the past time period 2 before the current moment, and the analysis module makes the final speculation algorithm perform several speculation processes to obtain the third device speculation operation data at several past moments, and makes the final candidate speculation algorithm also perform several speculation processes to obtain the fourth device speculation operation data at several past moments.

[0059] The analysis module obtains the device real operation data at the determined several past moments, calculates the third accuracy of the final speculation algorithm using a preset formula based on the device real operation data at several past moments and the third device speculation operation data at several past moments, and calculates the fourth accuracy of the final candidate speculation algorithm using a preset formula based on the device real operation data at several past moments and the fourth device speculation operation data at several past moments.

[0060] The analysis module determines whether the third accuracy is greater than or equal to the fourth accuracy. If so, it selects the final speculation algorithm; if not, it selects the final candidate speculation algorithm. And the analysis module uses the selected algorithm for speculation processing to obtain the device speculation operation data at the speculation moment.

[0061] Specifically, the process of the analysis module selecting one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the device speculation operation data at the speculation moment is introduced. Before starting the introduction, it should also be noted that a large number of training data in the training dataset used above are generated based on historical devices of the same type as the device for which the temperature data needs to be speculated. The selected speculation algorithm and the selected candidate speculation algorithm above also speculate on the temperature data of historical devices of the same type as the device for which the temperature data needs to be speculated. The device real operation data at past moments and the device speculation operation data at past moments of historical devices of the same type as the device for which the temperature data needs to be speculated are also used when generating the difference speculation algorithm above. However, if the device for which the temperature data needs to be speculated is a new model, the algorithm parameters in the final speculation algorithm may need to be updated. Since the device operation duration is not long enough, there is a lack of device real operation data. In order to ensure the accuracy of speculation processing in this case, the following method is proposed:

[0062] First, the analysis module obtains the actual operation data of the device at several past moments within the first past time period before the current moment. For each past moment, the analysis module also obtains different factor data corresponding to the past moment. That is to say, the analysis module generates a new training dataset based on the actual operation data of the device at several moments since the device started running, and different factor data corresponding to several moments respectively. On this basis, the analysis module also performs training processing on the final prediction algorithm. Second, the analysis module determines several past moments within the second past time period before the current moment. It should be noted that the second past time period is after the first past time period. For example, the second past time period starts after the first past time period ends, and the second past time period is shorter than the first past time period. Subsequently, the analysis module makes the final prediction algorithm perform several prediction processes to obtain the third predicted operation data of the device at several past moments, and makes the final candidate prediction algorithm also perform several prediction processes to obtain the fourth predicted operation data of the device at several past moments. Third, the analysis module obtains the actual operation data of the device at the determined several past moments, calculates the third accuracy of the final prediction algorithm using a preset formula based on the actual operation data of the device at several past moments and the third predicted operation data of the device at several past moments, calculates the fourth accuracy of the final candidate prediction algorithm using a preset formula based on the actual operation data of the device at several past moments and the fourth predicted operation data of the device at several past moments. The preset formula will be described in detail below. Fourth, the analysis module determines whether the third accuracy is greater than or equal to the fourth accuracy. If so, it selects the final prediction algorithm. If not, it selects the final candidate prediction algorithm. After that, the analysis module uses the selected algorithm to perform prediction processing, so as to obtain the predicted operation data of the device at the prediction moment.

[0063] Furthermore, the preset formula is where δ is the accuracy, k is the total number of actual operation data of the device, α i is the i-th actual operation data of the device, β i is the i-th predicted operation data of the device, and χ is the maximum actual operation data among all the actual operation data of the device.

[0064] Specifically, the accuracy of the algorithm can be calculated through the preset formula The larger the accuracy, the better the performance of the algorithm in performing prediction processing. δ is the accuracy, k is the total number of actual operation data of the device, which is the same as the total number of predicted operation data of the device. α i is the i-th actual operation data of the device, β i is the i-th predicted operation data of the device, and χ is the maximum actual operation data among all the actual operation data of the device.

[0065] Referring to Figure 2 as shown, the present invention also provides a comprehensive evaluation system for the wet regeneration cleaning technology of waste lead paste, including a preparation module, an analysis module, and a comprehensive evaluation module, which are used to implement the comprehensive evaluation method for the wet regeneration cleaning technology of waste lead paste described above. Among them, the functions of each module are as follows:

[0066] The preparation module is used to collect process data sets, environmental protection-related data sets, equipment operation data sets, cost data sets, and product test data sets;

[0067] The analysis module is used to analyze and process the process data set to obtain an evaluation result on the efficiency of the processing process, analyze and process the environmental protection-related data set to obtain an evaluation result on the implementation effect of environmental protection measures, analyze and process the cost data set to obtain an evaluation result on the product revenue, and analyze and process the product test data set to obtain an evaluation result on the product quality; at the same time, it is used to generate a number of speculation algorithms and a number of candidate speculation algorithms, determine the final speculation algorithm among the number of speculation algorithms, determine the final candidate speculation algorithm among the number of candidate speculation algorithms, and select one of the final speculation algorithm and the final candidate speculation algorithm for speculation processing to obtain the device speculation operation data at the speculation moment; and it is used to obtain the device real operation data at a number of past moments and the device speculation operation data at a number of past moments, calculate the device operation data difference between the device real operation data at the past moment and the device speculation operation data at the past moment for each past moment, and also generate a difference speculation algorithm based on a number of device operation data differences; it is also used to, in the case of using the final speculation algorithm for speculation processing, calculate the final device speculation operation data at the speculation moment according to the device speculation operation data at the speculation moment and the device speculation operation data difference at the speculation moment output by the difference speculation algorithm, and in the case of using the final candidate speculation algorithm for speculation processing, directly use the device speculation operation data at the speculation moment as the final device speculation operation data, and generate an evaluation result on the device operation stability based on the final device speculation operation data;

[0068] The comprehensive evaluation module is used to integrate different evaluation results to generate a comprehensive evaluation result.

[0069] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0070] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The above program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0071] The above technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0072] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

[0073] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for wet regeneration and cleaning technology of waste lead paste, characterized in that: The steps include: The preparation module collects a process data set, an environmental protection related data set, an equipment operation data set, a cost data set, and a product test data set, and the analysis module analyzes and processes the process data set to obtain an evaluation result on the efficiency of the process, analyzes and processes the environmental protection related data set to obtain an evaluation result on the effect of the implementation of environmental protection measures, analyzes and processes the cost data set to obtain an evaluation result on the product benefit, and analyzes and processes the product test data set to obtain an evaluation result on the product quality; The analysis module generates a plurality of inference algorithms and a plurality of candidate inference algorithms, determines a final inference algorithm from among the plurality of inference algorithms, determines a final candidate inference algorithm from among the plurality of candidate inference algorithms, and selects one of the final inference algorithm and the final candidate inference algorithm for inference processing to obtain inference operation data of the device at the inference time; The analysis module obtains the actual operation data of the device at a number of past moments and the estimated operation data of the device at a number of past moments, and for each past moment, the analysis module calculates the device operation data difference between the actual operation data of the device at the past moment and the estimated operation data of the device at the past moment, and the analysis module also generates a difference estimation algorithm based on the differences of the device operation data; When the final speculation algorithm is used for speculation processing, the analysis module calculates the final device speculation operation data at the speculation time based on the device speculation operation data at the speculation time and the difference of the device speculation operation data at the speculation time output by the difference speculation algorithm. When the final candidate speculation algorithm is used for speculation processing, the analysis module directly uses the device speculation operation data at the speculation time as the final device speculation operation data. Subsequently, the analysis module generates an evaluation result on the device operation stability based on the final device speculation operation data. The comprehensive evaluation module integrates different evaluation results to generate a comprehensive evaluation result.

2. The method according to claim 1, characterized in that The process of the analysis module generating several inference algorithms includes: first, the analysis module determines several factors that affect the inference results; second, the analysis module generates several factor combinations, each factor combination is composed of different factors, and generates several inference algorithms corresponding to the several factor combinations.

3. The method according to claim 2, characterized in that The process of the analysis module generating a plurality of candidate inference algorithms includes: the analysis module generates a plurality of candidate inference algorithms corresponding to the plurality of factor combinations according to the plurality of factor combinations that have been generated.

4. The method according to claim 3, characterized in that The inference algorithm is pre-trained by the analysis module using a training data set, where the training data set consists of different training data, including actual operation data of the equipment at past moments, and different factor data corresponding to the past moments.

5. The method according to claim 4, characterized in that The analysis module determines a final inference algorithm from among several inference algorithms, and determines a final candidate inference algorithm from among several candidate inference algorithms, including the following steps: The analysis module selects a speculation algorithm from among several speculation algorithms, and simultaneously selects a candidate speculation algorithm corresponding to the selected speculation algorithm from among several candidate speculation algorithms; The analysis module determines a number of past moments, and the analysis module causes the selected inference algorithm to perform inference processing a number of times to obtain the first device inference operation data of the number of past moments, and causes the selected candidate inference algorithm to perform inference processing a number of times to obtain the second device inference operation data of the number of past moments; The analysis module obtains the determined real operation data of the device at several past moments, calculates the first accuracy of the selected inference algorithm based on the real operation data of the device at several past moments and the first inferred operation data of the device at several past moments using a preset formula, calculates the second accuracy of the selected candidate inference algorithm based on the real operation data of the device at several past moments and the second inferred operation data of the device at several past moments using a preset formula, and calculates the comprehensive accuracy according to the first accuracy and the second accuracy; The analysis module determines whether there is an inference algorithm that has not been selected. If so, it jumps to the initial step of selecting the inference algorithm and the candidate inference algorithm. If not, the selected inference algorithm and the selected candidate inference algorithm corresponding to the maximum comprehensive accuracy are respectively regarded as the final inference algorithm and the final candidate inference algorithm.

6. The method according to claim 5, characterized in that The analysis module selects one of the final speculation algorithm and the final candidate speculation algorithm to perform speculation processing to obtain the device speculation operation data at the speculation time, including the following steps: The analysis module obtains the real operation data of the equipment at several past moments within a past time period before the current moment, and for each past moment, the analysis module obtains different factor data corresponding to the past moment, and the analysis module also performs training processing on the final inference algorithm; The analysis module determines a number of past moments in a past time period 2 before the current moment, and the analysis module causes the final speculation algorithm to perform speculation processing a number of times to obtain the third device speculation operation data at the number of past moments, and causes the final candidate speculation algorithm to also perform speculation processing a number of times to obtain the fourth device speculation operation data at the number of past moments; The analysis module obtains the determined real operation data of the device at several past moments, calculates the third accuracy of the final inference algorithm using a preset formula based on the real operation data of the device at several past moments and the inferred operation data of the third device at several past moments, and calculates the fourth accuracy of the final candidate inference algorithm using a preset formula based on the real operation data of the device at several past moments and the inferred operation data of the fourth device at several past moments; The analysis module determines whether the third accuracy is greater than or equal to the fourth accuracy. If yes, the analysis module selects a final inference algorithm. If not, the analysis module selects a final candidate inference algorithm. The analysis module uses the selected algorithm to perform inference processing to obtain the device inference operation data at the inference time.

7. The method according to claim 6, characterized in that The default formula is Among them, δ is the accuracy, k is the total number of real operation data of the equipment, and α i is the actual operation data of the i-th device, β i is the estimated operation data of the ith device, and χ is the largest device actual operation data among all device actual operation data.

8. A comprehensive evaluation system for wet regeneration and cleaning technology of waste lead paste, used to implement the method according to any one of claims 1 to 7, characterized in that: Includes the following modules: A preparation module is used to collect process data sets, environmental protection related data sets, equipment operation data sets, cost data sets, and product test data sets; An analysis module, used to analyze and process a process data set to obtain an evaluation result on the efficiency of the process, to analyze and process an environmental protection-related data set to obtain an evaluation result on the effect of the implementation of environmental protection measures, to analyze and process a cost data set to obtain an evaluation result on the product benefit, and to analyze and process a product test data set to obtain an evaluation result on the product quality; and to generate a plurality of inference algorithms and a plurality of candidate inference algorithms, determine a final inference algorithm from a plurality of inference algorithms, determine a final candidate inference algorithm from a plurality of candidate inference algorithms, and select one of the final inference algorithm and the final candidate inference algorithm for inference processing to obtain the equipment inference operation data at the inference time; and to obtain the equipment real operation data at a plurality of past moments, and the equipment inference operation data at a plurality of past moments, and for each past moment, calculate the equipment operation data difference between the equipment real operation data at the past moment and the equipment inference operation data at the past moment, and generate a difference inference algorithm based on the plurality of equipment operation data differences; It is also used to calculate the final device speculation operation data at the speculation time based on the device speculation operation data at the speculation time and the difference of the device speculation operation data at the speculation time output by the difference speculation algorithm when the final speculation algorithm is used for speculation processing; when the final candidate speculation algorithm is used for speculation processing, directly use the device speculation operation data at the speculation time as the final device speculation operation data, and generate an evaluation result on the device operation stability based on the final device speculation operation data; The comprehensive evaluation module is used to integrate different evaluation results to generate comprehensive evaluation results.

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