A process design operation system suitable for intelligent production of engineering machinery

Abstract

The application belongs to the technical field of engineering machinery production control, and specifically relates to a process design operation system suitable for intelligent production of engineering machinery, which comprises a server, a raw material classification consumption storage supervision module, a production process collection and disassembly module, a process machining rationality evaluation module, a process equipment strategy analysis module and a process operation risk decision module; the application analyzes the types of raw materials required in the production process of engineering machinery one by one, so as to timely investigate the consumption abnormality reasons of corresponding raw materials and replenish the raw materials, ensure the stable production of the corresponding engineering machinery production and machining process, improve the machining management efficiency and reduce the workload of the management personnel, and through the machining rationality analysis of the target process, the process equipment strategy analysis and the process operation risk analysis, the overall analysis of each process in the corresponding engineering machinery production and machining process is realized, which is beneficial to the management personnel to subsequently take corresponding supervision measures to improve each process.

Description

Technical Field

[0001] This invention relates to the field of production control technology for construction machinery, specifically a process design and operation system suitable for intelligent production of construction machinery. Background Technology

[0002] Construction machinery generally refers to a type of machinery used in building construction, including concrete machinery, road construction machinery, tunnel boring machinery, steel bar processing machinery, and aerial work machinery. In the production process of construction machinery, quality control and process control are generally required to ensure that the machinery produced is of qualified quality and can meet the set requirements.

[0003] Currently, it is difficult to conduct a reasonable analysis of the daily raw material consumption in the production and processing of construction machinery. It is impossible to comprehensively evaluate the effectiveness of raw material consumption supervision and provide timely early warnings for the replenishment of various raw materials. Furthermore, it is difficult to conduct a comprehensive analysis of each process in the production and processing flow, which makes it impossible for managers to take corresponding supervision measures to improve each process in the future.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a process design operation system suitable for intelligent production of engineering machinery. It solves the problems of existing technologies being unable to comprehensively evaluate the monitoring effect of raw material consumption and provide timely early warning of replenishment of various raw materials, and being unable to fully analyze each process in the production and processing flow, which makes it impossible for managers to implement monitoring measures to improve each process in the future.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A process design and operation system suitable for intelligent production of engineering machinery includes a server, a raw material classification, consumption and storage monitoring module, a production process acquisition and disassembly module, a process processing rationality assessment module, a process equipment strategy analysis module, and a process operation risk decision-making module. The raw material classification, consumption and storage monitoring module analyzes the types of raw materials required in the production process of engineering machinery one by one. Through analysis, it identifies the raw materials that are consumed abnormally and generates the corresponding consumption warning signals. Through analysis, it determines whether to generate a strong raw material monitoring signal and whether to generate a first-level or second-level raw material replenishment warning signal. The strong raw material monitoring signal, the consumption warning signal of the corresponding raw material that is consumed abnormally, and the first-level or second-level raw material replenishment warning signal are sent to the background monitoring terminal via the server.

[0008] The production process acquisition and disassembly module acquires the complete production process of the corresponding engineering machinery, obtains all its production processes based on the complete production process of the corresponding engineering machinery, and marks the corresponding production process as target process i, i = {1, 2, ..., n}, where n represents the number of production processes and n is a natural number greater than 1; the process processing rationality assessment module performs processing rationality analysis on target process i, and generates a strong supervision signal or equipment strategy analysis signal for target process i accordingly. The strong supervision signal of the process is sent to the background supervision terminal via the server, and the equipment strategy analysis signal is sent to the process equipment strategy analysis module via the server.

[0009] After receiving the equipment strategy analysis signal, the process equipment strategy analysis module will perform process equipment strategy analysis on the target process i. Based on this, it will generate a strong supervision signal or a process operation risk analysis signal for the target process i. The strong supervision signal will be sent to the background monitoring terminal via the server, and the process operation risk analysis signal will be sent to the process operation risk decision module via the server. After receiving the process operation risk analysis signal, the process operation risk decision module will perform process operation risk analysis on the target process i. Based on this, it will determine whether to generate a strong supervision signal for the target process i. After generating the strong supervision signal, it will be sent to the background monitoring terminal via the server.

[0010] Furthermore, the specific operation process of the raw material classification, consumption, and inventory monitoring module includes:

[0011] The types of raw materials required in the production process of the corresponding engineering machinery are obtained, and the corresponding types of raw materials are marked as target raw materials u, u = {1, 2, ..., m}, where m is the number of raw material types and m is a natural number greater than 1; the daily consumption of the corresponding target raw material u during the processing and production of the corresponding engineering machinery is collected, and the daily consumption is compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is not within the preset daily consumption range, it is determined that the consumption of the corresponding target raw material u is abnormal on the corresponding processing day, and the corresponding target raw material u is marked as an abnormal consumption raw material and a consumption warning signal of the corresponding target raw material u is generated.

[0012] All raw materials with abnormal consumption are obtained. The deviation of the daily consumption of the corresponding raw material from the corresponding preset daily consumption range is marked as the daily consumption deviation value. The daily consumption deviation value is compared with the preset daily consumption deviation threshold of the corresponding target raw material u. If the daily consumption deviation value exceeds the corresponding preset daily consumption deviation threshold, the corresponding raw material with abnormal consumption is marked as an over-deviation raw material. The raw material consumption evaluation coefficient is obtained by numerically calculating the quantity of raw materials with abnormal consumption and the quantity of over-deviation raw materials. The raw material consumption evaluation coefficient is compared with the preset raw material consumption evaluation coefficient threshold. If the raw material consumption evaluation coefficient exceeds the preset raw material consumption evaluation coefficient threshold, a strong raw material supervision signal is generated.

[0013] During a defined monitoring period, the daily consumption of target raw material u for each processing day within that period is collected. A raw material consumption set is created from all daily consumption values ​​of target raw material u during the monitoring period. The summation and average of these raw material consumption sets are then used to obtain the average daily consumption of target raw material u. The remaining storage level of target raw material u is obtained, and the ratio between the remaining storage level and the corresponding average daily consumption is calculated to obtain a replenishment urgency coefficient. This coefficient is then compared with a preset replenishment urgency coefficient range. If the replenishment urgency coefficient does not exceed the minimum value of the preset replenishment urgency coefficient range, a first-level warning signal for raw material replenishment of the corresponding target raw material u is generated. If the replenishment urgency coefficient is within the preset replenishment urgency coefficient range, a second-level warning signal for raw material replenishment of the corresponding target raw material u is generated. If the replenishment urgency coefficient exceeds the maximum value of the preset replenishment urgency coefficient range, it is determined that the storage level of the corresponding target raw material u is sufficient, and no replenishment warning signal is generated.

[0014] Furthermore, if no consumption warning signal for the corresponding target raw material u is generated, when establishing the raw material consumption set for the target raw material u, the variance of the raw material consumption set is calculated to obtain the daily consumption dispersion value of the raw material. The daily average consumption of the target raw material u and the daily consumption dispersion value are compared with the corresponding preset daily average consumption range and preset daily consumption dispersion threshold. If the daily average consumption is within the preset daily average consumption range and the daily consumption dispersion value does not exceed the preset daily consumption dispersion threshold, it is determined that the consumption supervision of the target raw material u during the supervision period is normal. Otherwise, it is determined that the consumption supervision of the target raw material u during the supervision period is abnormal and a corresponding consumption fluctuation signal for the target raw material u is generated. The corresponding consumption fluctuation signal for the target raw material u is then sent to the back-end supervision terminal via the server.

[0015] Furthermore, the specific operation process of the process rationality assessment module includes:

[0016] The solid waste volume, production exhaust gas volume, and production waste liquid volume of target process i during the production and processing of the corresponding engineering machinery on the same day are collected. The solid waste volume, production exhaust gas volume, and production waste liquid volume are compared with the corresponding preset range of target process i. If at least one of the solid waste volume, production exhaust gas volume, and production waste liquid volume is not within the corresponding preset range, a process strong supervision signal for target process i is generated. If the solid waste volume, production exhaust gas volume, and production waste liquid volume are all within the corresponding preset range, the maximum and minimum values ​​of the corresponding preset solid waste volume range are summed and the average value is taken to obtain the solid waste judgment value. Similarly, the exhaust gas judgment value and waste liquid judgment value are obtained.

[0017] The difference between the solid waste quantity value and the solid waste judgment value is calculated and the average value is taken to obtain the solid waste difference value. Similarly, the waste gas difference value and waste liquid difference value are obtained. The solid waste difference value, waste gas difference value and waste liquid difference value are numerically calculated to obtain the rationality coefficient. The rationality coefficient is numerically compared with the preset rationality coefficient threshold of the target process i. If the rationality coefficient exceeds the preset rationality coefficient threshold, a strong process supervision signal is generated; otherwise, an equipment strategy analysis signal is generated and sent to the process equipment strategy analysis module via the server.

[0018] Furthermore, the specific operation process of the process equipment strategy analysis module includes:

[0019] The production and processing equipment for the target process i is obtained. The stable working time and number of failures of the corresponding production and processing equipment on the same day are collected. The duration of each failure is summed to obtain the failure running time. The ratio of the failure running time to the stable working time is calculated to obtain the failure duration ratio. The time difference between two adjacent failures is calculated to obtain the single failure interval. The single failure intervals of all failures are summed and averaged to obtain the average failure interval.

[0020] The initial assessment value of the target process i is obtained by normalizing the proportion of failure duration, the number of failures, and the average duration of failure intervals. The level influence value of the corresponding production and processing equipment is obtained through precise analysis of equipment level. The level influence value is multiplied by the initial assessment value of the process, and the product is marked as the process equipment strategy value. The process equipment strategy value is compared with the corresponding preset process equipment strategy threshold. If the process equipment strategy value exceeds the preset process equipment strategy threshold, a strong process supervision signal is generated. Otherwise, an operational risk analysis signal is generated and sent to the process operational risk decision module via the server.

[0021] Furthermore, the specific analysis process for precise equipment level analysis is as follows:

[0022] The system obtains the duration of operation of the corresponding production and processing equipment for target process i in each historical operation, as well as the duration of operation in its unsuitable environmental state during each operation. If the duration of operation exceeds a preset duration threshold or the duration of operation in its unsuitable environmental state exceeds a preset unsuitable environmental state duration threshold, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1. The system also obtains the production interval duration and the commissioning interval duration of target process i, sums the production interval duration and commissioning interval duration, and takes the average value to obtain the equipment comprehensive evaluation duration. The system sums the duration of operation of the corresponding production and processing equipment for target process i to obtain the total equipment operation time, and marks the number of operations corresponding to the operation symbol YF-1 as the high-loss operation frequency.

[0023] The equipment comprehensive evaluation time, total equipment operation time, and high-loss operation frequency of the corresponding production and processing equipment are normalized and calculated to obtain the level analysis value. Several preset level analysis value ranges corresponding to the production and processing equipment are retrieved from the server, as well as a set of influencing factors corresponding to each set of level analysis value ranges. The level analysis value is compared with all preset level analysis value ranges one by one. The preset level analysis value ranges that cover the level analysis value are marked as the selected ranges, and the influencing factors corresponding to the selected ranges are marked as the level influence values ​​of the production and processing equipment.

[0024] Furthermore, the specific operation process of the process operation risk decision-making module includes:

[0025] The frequency of safety accidents and the frequency of non-standard operator behavior during the daily production and processing of target process i are obtained. The frequency of safety accidents and the frequency of non-standard operator behavior are compared with the preset safety accident frequency threshold and the preset non-standard behavior frequency threshold for target process i. If the frequency of safety accidents exceeds the preset safety accident frequency threshold or the frequency of non-standard operator behavior exceeds the preset non-standard behavior frequency threshold, a strong supervision signal for target process i is generated. If the frequency of safety accidents does not exceed the preset safety accident frequency threshold and the frequency of non-standard operator behavior does not exceed the preset non-standard behavior frequency threshold, the operation risk of the process is judged to be qualified.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. In this invention, the raw material classification and consumption monitoring module analyzes the types of raw materials required in the production process of the corresponding engineering machinery one by one. The analysis identifies the raw materials that are consumed abnormally and generates the corresponding consumption warning signal. The analysis also determines whether to generate a strong monitoring signal for raw materials and whether to generate a first-level or second-level warning signal for raw material replenishment. This allows for timely investigation of the causes of abnormal consumption of the corresponding raw materials and timely replenishment of the corresponding raw materials. Furthermore, it strengthens the monitoring of subsequent raw material consumption as needed, ensuring the stable operation of the corresponding engineering machinery production and processing process, improving processing management efficiency, and reducing the workload of management personnel.

[0028] 2. In this invention, by performing a processing rationality analysis on the target process i, a strong monitoring signal or an equipment strategy analysis signal for the target process i is generated. When generating the equipment strategy analysis signal, a process equipment strategy analysis is performed on the target process i, thereby generating a strong monitoring signal or a process operation risk analysis signal for the target process i. When generating the process operation risk analysis signal, a process operation risk analysis is performed on the corresponding target process i, thereby determining whether to generate a strong monitoring signal for the target process i. After generating the strong monitoring signal, it is sent to the background monitoring terminal by the server, realizing a comprehensive analysis of each process in the corresponding engineering machinery production and processing flow, which is beneficial for managers to subsequently improve the corresponding monitoring measures for each process. Attached Figure Description

[0029] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0030] Figure 1 This is an overall system block diagram of the present invention;

[0031] Figure 2 This is a communication block diagram between the server and the backend monitoring terminal in this invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: As Figure 1-2 As shown, the present invention proposes a process design operation system suitable for intelligent production of engineering machinery, including a server, a raw material classification, consumption and storage monitoring module, a production process acquisition and disassembly module, a process processing rationality assessment module, a process equipment strategy analysis module, and a process operation risk decision-making module. The server is communicatively connected to the raw material classification, consumption and storage monitoring module, the production process acquisition and disassembly module, the process processing rationality assessment module, the process equipment strategy analysis module, and the process operation risk decision-making module.

[0034] The raw material classification and consumption monitoring module analyzes each type of raw material required in the production process of the corresponding engineering machinery. Through analysis, it identifies raw materials with abnormal consumption and generates corresponding consumption warning signals. Further analysis determines whether to generate a strong raw material monitoring signal and a primary or secondary raw material replenishment warning signal. These signals are then sent via server to the backend monitoring terminal for timely investigation of abnormal raw material consumption and to facilitate timely replenishment, ensuring the stable operation of the engineering machinery production process. The specific operation process of the raw material classification and consumption monitoring module is as follows:

[0035] The types of raw materials required in the production process of the corresponding engineering machinery are obtained, and the corresponding types of raw materials are marked as target raw materials u, u = {1, 2, ..., m}, where m is the number of raw material types and m is a natural number greater than 1; the daily consumption of the corresponding target raw material u during the processing and production of the corresponding engineering machinery is collected, and the daily consumption is compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is within the preset daily consumption range, it is determined that the consumption of the corresponding target raw material u on the corresponding processing day is normal; if the daily consumption is not within the preset daily consumption range, it is determined that the consumption of the corresponding target raw material u on the corresponding processing day is abnormal, and the corresponding target raw material u is marked as an abnormal consumption raw material and a consumption warning signal for the corresponding target raw material u is generated.

[0036] All waste materials are obtained. The deviation of the daily consumption of the corresponding waste material from the corresponding preset daily consumption range is marked as the daily consumption deviation value. The daily consumption deviation value is compared with the preset daily consumption deviation threshold of the corresponding target material u. If the daily consumption deviation value exceeds the corresponding preset daily consumption deviation threshold, the corresponding waste material is marked as an over-deviation material. The material consumption evaluation coefficient XPu is obtained by numerically calculating the quantity of waste material YSu and the quantity of over-deviation material CPU using the formula XPu=a1*YSu+a2*CPu, where a1 and a2 are preset weight coefficients, and a2>a1>0.

[0037] It should be noted that the value of the raw material consumption assessment coefficient XPu is directly proportional to the amount of raw materials consumed abnormally (YSu) and the amount of raw materials exceeding the standard (CPu). The larger the value of the raw material consumption assessment coefficient XPu, the worse the raw material consumption supervision during the production and processing process, and the more necessary it is to strengthen the supervision of raw material consumption in the future. The raw material consumption assessment coefficient XPu is compared with the preset raw material consumption assessment coefficient threshold. If the raw material consumption assessment coefficient XPu exceeds the preset raw material consumption assessment coefficient threshold, a strong raw material supervision signal is generated so that the supervision of the raw material consumption process can be strengthened in a timely manner in the future, so as to achieve effective management of the corresponding engineering machinery production process. If the raw material consumption assessment coefficient XPu does not exceed the preset raw material consumption assessment coefficient threshold, the raw material consumption supervision of the corresponding processing day is judged to be qualified.

[0038] The monitoring period is set, preferably seven days. The daily consumption of the target raw material u is collected for each processing day during the monitoring period. A raw material consumption set is established based on all daily consumptions of the target raw material u during the monitoring period. The summation of the raw material consumption set is calculated, and the average value is taken to obtain the average daily consumption of the target raw material u. The remaining storage amount of the current target raw material u is obtained. The ratio of the remaining storage amount to the corresponding average daily consumption is calculated to obtain the replenishment urgency coefficient. It should be noted that the smaller the replenishment urgency coefficient, the more timely the replenishment of the corresponding target raw material u is required. The replenishment urgency coefficient is compared with the preset replenishment urgency coefficient range.

[0039] If the replenishment urgency coefficient does not exceed the minimum value of the preset replenishment urgency coefficient range, it indicates that the storage replenishment of the corresponding target raw material u is urgently needed, and a first-level early warning signal for raw material u is generated; if the replenishment urgency coefficient is within the preset replenishment urgency coefficient range, it indicates that the storage replenishment of the corresponding target raw material u can be carried out as needed, and a second-level early warning signal for raw material u is generated; if the replenishment urgency coefficient exceeds the maximum value of the preset replenishment urgency coefficient range, it indicates that the storage replenishment of the target raw material u is not needed, and it is determined that the storage of the corresponding target raw material u is sufficient and no raw material replenishment early warning signal is generated.

[0040] The production process acquisition and disassembly module acquires the complete production process of the corresponding construction machinery. Based on the complete production process, it obtains all production steps and marks the corresponding production steps as target steps i, i = {1, 2, ..., n}, where n represents the number of production steps and is a natural number greater than 1. The process processing rationality assessment module performs processing rationality analysis on target steps i, and generates a strong supervision signal or equipment strategy analysis signal for target steps i accordingly. The strong supervision signal is sent to the background monitoring terminal via the server, and the equipment strategy analysis signal is sent to the process equipment strategy analysis module via the server. The specific operation process of the process processing rationality assessment module is as follows:

[0041] The system collects the solid waste, production exhaust gas, and production waste liquid values ​​during the daily production and processing of the corresponding engineering machinery in target process i. These values ​​are then compared with the corresponding preset ranges for target process i. If at least one of these values ​​is outside the preset range, a strong monitoring signal for target process i is generated. If all three values ​​are within their respective preset ranges, the maximum and minimum values ​​of the corresponding preset solid waste value ranges are summed and averaged to obtain the solid waste determination value. Similarly, the exhaust gas and waste liquid determination values ​​are obtained. The difference between the solid waste value and the solid waste determination value is calculated and averaged to obtain the solid waste difference value. Similarly, the exhaust gas and waste liquid difference values ​​are obtained.

[0042] The rationality coefficient HXi is obtained by numerically calculating the solid waste difference GFi, waste gas difference FQi, and waste liquid difference FYi using the formula HXi = b1*GFi + b2*FQi + b3*FYi. Here, b1, b2, and b3 are preset weighting coefficients, and all of them are greater than zero. It should be noted that the value of the rationality coefficient HXi is directly proportional to the solid waste difference GFi, waste gas difference FQi, and waste liquid difference FYi. The larger the value of the rationality coefficient HXi, the more abnormal the processing of target process i is. The rationality coefficient HXi is compared with the preset rationality coefficient threshold of target process i. If the rationality coefficient HXi exceeds the preset threshold, it indicates that the subsequent monitoring of the processing of target process i needs to be strengthened, and a strong monitoring signal for the process is generated. If the rationality coefficient HXi does not exceed the preset threshold, a corresponding equipment strategy analysis signal for target process i is generated and sent to the process equipment strategy analysis module via the server.

[0043] After receiving the equipment strategy analysis signal, the process equipment strategy analysis module performs process equipment strategy analysis on the corresponding target process i. Based on this analysis, it generates a strong monitoring signal or a process operation risk analysis signal for target process i. The strong monitoring signal is sent to the background monitoring terminal via the server, and the process operation risk analysis signal is sent to the process operation risk decision module via the server. The specific operation process of the process equipment strategy analysis module is as follows:

[0044] The production and processing equipment for the target process i is obtained. The stable working time and number of failures of the corresponding production and processing equipment on the same day are collected. The duration of each failure is summed to obtain the failure running time. The ratio of the failure running time to the stable working time is calculated to obtain the failure duration ratio. The time difference between two adjacent failures is calculated to obtain the single failure interval. The single failure intervals of all failures are summed and averaged to obtain the average failure interval.

[0045] And through precise analysis of equipment level, the level influence value of the corresponding production and processing equipment is obtained. Specifically, the duration of operation of the corresponding production and processing equipment of target process i in each historical work process is obtained, as well as the working time in its unsuitable environment state during each operation. If the duration of operation exceeds the preset duration threshold or the working time in its unsuitable environment state exceeds the preset unsuitable environment state working time threshold, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1; the production interval time and the commissioning interval time of target process i are obtained, the production interval time and the commissioning interval time are summed and averaged to obtain the equipment comprehensive evaluation time; the total operating time of the equipment is obtained by summing the duration of operation of the corresponding production and processing equipment of target process i each time, and the number of operations corresponding to the operation symbol YF-1 is marked as high loss operation frequency.

[0046] Through formula The equipment comprehensive evaluation time ZPi, total equipment operating time ZYi, and high-loss operating frequency GPi of the corresponding production and processing equipment are normalized to obtain the level analysis value FJi. Among them, ep1, ep2, and ep3 are preset proportional coefficients, ep3 > ep2 > ep1 > 0. Furthermore, the larger the level analysis value FJi, the worse the equipment condition of the corresponding production and processing equipment. Several preset level analysis value ranges corresponding to the production and processing equipment are retrieved from the server, as well as a set of influencing factors corresponding to each set of level analysis value ranges. It should be noted that the larger the value of the preset level analysis value range, the larger the value of the corresponding influencing factor. The level analysis value FJi is compared with all preset level analysis value ranges one by one. The preset level analysis value ranges that cover the level analysis value are marked as selected ranges, and the influencing factors corresponding to the selected ranges are marked as the level influence values ​​of the production and processing equipment.

[0047] Through formula The initial evaluation value CTi is obtained by normalizing the failure duration percentage (GZi), failure count (GSi), and average failure interval (GJi) of target process i. Here, es1, es2, and es3 are preset proportional coefficients, and the values ​​of es1, es2, and es3 are all greater than zero. It should be noted that the value of the initial evaluation value CTi is directly proportional to the failure duration percentage (GZi) and failure count (GSi), and inversely proportional to the average failure interval (GJi). The larger the value of the initial evaluation value CTi, the worse the processing condition of the production equipment of target process i is, and the more supervision is needed in the subsequent production process.

[0048] The level influence value of the corresponding production and processing equipment is multiplied by the initial evaluation value of the process, and the product is marked as the process equipment strategy value CYi. The process equipment strategy value CYi is compared with the corresponding preset process equipment strategy threshold. If the process equipment strategy value CYi exceeds the preset process equipment strategy threshold, it indicates that the supervision of the target process i needs to be strengthened, and a strong supervision signal for the process is generated. If the process equipment strategy value CYi does not exceed the preset process equipment strategy threshold, an operational risk analysis signal is generated and sent to the process operational risk decision module by the server.

[0049] After receiving the process operation risk analysis signal, the process operation risk decision module will perform process operation risk analysis on the corresponding target process i, and determine whether to generate a strong supervision signal for target process i. After generating a strong supervision signal, the server will send it to the background supervision terminal. The specific operation process of the process operation risk decision module is as follows:

[0050] The frequency of safety accidents and the frequency of non-standard operator behavior during the daily production and processing of target process i are obtained. The frequency of safety accidents represents the number of safety accidents, and the frequency of non-standard operator behavior represents the number of times operators violate operating procedures. These frequencies are then compared with preset thresholds for the frequency of safety accidents and non-standard operator behavior for the corresponding target process i. If either the frequency of safety accidents exceeds the preset threshold or the frequency of non-standard operator behavior exceeds the preset threshold for non-standard operator behavior, it indicates that further supervision of the target process i is needed, and a strong supervision signal for target process i is generated. If neither the frequency of safety accidents nor the frequency of non-standard operator behavior exceeds the preset threshold, it indicates that further supervision of the target process i is not needed, and the process operation risk is deemed acceptable.

[0051] Example 2: The difference between this example and Example 1 is that if no consumption warning signal for the corresponding target raw material u is generated, when establishing the raw material consumption set for the target raw material u, the variance of the raw material consumption set is calculated to obtain the daily consumption dispersion value. The daily average consumption of the target raw material u and the daily consumption dispersion value are compared with the corresponding preset daily average consumption range and preset daily consumption dispersion threshold. If the daily average consumption is within the preset daily average consumption range and the daily consumption dispersion value does not exceed the preset daily consumption dispersion threshold, it indicates that the daily consumption deviation is small and the daily consumption is close to normal. In this case, it is determined that the consumption supervision of the target raw material u during the supervision period is normal. Otherwise, it is determined that the consumption supervision of the target raw material u during the supervision period is abnormal and a corresponding consumption fluctuation signal for the target raw material u is generated. The corresponding consumption fluctuation signal for the target raw material u is sent to the background supervision terminal via the server. When the corresponding management personnel receive the consumption fluctuation signal, they should promptly investigate the relevant causes so as to take corresponding targeted management measures.

[0052] The working principle of this invention is as follows: During use, the raw material classification and consumption monitoring module analyzes each type of raw material required in the corresponding engineering machinery production process. This analysis identifies abnormally consumed raw materials and generates corresponding consumption warning signals. Further analysis determines whether to generate a strong raw material monitoring signal, and whether to generate a primary or secondary raw material replenishment warning signal. This allows for timely investigation of abnormal raw material consumption and prompt replenishment. Furthermore, it strengthens subsequent raw material consumption monitoring as needed, ensuring the stable operation of the corresponding engineering machinery production process. The process processing rationality evaluation module analyzes the processing rationality of the target process i, based on… This generates a strong monitoring signal or equipment strategy analysis signal for target process i. When generating the equipment strategy analysis signal, the process equipment strategy analysis module performs process equipment strategy analysis on target process i, thereby generating a strong monitoring signal or process operation risk analysis signal for target process i. When generating the process operation risk analysis signal, the process operation risk decision module performs process operation risk analysis on the corresponding target process i, thereby determining whether to generate a strong monitoring signal for target process i. After generating the strong monitoring signal, the server sends it to the backend monitoring terminal, realizing a comprehensive analysis of each process in the corresponding engineering machinery production and processing flow, which is beneficial for managers to subsequently implement corresponding monitoring measures to improve each process.

[0053] The above formulas are all dimensionless numerical calculations. These formulas are derived from software simulations using collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The preferred embodiments do not describe all details exhaustively, nor do they limit the invention to specific implementations. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A process design and operation system suitable for intelligent production of engineering machinery, characterized in that, It includes a server, a raw material classification and consumption monitoring module, a production process data acquisition and disassembly module, a process processing rationality assessment module, a process equipment strategy analysis module, and a process operation risk decision-making module. The raw material classification and consumption monitoring module analyzes the types of raw materials required in the production process of engineering machinery one by one. Through analysis, it identifies the raw materials that are consumed abnormally and generates the corresponding consumption warning signal. Through analysis, it determines whether to generate a strong raw material monitoring signal and whether to generate a first-level or second-level raw material replenishment warning signal. The strong raw material monitoring signal, the consumption warning signal of the corresponding raw material that is consumed abnormally, and the first-level or second-level raw material replenishment warning signal are sent to the background monitoring terminal via the server. The production process acquisition and disassembly module acquires the complete production process of the corresponding engineering machinery, obtains all its production processes based on the complete production process of the corresponding engineering machinery, and marks the corresponding production process as target process i, i = {1, 2, ..., n}, where n represents the number of production processes and n is a natural number greater than 1; the process processing rationality assessment module performs processing rationality analysis on target process i, and generates a strong supervision signal or equipment strategy analysis signal for target process i accordingly. The strong supervision signal of the process is sent to the background supervision terminal via the server, and the equipment strategy analysis signal is sent to the process equipment strategy analysis module via the server. After receiving the equipment strategy analysis signal, the process equipment strategy analysis module will perform process equipment strategy analysis on the target process i. Based on this, it will generate a strong supervision signal or a process operation risk analysis signal for the target process i. The strong supervision signal will be sent to the background monitoring terminal via the server, and the process operation risk analysis signal will be sent to the process operation risk decision module via the server. After receiving the process operation risk analysis signal, the process operation risk decision module will perform process operation risk analysis on the target process i. Based on this, it will determine whether to generate a strong supervision signal for the target process i. After generating the strong supervision signal, it will be sent to the background monitoring terminal via the server.

2. The process design and operation system for intelligent production of engineering machinery according to claim 1, characterized in that, The specific operation process of the raw material classification, consumption, and inventory monitoring module includes: The types of raw materials required in the production process of the corresponding engineering machinery are obtained, and the corresponding types of raw materials are marked as target raw materials u, u = {1, 2, ..., m}, where m is the number of raw material types and m is a natural number greater than 1; the daily consumption of the corresponding target raw material u during the processing and production of the corresponding engineering machinery is collected, and the daily consumption is compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is not within the preset daily consumption range, it is determined that the consumption of the corresponding target raw material u is abnormal on the corresponding processing day, and the corresponding target raw material u is marked as an abnormal consumption raw material and a consumption warning signal of the corresponding target raw material u is generated. All raw materials with abnormal consumption are obtained. The deviation of the daily consumption of the corresponding raw material from the corresponding preset daily consumption range is marked as the daily consumption deviation value. The daily consumption deviation value is compared with the preset daily consumption deviation threshold of the corresponding target raw material u. If the daily consumption deviation value exceeds the corresponding preset daily consumption deviation threshold, the corresponding raw material with abnormal consumption is marked as an over-deviation raw material. The raw material consumption evaluation coefficient is obtained by numerically calculating the quantity of raw materials with abnormal consumption and the quantity of over-deviation raw materials. The raw material consumption evaluation coefficient is compared with the preset raw material consumption evaluation coefficient threshold. If the raw material consumption evaluation coefficient exceeds the preset raw material consumption evaluation coefficient threshold, a strong raw material supervision signal is generated. During a defined monitoring period, the daily consumption of target raw material u for each processing day within that period is collected. A raw material consumption set is created from all daily consumption values ​​of target raw material u during the monitoring period. The summation and average of these raw material consumption sets are then used to obtain the average daily consumption of target raw material u. The remaining storage level of target raw material u is obtained, and the ratio between the remaining storage level and the corresponding average daily consumption is calculated to obtain a replenishment urgency coefficient. This coefficient is then compared with a preset replenishment urgency coefficient range. If the replenishment urgency coefficient does not exceed the minimum value of the preset replenishment urgency coefficient range, a first-level warning signal for raw material replenishment of the corresponding target raw material u is generated. If the replenishment urgency coefficient is within the preset replenishment urgency coefficient range, a second-level warning signal for raw material replenishment of the corresponding target raw material u is generated. If the replenishment urgency coefficient exceeds the maximum value of the preset replenishment urgency coefficient range, it is determined that the storage level of the corresponding target raw material u is sufficient, and no replenishment warning signal is generated.

3. The process design and operation system for intelligent production of engineering machinery according to claim 2, characterized in that, If no consumption warning signal for the corresponding target raw material u is generated, when establishing the raw material consumption set for the target raw material u, the variance of the raw material consumption set is calculated to obtain the daily consumption dispersion value of the raw material. The daily average consumption of the target raw material u and the daily consumption dispersion value are compared with the corresponding preset daily average consumption range and preset daily consumption dispersion threshold. If the daily average consumption is within the preset daily average consumption range and the daily consumption dispersion value does not exceed the preset daily consumption dispersion threshold, it is determined that the consumption supervision of the target raw material u during the supervision period is normal. Otherwise, it is determined that the consumption supervision of the target raw material u during the supervision period is abnormal and a corresponding consumption fluctuation signal for the target raw material u is generated. The corresponding consumption fluctuation signal for the target raw material u is then sent to the back-end supervision terminal via the server.

4. The process design and operation system for intelligent production of engineering machinery according to claim 1, characterized in that, The specific operation process of the process rationality assessment module includes: The solid waste volume, production exhaust gas volume, and production waste liquid volume of target process i during the production and processing of the corresponding engineering machinery on the same day are collected. The solid waste volume, production exhaust gas volume, and production waste liquid volume are compared with the corresponding preset range of target process i. If at least one of the solid waste volume, production exhaust gas volume, and production waste liquid volume is not within the corresponding preset range, a process strong supervision signal for target process i is generated. If the solid waste volume, production exhaust gas volume, and production waste liquid volume are all within the corresponding preset range, the maximum and minimum values ​​of the corresponding preset solid waste volume range are summed and the average value is taken to obtain the solid waste judgment value. Similarly, the exhaust gas judgment value and waste liquid judgment value are obtained. The difference between the solid waste quantity value and the solid waste judgment value is calculated and the average value is taken to obtain the solid waste difference value. Similarly, the waste gas difference value and waste liquid difference value are obtained. The solid waste difference value, waste gas difference value and waste liquid difference value are numerically calculated to obtain the rationality coefficient. The rationality coefficient is numerically compared with the preset rationality coefficient threshold of the target process i. If the rationality coefficient exceeds the preset rationality coefficient threshold, a strong process supervision signal is generated; otherwise, an equipment strategy analysis signal is generated and sent to the process equipment strategy analysis module via the server.

5. A process design and operation system for intelligent production of engineering machinery according to claim 4, characterized in that, The specific operation process of the process equipment strategy analysis module includes: The production and processing equipment for the target process i is obtained. The stable working time and number of failures of the corresponding production and processing equipment on the same day are collected. The duration of each failure is summed to obtain the failure running time. The ratio of the failure running time to the stable working time is calculated to obtain the failure duration ratio. The time difference between two adjacent failures is calculated to obtain the single failure interval. The single failure intervals of all failures are summed and averaged to obtain the average failure interval. The initial assessment value of the target process i is obtained by normalizing the proportion of failure duration, the number of failures, and the average duration of failure intervals. The level influence value of the corresponding production and processing equipment is obtained through precise analysis of equipment level. The level influence value is multiplied by the initial assessment value of the process, and the product is marked as the process equipment strategy value. The process equipment strategy value is compared with the corresponding preset process equipment strategy threshold. If the process equipment strategy value exceeds the preset process equipment strategy threshold, a strong process supervision signal is generated. Otherwise, an operational risk analysis signal is generated and sent to the process operational risk decision module via the server.

6. A process design and operation system for intelligent production of engineering machinery according to claim 5, characterized in that, The specific analysis process for precise equipment level analysis is as follows: The system obtains the duration of operation of the corresponding production and processing equipment for target process i in each historical operation, as well as the duration of operation in its unsuitable environmental state during each operation. If the duration of operation exceeds a preset duration threshold or the duration of operation in its unsuitable environmental state exceeds a preset unsuitable environmental state duration threshold, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1. The system also obtains the production interval duration and the commissioning interval duration of target process i, sums the production interval duration and commissioning interval duration, and takes the average value to obtain the equipment comprehensive evaluation duration. The system sums the duration of operation of the corresponding production and processing equipment for target process i to obtain the total equipment operation time, and marks the number of operations corresponding to the operation symbol YF-1 as the high-loss operation frequency. The equipment comprehensive evaluation time, total equipment operation time and high loss operation frequency of the corresponding production and processing equipment are normalized to obtain the grade analysis value; The system retrieves several preset ranges of level analysis values ​​corresponding to the production and processing equipment from the server, as well as a set of influencing factors corresponding to each range of level analysis values. The level analysis values ​​are compared with all preset ranges of level analysis values ​​one by one. The preset ranges of level analysis values ​​that cover the level analysis values ​​are marked as selected ranges, and the influencing factors corresponding to the selected ranges are marked as the level influence values ​​of the production and processing equipment.

7. A process design and operation system for intelligent production of engineering machinery according to claim 5, characterized in that, The specific operation process of the process operation risk decision-making module includes: The frequency of safety accidents and the frequency of non-standard operator behavior during the daily production and processing of target process i are obtained. The frequency of safety accidents and the frequency of non-standard operator behavior are compared with the preset safety accident frequency threshold and the preset non-standard behavior frequency threshold for target process i. If the frequency of safety accidents exceeds the preset safety accident frequency threshold or the frequency of non-standard operator behavior exceeds the preset non-standard behavior frequency threshold, a strong supervision signal for target process i is generated. If the frequency of safety accidents does not exceed the preset safety accident frequency threshold and the frequency of non-standard operator behavior does not exceed the preset non-standard behavior frequency threshold, the operation risk of the process is judged to be qualified.

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