Process design operation system suitable for intelligent production of engineering machinery

Abstract

The invention belongs to the technical field of engineering machinery production management and control, and particularly relates to a process design operation system suitable for intelligent production of engineering machinery. Comprising a server, a raw material classification consumption supervision module, a production process collection and disassembly module, a process processing rationality evaluation module, a process equipment strategy analysis module and a process operation risk decision module. The types of the raw materials required in the production process of the corresponding engineering machinery are analyzed one by one, so that consumption abnormity reason investigation and raw material supplementation of the corresponding raw materials can be carried out in time, the stable proceeding of the production processing flow of the corresponding engineering machinery is ensured, the processing management efficiency is improved, and the workload of managers is reduced; and processing rationality analysis, process equipment strategy analysis and process operation risk analysis are carried out on the target process, so that comprehensive analysis of each process in the corresponding engineering machinery production processing flow is realized, and management personnel can carry out corresponding supervision measure improvement on each process subsequently.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery production control, and in particular to a process design operation system suitable for intelligent production of engineering machinery. Background Art

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

[0003] At present, it is difficult to reasonably analyze the raw material consumption status of the processing day in the production and processing of engineering machinery, and it is impossible to comprehensively evaluate the effect of raw material consumption supervision and timely issue early warnings for the replenishment of various raw materials. It is also difficult to comprehensively analyze each process in the production and processing flow, resulting in the inability of management personnel to take corresponding supervision measures to improve each process in the future;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a process design operation system suitable for the intelligent production of construction machinery, which solves the problem that the prior art is unable to comprehensively evaluate the effect of raw material consumption supervision and timely issue replenishment warnings for various types of raw materials, and it is difficult to comprehensively analyze each process in the production and processing flow, resulting in the inability of management personnel to improve supervision measures for each process in the future.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A process design operation system suitable for intelligent production of engineering machinery, comprising a server, a raw material classification consumption and storage supervision module, a production process collection and disassembly module, a process processing rationality evaluation module, a process equipment strategy analysis module and a process operation risk decision module; the raw material classification consumption and storage supervision module analyzes the types of raw materials required in the corresponding engineering machinery production process one by one, determines the abnormal consumption raw materials through analysis and generates consumption warning signals of the corresponding abnormal consumption raw materials, and determines whether to generate a strong raw material supervision signal through analysis, and determines whether to generate a first-level raw material replenishment warning signal or a second-level raw material replenishment warning signal of the corresponding raw material through analysis, and sends the strong raw material supervision signal, the consumption warning signal of the corresponding abnormal consumption raw material, and the first-level raw material replenishment warning signal or the second-level raw material replenishment warning signal to the background supervision terminal via the server;

[0008] The production process collection and disassembly module collects the entire production process of the corresponding engineering machinery, obtains all its production processes based on the entire production process of the corresponding engineering machinery, and marks the corresponding production process as the target process i, i = {1, 2, ..., n}, n represents the number of production processes and n is a natural number greater than 1; the process processing rationality evaluation module performs processing rationality analysis on the target process i, and generates a strong supervision signal or equipment strategy analysis signal for the target process i, and sends the process strong supervision signal to the background supervision terminal via the server, and sends the equipment strategy analysis signal 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 corresponding target process i, thereby generating a strong supervision signal or process operation risk analysis signal for the target process i, and send the strong supervision signal to the background supervision terminal via the server, and send the process operation risk analysis signal 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 corresponding target process i, thereby determining whether to generate a strong supervision signal for the target process i, and after generating the strong supervision signal, send it to the background supervision terminal via the server.

[0010] Furthermore, the specific operation process of the raw material classification consumption and storage supervision 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, where u = {1, 2, ..., m}, 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 numerically compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is not within the corresponding 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 of the corresponding target raw material u is generated;

[0012] Obtain all abnormal raw materials, mark the deviation value of the daily consumption of the corresponding abnormal raw materials compared with the corresponding preset daily consumption range as the daily raw material consumption deviation value, compare the daily raw material consumption deviation value with the preset daily raw material consumption deviation threshold of the corresponding target raw material u, if the daily raw material consumption deviation value exceeds the corresponding preset daily raw material consumption deviation threshold, then mark the corresponding abnormal raw material as an over-deviant raw material; numerically calculate the number of abnormal raw materials and the number of over-deviant raw materials to obtain the raw material consumption assessment coefficient, compare the raw material consumption assessment coefficient with the preset raw material consumption assessment coefficient threshold, if the raw material consumption assessment coefficient exceeds the preset raw material consumption assessment coefficient threshold, then generate a strong raw material supervision signal;

[0013] A supervision period is set, and the daily consumption of the target raw material u on each processing day during the supervision period is collected, a raw material consumption set is established for all daily consumptions of the target raw material u during the supervision period, the raw material consumption set is summed and the average is taken to obtain the daily average consumption of the target raw material u; the remaining storage volume of the current target raw material u is obtained, and the ratio of the remaining storage volume to the corresponding daily average consumption is calculated to obtain the replenishment emergency coefficient, and the replenishment emergency coefficient is numerically compared with the preset replenishment emergency coefficient range; if the replenishment emergency coefficient does not exceed the minimum value of the preset replenishment emergency coefficient range, a first-level raw material replenishment warning signal for the corresponding target raw material u is generated; if the replenishment emergency coefficient is within the preset replenishment emergency coefficient range, a second-level raw material replenishment warning signal for the corresponding target raw material u is generated; if the replenishment emergency coefficient exceeds the maximum value of the preset replenishment emergency coefficient range, it is judged that the storage volume of the corresponding target raw material u is sufficient and no raw material replenishment warning signal is generated.

[0014] Furthermore, if a consumption warning signal corresponding to the target raw material u is not generated, when establishing the raw material consumption set of the target raw material u, the variance calculation is performed on the raw material consumption set to obtain the raw material daily consumption dispersion value, and the average daily consumption of the target raw material u and the daily raw material consumption dispersion value are numerically compared with the corresponding preset daily average consumption range and the preset raw material daily consumption dispersion threshold value, respectively; if the average daily consumption is within the preset daily average consumption range and the daily raw material consumption dispersion value does not exceed the preset raw material daily consumption dispersion threshold value, then it is judged that the consumption supervision of the target raw material u during the supervision period is normal; in other cases, it is judged that the consumption supervision of the target raw material u during the supervision period is abnormal and a consumption fluctuation signal of the corresponding target raw material u is generated, and the consumption fluctuation signal of the corresponding target raw material u is sent to the background supervision terminal via the server.

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

[0016] Collect the solid waste value, production waste gas value and production waste liquid value of the target process i when the corresponding engineering machinery is being produced and processed on the same day, and compare the solid waste value, production waste gas value and production waste liquid value with the corresponding preset range of the target process i. If at least one of the solid waste value, production waste gas value and production waste liquid value is not within the corresponding preset range, generate a strong process supervision signal for the target process i. If the solid waste value, production waste gas value and production waste liquid value are all within the corresponding preset range, then sum and calculate the maximum and minimum values ​​of the corresponding preset solid waste value range and take the average to obtain the solid waste judgment value, and similarly obtain the waste gas judgment value and waste liquid judgment value;

[0017] The difference between the solid waste quantity value and the solid waste judgment value is calculated and the average is taken to obtain the solid waste difference. Similarly, the waste gas difference and the waste liquid difference are obtained. The solid waste difference, the waste gas difference and the waste liquid difference 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] Obtain the production and processing equipment of the target process i, collect the stable working time and number of failures of the corresponding production and processing equipment on the day, sum up the duration of each failure to obtain the failure operation time, calculate the ratio of the failure operation time to the stable working time to obtain the failure duration ratio; calculate the time difference between the moments of two adjacent failures to obtain the single failure interval duration, sum up all the single failure interval durations and take the average to obtain the average failure interval duration;

[0020] The failure time ratio, number of failures and average duration of failure intervals of target process i are normalized and calculated to obtain the initial evaluation value of the process, and the grade influence value of the corresponding production and processing equipment is obtained through precise analysis of the equipment grade. The grade influence value is multiplied by the initial evaluation value of the process and the product of the two is marked as the process equipment strategy value; the process equipment strategy value is numerically 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 operation risk analysis signal is generated and sent to the process operation risk decision module via the server.

[0021] Furthermore, the specific analysis process of device level precision analysis is as follows:

[0022] Obtain the operation duration of each time of the corresponding production and processing equipment of the target process i in the historical working process, as well as the working time in its unsuitable environmental state during each operation process. If the operation duration exceeds the preset operation duration threshold or the working time in its unsuitable environmental state exceeds the preset working time threshold in the unsuitable environmental state, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1; and obtain the production interval time and the commissioning interval time of the target process i, sum and calculate the production interval time and the commissioning interval time and take the average to obtain the equipment comprehensive evaluation time; sum and calculate the operation duration of each time of the corresponding production and processing equipment of the target process i to obtain the total operation time of the equipment, and mark 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 grade analysis value; several groups of preset grade analysis value ranges corresponding to the production and processing equipment are retrieved from the server, and a group of influencing factors corresponding to each group of grade analysis value ranges are set in advance, the grade analysis value is compared with all preset grade analysis value ranges one by one, the preset grade analysis value range covering the grade analysis value is marked as the selected range, and the influencing factor corresponding to the selected range is marked as the grade influence value of the production and processing equipment.

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

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

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. In the present invention, the types of raw materials required in the production process of the corresponding engineering machinery are analyzed one by one through the raw material classification consumption and storage supervision module, and the abnormal consumption raw materials are determined through analysis and the consumption warning signal of the corresponding abnormal consumption raw materials is generated, and the analysis is used to determine whether to generate a strong raw material supervision signal, and the analysis is used to determine whether to generate a raw material replenishment level 1 warning signal or a raw material replenishment level 2 warning signal, so as to timely investigate the causes of abnormal consumption of the corresponding raw materials and facilitate timely replenishment of the corresponding raw materials, and strengthen the subsequent raw material consumption supervision as needed, so as to ensure the stable progress of the production and processing process of the corresponding engineering machinery, improve the processing management efficiency and reduce the workload of the management personnel;

[0028] 2. In the present invention, a processing rationality analysis is performed on the target process i, thereby generating a strong supervision signal or an equipment strategy analysis signal for the target process i; when generating the equipment strategy analysis signal, a process equipment strategy analysis is performed on the target process i, thereby generating a strong supervision 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 supervision signal for the target process i; after the strong supervision signal is generated, it is sent to the background supervision terminal via the server, thereby achieving a comprehensive analysis of each process in the corresponding engineering machinery production and processing flow, which is beneficial for the management personnel to subsequently improve the corresponding supervision measures for each process. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

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

[0031] Figure 2 It is a communication block diagram between the server and the background monitoring terminal in the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiment 1: Figure 1-2 As shown, the process design operation system suitable for intelligent production of engineering machinery proposed by the present invention includes a server, a raw material classification and consumption supervision module, a production process collection and disassembly module, a process processing rationality evaluation module, a process equipment strategy analysis module and a process operation risk decision module, and the server is communicatively connected with the raw material classification and consumption supervision module, the production process collection and disassembly module, the process processing rationality evaluation module, the process equipment strategy analysis module and the process operation risk decision module;

[0034] The raw material classification consumption and storage supervision module analyzes the types of raw materials required in the production process of the corresponding engineering machinery one by one, determines the abnormal consumption raw materials through analysis and generates the corresponding abnormal consumption raw material consumption warning signal, and determines whether to generate the raw material strong supervision signal through analysis, and whether to generate the raw material replenishment level 1 warning signal or the raw material replenishment level 2 warning signal through analysis, and sends the raw material strong supervision signal, the corresponding abnormal consumption raw material consumption warning signal, and the corresponding raw material replenishment level 1 warning signal or the raw material replenishment level 2 warning signal to the background supervision terminal through the server, so as to timely investigate the causes of abnormal consumption of the corresponding raw materials, and facilitate the timely replenishment of the corresponding raw materials to ensure the stable progress of the corresponding engineering machinery production and processing process; the specific operation process of the raw material classification consumption and storage supervision 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, where u = {1, 2, ..., m}, 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 numerically compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is within the corresponding preset daily consumption range, it is judged 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 corresponding preset daily consumption range, it is judged 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 of the corresponding target raw material u is generated;

[0036] Obtain all abnormal consumption raw materials, mark the deviation value of the daily consumption of the corresponding abnormal consumption raw materials compared with the corresponding preset daily consumption range as the daily consumption deviation value of the raw materials, compare the daily consumption deviation value of the raw materials with the preset daily consumption deviation threshold value of the corresponding target raw material u, if the daily consumption deviation value of the raw materials exceeds the corresponding preset daily consumption deviation threshold value of the raw materials, then mark the corresponding abnormal consumption raw materials as over-deviant raw materials; numerically calculate the number of abnormal consumption raw materials YSu and the number of over-deviant raw materials CPu through the formula XPu=a1*YSu+a2*CPu to obtain the raw material consumption evaluation coefficient XPu, wherein 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 positively proportional to the number of abnormal raw materials YSu and the number of over-deviant raw materials CPu. The larger the value of the raw material consumption assessment coefficient XPu, the worse the raw material consumption supervision in the production and processing process is, and the more it is necessary to strengthen the raw material consumption supervision in the subsequent period. The raw material consumption assessment coefficient XPu is numerically 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 as to strengthen the supervision of the raw material consumption process in a timely manner in the subsequent period, and realize the effective management of the corresponding construction machinery production process. If the raw material consumption assessment coefficient XPu does not exceed the preset raw material consumption assessment coefficient threshold, it is judged that the raw material consumption supervision of the corresponding processing day is qualified.

[0038] And set a supervision period, preferably, the supervision period is seven days; collect the daily consumption of the target raw material u on each processing day during the supervision period, establish a raw material consumption set for all daily consumptions of the target raw material u during the supervision period, sum and calculate the raw material consumption set and take the average to obtain the daily average consumption of the target raw material u; obtain the remaining storage of the current target raw material u, calculate the ratio of the remaining storage to the corresponding daily average consumption to obtain the replenishment emergency coefficient. It should be noted that the smaller the value of the replenishment emergency coefficient, the more timely the storage and replenishment of the corresponding target raw material u is needed; compare the replenishment emergency coefficient with the preset replenishment emergency coefficient range;

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

[0040] The production process collection and disassembly module collects the entire production process of the corresponding engineering machinery, obtains all its production processes based on the entire production process of the corresponding engineering machinery, and marks the corresponding production process as the target process i, i = {1, 2, ..., n}, n represents the number of production processes and n is a natural number greater than 1; the process processing rationality evaluation module performs processing rationality analysis on the target process i, and generates a strong supervision signal or equipment strategy analysis signal for the target process i, and sends the process strong supervision signal to the background supervision terminal via the server, and sends the process equipment strategy analysis signal to the process equipment strategy analysis module via the server; the specific operation process of the process processing rationality evaluation module is as follows:

[0041] Collect the solid waste value, production waste gas value and production waste liquid value of the target process i when the corresponding engineering machinery is being produced and processed on the same day, and compare the solid waste value, production waste gas value and production waste liquid value with the corresponding preset range of the target process i. If at least one of the solid waste value, production waste gas value and production waste liquid value is not within the corresponding preset range, generate a strong process supervision signal for the target process i. If the solid waste value, production waste gas value and production waste liquid value are all within the corresponding preset range, then sum and calculate the maximum and minimum values ​​of the corresponding preset solid waste value range and take the average to obtain the solid waste judgment value, and similarly obtain the waste gas judgment value and waste liquid judgment value; calculate the difference between the solid waste value and the solid waste judgment value and take the average to obtain the solid waste difference, and similarly obtain the waste gas difference and waste liquid difference;

[0042] The solid waste difference GF i, the waste gas difference FQi and the waste liquid difference FY i are numerically calculated by the formula HXi=b1*GF i+b2*FQ i+b3*FYi to obtain the rationality coefficient HXi, wherein b1, b2 and b3 are preset weight coefficients, and the values ​​of b1, b2 and b3 are all greater than zero; it should be noted that the numerical value of the rationality coefficient HXi is directly proportional to the solid waste difference GF i, the waste gas difference FQi and the waste liquid difference FYi. The larger the numerical value of the rationality coefficient HXi is, the more abnormal the processing of the target process i is; the rationality coefficient HXi is numerically compared with the preset rationality coefficient threshold of the target process i. If the rationality coefficient HXi exceeds the preset rationality coefficient threshold, it indicates that the supervision of the processing of the target process i needs to be strengthened in the future, and a strong process supervision signal is generated. If the rationality coefficient HXi does not exceed the preset rationality coefficient threshold, an equipment strategy analysis signal corresponding to the target process i is generated and the equipment strategy analysis signal is 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 will perform process equipment strategy analysis on the target process i, and generate a strong supervision signal or process operation risk analysis signal for the target process i, and send the strong supervision signal to the background supervision terminal via the server, and send the process operation risk analysis signal 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] Obtain the production and processing equipment of the target process i, collect the stable working time and number of failures of the corresponding production and processing equipment on the day, sum up the duration of each failure to obtain the failure operation time, calculate the ratio of the failure operation time to the stable working time to obtain the failure duration ratio; calculate the time difference between the moments of two adjacent failures to obtain the single failure interval duration, sum up all the single failure interval durations and take the average to obtain the average failure interval duration;

[0045] And through accurate analysis of the equipment level, the level impact value of the corresponding production and processing equipment is obtained, specifically: the operation duration of each time of the corresponding production and processing equipment of the target process i in the historical working process, and the working time in its unsuitable environmental state during each operation process are obtained. If the operation duration exceeds the preset operation duration threshold or the working time in its unsuitable environmental state exceeds the preset working time threshold in the unsuitable environmental state, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1; and the production interval time and the commissioning interval time of the target process i are obtained, the production interval time and the commissioning interval time are summed and calculated, and the average is taken to obtain the equipment comprehensive evaluation time; the operation duration of each time of the corresponding production and processing equipment of the target process i is summed and calculated to obtain the total operation time of the equipment, and the number of operations corresponding to the operation symbol YF-1 is marked as a high-loss operation frequency;

[0046] By formula The equipment comprehensive evaluation time ZPi, the equipment total operation time ZYi and the high-loss operation frequency GPi of the corresponding production and processing equipment are normalized and calculated to obtain the grade analysis value FJi; wherein, ep1, ep2 and ep3 are preset proportional coefficients, ep3>ep2>ep1>0; and, the larger the value of the grade analysis value FJi is, the worse the equipment condition of the corresponding production and processing equipment is; a plurality of preset grade analysis value ranges corresponding to the production and processing equipment are retrieved from the server, and a group of influencing factors corresponding to each group of grade analysis value ranges are set in advance. It should be noted that the larger the value of the preset grade analysis value range is, the larger the value of the corresponding influencing factor is; the grade analysis value FJi is compared with all preset grade analysis value ranges one by one, the preset grade analysis value range covering the grade analysis value is marked as the selected range, and the influencing factor corresponding to the selected range is marked as the grade influencing value of the production and processing equipment;

[0047] By formula The fault duration ratio GZ i, the number of faults GS i and the average duration of fault intervals GJ i of the target process i are normalized and calculated to obtain the process initial evaluation value CT i, wherein 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 process initial evaluation value CT i is directly proportional to the fault duration ratio GZ i and the number of faults GS i, and inversely proportional to the average duration of fault intervals GJ i, and the larger the value of the process initial evaluation value CT i, the worse the processing condition of the production and processing equipment of the target process i, and the more supervision needs to be strengthened in the subsequent production process;

[0048] Multiply the level impact value of the corresponding production and processing equipment by the initial evaluation value of the process and mark the product of the two as the process equipment strategy value CYi; compare the process equipment strategy value CYi 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 it is necessary to strengthen the supervision of the target process i in the future, and a strong process supervision signal is generated; if the process equipment strategy value CYi does not exceed the preset process equipment strategy threshold, an operation risk analysis signal is generated and sent to the process operation risk decision module via 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 target process i, and judge whether to generate a strong supervision signal for the target process i. After the strong supervision signal is generated, it will be sent to the background supervision terminal via the server. 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 behaviors of operators in the production and processing of the target process i on the day are obtained, wherein the frequency of safety accidents is a data value indicating the number of safety accidents, and the frequency of non-standard behaviors of operators is a data value indicating the number of illegal operations of operators; the frequency of safety accidents and the frequency of non-standard behaviors of operators are numerically compared with the preset safety accident frequency threshold and the preset non-standard behavior frequency threshold of the corresponding target process i; if the frequency of safety accidents exceeds the preset safety accident frequency threshold or the frequency of non-standard behaviors of operators exceeds the preset non-standard behaviors frequency threshold, indicating that the supervision of the target process i needs to be strengthened in the future, a strong process supervision signal for the 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 behaviors of operators does not exceed the preset non-standard behaviors frequency threshold, indicating that the supervision of the target process i does not need to be strengthened in the future, the process operation risk is judged to be qualified.

[0051] Embodiment 2: The difference between this embodiment and embodiment 1 is that, if the consumption warning signal corresponding to the target raw material u is not generated, then when the raw material consumption set of the target raw material u is established, the variance calculation is performed on the raw material consumption set to obtain the raw material daily consumption dispersion value, and the daily average consumption of the target raw material u and the daily raw material consumption dispersion value are numerically compared with the corresponding preset daily average consumption range and the preset raw material daily consumption dispersion threshold value, if the daily average consumption is within the preset daily average consumption range and the daily raw material consumption dispersion value does not exceed the preset raw material daily consumption dispersion threshold value, indicating that the daily consumption deviation is small and the daily consumption tends to be normal, then it is judged that the consumption supervision of the target raw material u during the supervision period is normal, and in other cases, it is judged that the consumption supervision of the target raw material u during the supervision period is abnormal and a consumption fluctuation signal of the corresponding target raw material u is generated, and the consumption fluctuation signal of the corresponding target raw material u is sent to the background supervision terminal via the server, and the corresponding management personnel shall promptly conduct relevant cause investigation when receiving the consumption fluctuation signal, so as to make corresponding targeted management measures later.

[0052] The working principle of the present invention is as follows: when in use, the types of raw materials required in the production process of the corresponding engineering machinery are analyzed one by one through the raw material classification consumption and storage supervision module, and the abnormal consumption raw materials are determined through analysis and a consumption warning signal of the corresponding abnormal consumption raw materials is generated, and through analysis, it is judged whether a strong raw material supervision signal is generated, and through analysis, it is judged whether a first-level raw material replenishment warning signal or a second-level raw material replenishment warning signal is generated for the corresponding raw material, so as to timely investigate the causes of abnormal consumption of the corresponding raw materials and facilitate timely replenishment of the corresponding raw materials, and strengthen subsequent raw material consumption supervision as needed to ensure the stable progress of the production and processing process of the corresponding engineering machinery; the target process i is subjected to processing rationality analysis through the process processing rationality evaluation module, and according to the process processing rationality evaluation module, the target process i is subjected to processing rationality analysis, and the target process i is subjected to processing rationality evaluation module. In this way, a strong supervision signal or equipment strategy analysis signal for the target process i is generated. When the equipment strategy analysis signal is generated, the process equipment strategy analysis is performed on the target process i through the process equipment strategy analysis module. Based on this, a strong supervision signal or process operation risk analysis signal for the target process i is generated. When the process operation risk analysis signal is generated, the process operation risk analysis is performed on the corresponding target process i through the process operation risk decision module. Based on this, it is determined whether to generate a strong supervision signal for the target process i. After the strong supervision signal is generated, it is sent to the background supervision terminal via the server, thereby realizing a comprehensive analysis of each process in the corresponding engineering machinery production and processing flow, which is conducive to the management personnel to subsequently improve the corresponding supervision measures for each process.

[0053] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A process design operation system suitable for intelligent production of engineering machinery, characterized in that: It includes a server, a raw material classification and consumption supervision module, a production process collection and disassembly module, a process processing rationality assessment module, a process equipment strategy analysis module and a process operation risk decision module; the raw material classification and consumption supervision module analyzes the types of raw materials required in the corresponding engineering machinery production process one by one, determines the abnormal consumption raw materials through analysis and generates consumption warning signals of the corresponding abnormal consumption raw materials, and determines whether to generate a strong raw material supervision signal through analysis, and determines whether to generate a first-level raw material replenishment warning signal or a second-level raw material replenishment warning signal through analysis for the corresponding raw materials, and sends the strong raw material supervision signal, the consumption warning signal of the corresponding abnormal consumption raw materials, and the first-level raw material replenishment warning signal or the second-level raw material replenishment warning signal to the background supervision terminal via the server; The production process collection and disassembly module collects the entire production process of the corresponding engineering machinery, obtains all its production processes based on the entire production process of the corresponding engineering machinery, and marks the corresponding production process as the target process i, i = {1, 2, ..., n}, n represents the number of production processes and n is a natural number greater than 1; the process processing rationality evaluation module performs processing rationality analysis on the target process i, and generates a strong supervision signal or equipment strategy analysis signal for the target process i, and sends the process strong supervision signal to the background supervision terminal via the server, and sends the equipment strategy analysis signal 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 corresponding target process i, thereby generating a strong supervision signal or process operation risk analysis signal for the target process i, and send the strong supervision signal to the background supervision terminal via the server, and send the process operation risk analysis signal 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 corresponding target process i, thereby determining whether to generate a strong supervision signal for the target process i, and after generating the strong supervision signal, send it to the background supervision terminal via the server.

2. A process design operation system suitable for intelligent production of engineering machinery according to claim 1, characterized in that: The specific operation process of the raw material classification consumption and storage supervision 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, where u = {1, 2, ..., m}, 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 numerically compared with the preset daily consumption range of the corresponding target raw material u. If the daily consumption is not within the corresponding 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 of the corresponding target raw material u is generated; Obtain all abnormal raw materials, mark the deviation value of the daily consumption of the corresponding abnormal raw materials compared with the corresponding preset daily consumption range as the daily raw material consumption deviation value, compare the daily raw material consumption deviation value with the preset daily raw material consumption deviation threshold of the corresponding target raw material u, if the daily raw material consumption deviation value exceeds the corresponding preset daily raw material consumption deviation threshold, then mark the corresponding abnormal raw material as an over-deviant raw material; numerically calculate the number of abnormal raw materials and the number of over-deviant raw materials to obtain the raw material consumption assessment coefficient, compare the raw material consumption assessment coefficient with the preset raw material consumption assessment coefficient threshold, if the raw material consumption assessment coefficient exceeds the preset raw material consumption assessment coefficient threshold, then generate a strong raw material supervision signal; A supervision period is set, and the daily consumption of the target raw material u on each processing day during the supervision period is collected, a raw material consumption set is established for all daily consumptions of the target raw material u during the supervision period, the raw material consumption set is summed and the average is taken to obtain the daily average consumption of the target raw material u; the remaining storage volume of the current target raw material u is obtained, and the ratio of the remaining storage volume to the corresponding daily average consumption is calculated to obtain the replenishment emergency coefficient, and the replenishment emergency coefficient is numerically compared with the preset replenishment emergency coefficient range; if the replenishment emergency coefficient does not exceed the minimum value of the preset replenishment emergency coefficient range, a first-level raw material replenishment warning signal for the corresponding target raw material u is generated; if the replenishment emergency coefficient is within the preset replenishment emergency coefficient range, a second-level raw material replenishment warning signal for the corresponding target raw material u is generated; if the replenishment emergency coefficient exceeds the maximum value of the preset replenishment emergency coefficient range, it is judged that the storage volume of the corresponding target raw material u is sufficient and no raw material replenishment warning signal is generated.

3. A process design operation system suitable for intelligent production of engineering machinery according to claim 2, characterized in that: If the consumption warning signal of the corresponding target raw material u is not generated, then when establishing the raw material consumption set of the target raw material u, the variance calculation is performed on the raw material consumption set to obtain the raw material daily consumption dispersion value, and the average daily consumption of the target raw material u and the daily raw material consumption dispersion value are numerically compared with the corresponding preset daily average consumption range and the preset raw material daily consumption dispersion threshold value respectively; if the average daily consumption is within the preset daily average consumption range and the daily raw material consumption dispersion value does not exceed the preset raw material daily consumption dispersion threshold value, then it is judged that the consumption supervision of the target raw material u during the supervision period is normal; in other cases, it is judged that the consumption supervision of the target raw material u during the supervision period is abnormal and a consumption fluctuation signal of the corresponding target raw material u is generated, and the consumption fluctuation signal of the corresponding target raw material u is sent to the background supervision terminal via the server.

4. The process design operation system suitable for intelligent production of engineering machinery according to claim 1 is characterized in that: The specific operation process of the process rationality evaluation module includes: Collect the solid waste value, production waste gas value and production waste liquid value of the target process i when the corresponding engineering machinery is being produced and processed on the same day, and compare the solid waste value, production waste gas value and production waste liquid value with the corresponding preset range of the target process i. If at least one of the solid waste value, production waste gas value and production waste liquid value is not within the corresponding preset range, generate a strong process supervision signal for the target process i. If the solid waste value, production waste gas value and production waste liquid value are all within the corresponding preset range, then sum and calculate the maximum and minimum values ​​of the corresponding preset solid waste value range and take the average to obtain the solid waste judgment value, and similarly obtain the waste gas judgment value and waste liquid judgment value; The difference between the solid waste quantity value and the solid waste judgment value is calculated and the average is taken to obtain the solid waste difference. Similarly, the waste gas difference and the waste liquid difference are obtained. The solid waste difference, the waste gas difference and the waste liquid difference 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. The process design operation system suitable for intelligent production of engineering machinery according to claim 4 is characterized in that: The specific operation process of the process equipment strategy analysis module includes: Obtain the production and processing equipment of the target process i, collect the stable working time and number of failures of the corresponding production and processing equipment on the day, sum up the duration of each failure to obtain the failure operation time, calculate the ratio of the failure operation time to the stable working time to obtain the failure duration ratio; calculate the time difference between the moments of two adjacent failures to obtain the single failure interval duration, sum up all the single failure interval durations and take the average to obtain the average failure interval duration; The failure time ratio, number of failures and average duration of failure intervals of target process i are normalized and calculated to obtain the initial evaluation value of the process, and the grade influence value of the corresponding production and processing equipment is obtained through precise analysis of the equipment grade. The grade influence value is multiplied by the initial evaluation value of the process and the product of the two is marked as the process equipment strategy value; the process equipment strategy value is numerically 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 operation risk analysis signal is generated and sent to the process operation risk decision module via the server.

6. A process design operation system suitable for intelligent production of engineering machinery according to claim 5, characterized in that: The specific analysis process of equipment level precision analysis is as follows: Obtain the operation duration of each time of the corresponding production and processing equipment of the target process i in the historical working process, as well as the working time in its unsuitable environmental state during each operation process. If the operation duration exceeds the preset operation duration threshold or the working time in its unsuitable environmental state exceeds the preset working time threshold in the unsuitable environmental state, the corresponding operation process of the production and processing equipment is assigned the operation symbol YF-1; and obtain the production interval time and the commissioning interval time of the target process i, sum and calculate the production interval time and the commissioning interval time and take the average to obtain the equipment comprehensive evaluation time; sum and calculate the operation duration of each time of the corresponding production and processing equipment of the target process i to obtain the total operation time of the equipment, and mark the number of operations corresponding to the operation symbol YF-1 as the high-loss operation frequency; Normalize and calculate the equipment comprehensive evaluation time, equipment total operation time and high-loss operation frequency of the corresponding production and processing equipment to obtain the grade analysis value; A plurality of preset level analysis value ranges corresponding to the production and processing equipment are retrieved from the server, and a group of influencing factors are set in advance corresponding to each level analysis value range. The level analysis value is compared with all preset level analysis value ranges one by one, and the preset level analysis value range covering the level analysis value is marked as a selected range, and the influencing factor corresponding to the selected range is marked as the level influencing value of the production and processing equipment.

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

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