CN116882763A - Industrial production risk assessment system based on big data - Google Patents
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Abstract
The invention discloses an industrial production risk assessment system based on big data, which belongs to the field of industry and is used for solving the problem that the risk assessment of the production process in a current factory workshop is limited.
Description
Technical Field
The invention belongs to the field of industry, relates to a production risk assessment technology, and particularly relates to an industrial production risk assessment system based on big data.
Background
Industry is a social material production sector that explores natural resources and processes various raw materials. The industry is a product of the development of the social division industry, is a main component part of the second industry after the development stages of the hand industry, the machine industry and the like, and is divided into a light industry and a heavy industry. In industrial processes, production risk needs to be assessed.
The risk assessment of the production process in the factory workshop is limited, and the production risk is not assessed in the whole aspect by combining multiple dimensions and various layers, so that how to achieve comprehensive and accurate assessment of the production risk of the factory workshop based on multiple factors is a great difficulty to be solved at present;
for this purpose, we propose an industrial production risk assessment system based on big data.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide an industrial production risk assessment system based on big data.
The technical problems to be solved by the invention are as follows:
how to achieve a comprehensive and accurate assessment of plant-to-plant production risk based on multiple factors.
The aim of the invention can be achieved by the following technical scheme:
the industrial production risk assessment system based on big data comprises a region division module, a data acquisition module, an equipment monitoring module, an environment monitoring module, a supply and sales analysis module, a risk assessment module, a database and a server, wherein the region division module is used for dividing a factory workshop into a plurality of monitoring regions; the database is used for storing standard environment information of a factory workshop and sending the standard environment information to the environment monitoring module; the data acquisition module is used for acquiring real-time environment information in a monitoring area in a factory workshop, equipment information of equipment in the monitoring area and supply and sales information of the factory workshop and sending the real-time environment information to the environment monitoring module, the equipment information to the equipment monitoring module and the supply and sales information to the supply and sales analysis module;
the equipment monitoring module is used for monitoring equipment conditions of the monitoring area, obtaining equipment risk coefficients of the monitoring area and feeding the equipment risk coefficients back to the server, and the server sends the equipment risk coefficients to the risk assessment module;
the environment monitoring module is used for monitoring the environment condition of the monitoring area, obtaining an environment risk coefficient of the monitoring area and sending the environment risk coefficient to the server, and then the server sends the environment risk coefficient to the risk assessment module;
the supply and sales analysis module is used for analyzing supply and sales conditions of the factory workshop, obtaining supply and sales risk coefficients of the factory workshop, feeding the supply and sales risk coefficients back to the server, and sending the supply and sales risk coefficients to the risk assessment module by the server;
the risk assessment module is used for assessing the industrial production risk of the factory workshop and generating an area safety signal, an area abnormal signal, a supply and sales abnormal signal or a supply and sales normal signal.
Further, the standard environmental information is a standard temperature value, a standard humidity value and a standard air flow rate of a factory workshop;
the equipment information is the number of faults of equipment in the monitoring area, the maintenance time and the purchase time when each fault occurs;
the real-time environment information is a temperature value, a humidity value, an air flow rate, suspended particle amount and toxic gas content in the monitoring area;
the supply and sales information is the daily production amount and daily sales amount of the week in the factory shop.
Further, the monitoring process of the device monitoring module is specifically as follows:
acquiring the number of faults of equipment in a monitoring area and maintaining time length when each fault occurs;
obtaining purchase time of the equipment in the monitoring area, and subtracting the purchase time from the current time of the server to obtain purchase time of the equipment in the monitoring area;
the number of the devices in the monitoring area is obtained and recorded as the number of the devices, the number of the faults of the devices in the monitoring area is added and divided by the number of the devices to obtain the number of the average faults of the devices in the monitoring area, and the maintenance average time and the purchase average time of the devices in the monitoring area are obtained through calculation in the same way;
and calculating the equipment risk value of the monitoring area, and obtaining the equipment risk coefficient of the monitoring area according to the equipment risk value.
Further, the monitoring process of the environment monitoring module is specifically as follows:
obtaining a standard temperature value, a standard humidity value and a standard air flow rate of a factory workshop through a database;
then acquiring a temperature value, a humidity value, an air flow rate, suspended particle quantity and toxic gas content of a monitoring area;
firstly, calculating a temperature deviation value, a humidity deviation value and an air flow speed deviation value of a monitoring area in a factory workshop;
and then calculating the environmental risk coefficient of the monitoring area in the factory workshop.
Further, the analysis process of the supply and sales analysis module is specifically as follows:
acquiring daily throughput and daily sales of the week in a factory workshop;
calculating the daily deviation amount of each day of the week in the factory workshop, and adding and summing the daily deviation amounts to obtain the week deviation amount;
if the circumferential deviation is greater than zero, indicating that the last round of sales state is a diapause, and calculating to obtain a sales risk coefficient of a factory workshop by using a diapause risk calculation scheme;
if the circumferential deviation is smaller than zero, the last round of sales state is described as out-of-stock, and the sales risk coefficient of the factory workshop is calculated by using an out-of-stock risk calculation scheme;
if the circumferential deviation is equal to zero, the sales risk factor is zero.
Further, the specific calculation scheme of the risk of the diapause is as follows:
the circumferential deviation amount is compared with a preset deviation amount, and the factory workshop is judged to be a third-level stagnation risk, a second-level stagnation risk or a first-level stagnation risk;
and obtaining corresponding sales risk coefficients of the factory workshop according to the three-level sales risk, the two-level sales risk or the first-level sales risk.
Further, the specific out-of-stock risk calculation scheme is as follows:
the circumferential deviation amount is compared with a preset deviation amount, and the factory workshop is judged to be a third-level out-of-stock risk, a second-level out-of-stock risk or a first-level out-of-stock risk;
and obtaining a corresponding sales risk coefficient of the factory workshop according to the third-level sales risk, the second-level sales risk or the first-level sales risk.
Further, the risk assessment module comprises the following specific assessment processes:
acquiring an equipment risk coefficient of a monitoring area, an environment risk coefficient J of the monitoring area and a supply and sales risk coefficient of a factory workshop;
calculating a risk coefficient value of the monitoring area;
generating a regional safety signal when the risk coefficient value is smaller than or equal to the risk coefficient threshold value, and generating a regional abnormality signal when the risk coefficient value is larger than the risk coefficient threshold value;
and then acquiring a preset supply and sales risk coefficient of a factory workshop, if the supply and sales risk coefficient is larger than the preset supply and sales risk coefficient, generating a supply and sales abnormal signal, and if the supply and sales risk coefficient is smaller than or equal to the preset supply and sales risk coefficient, generating a supply and sales normal signal.
Further, the system further comprises an alarm module, the risk assessment module feeds back the regional safety signal, the regional abnormality signal, the supply and sales abnormality signal or the supply and sales normal signal to the server, if the server receives the regional safety signal or the supply and sales normal signal, no operation is performed, if the server receives the regional abnormality signal or the supply and sales abnormality signal, an alarm instruction is generated and loaded to the alarm module, and the alarm module receives the alarm instruction and then performs alarm work.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a factory workshop is divided into a plurality of monitoring areas by utilizing an area dividing module, equipment conditions of the monitoring areas are monitored by utilizing an equipment monitoring module, equipment risk coefficients of the monitoring areas are obtained and are sent to a risk assessment module, environmental conditions of the monitoring areas are monitored by utilizing an environmental monitoring module, the environmental risk coefficients of the monitoring areas are obtained and are sent to the risk assessment module, the sales conditions of the factory workshop are finally analyzed by utilizing a sales analysis module, the sales risk coefficients of the factory workshop are obtained and are sent to the risk assessment module, and the risk assessment module assesses industrial production risks of the factory workshop to generate an area safety signal, an area abnormality signal, a sales abnormality signal or a sales normal signal.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is an overall system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In an embodiment, referring to fig. 1, an industrial production risk assessment system based on big data is disclosed, and in this embodiment, the system is used for performing risk assessment on industrial production behaviors of a factory workshop, and includes a region dividing module, a data acquisition module, a device monitoring module, an environment monitoring module, a supply and sales analysis module, a risk assessment module, a database, an alarm module and a server;
in a specific implementation, the area dividing module is configured to divide an area of a factory workshop into a plurality of monitoring areas n, n=1, 2, … …, z, z is a positive integer, and the dividing may be performed according to an area or according to a set boundary line, which is not limited herein;
the server is connected with a database, and the database is used for storing the standard environment information of the factory workshop and sending the standard environment information to the environment monitoring module;
the standard environment information is a standard temperature value, a standard humidity value and a standard air flow rate of a factory workshop;
the data acquisition module is used for acquiring real-time environment information in a monitoring area in a factory workshop, equipment information of equipment in the monitoring area and supply and sales information of the factory workshop, and transmitting the real-time environment information, the equipment information and the supply and sales information to the server, wherein the server transmits the real-time environment information to the environment monitoring module, the server transmits the equipment information to the equipment monitoring module, and the server transmits the supply and sales information to the supply and sales analysis module;
the specific explanation is that the equipment information is the number of faults of equipment in the monitoring area, the maintenance time, the purchase time and the like when each fault occurs; the real-time environment information is a temperature value, a humidity value, an air flow rate, suspended particle amount, toxic gas content and the like in the monitoring area; the supply and sales information is the daily production amount, daily sales amount and the like of the week of the factory workshop;
in the implementation, the real-time environment information can be acquired through various sensor assemblies and related equipment, such as a temperature and humidity sensor, a gas flow rate meter, a gas detector and the like, for example, a factory workshop is used for producing reinforcing steel bars, and the equipment in the monitoring area can be a cutting machine, a stranding machine and the like;
the equipment monitoring module is used for monitoring equipment conditions of a monitoring area, and the monitoring process is specifically as follows:
acquiring the fault times CSnu of equipment in a monitoring area and the maintenance duration SCnu of each fault;
obtaining purchase time of the equipment in the monitoring area, and subtracting the purchase time from the current time of the server to obtain purchase time SJnu of the equipment in the monitoring area, wherein u=1, 2, … …, x and x are positive integers, and u represents the number of the equipment in the monitoring area;
the method comprises the steps of obtaining the number of devices in a monitoring area, recording the number as the number of the devices, and obtaining the average failure times CSn of the devices in the monitoring area by adding and dividing the failure times of the devices in the monitoring area by the number of the devices;
similarly, the maintenance average time length SCn and the purchase average time length SJn of the equipment in the monitoring area are calculated;
the device risk value SXn for the monitored area is calculated by the formula SXn =csn×a1+scn×a2+ SJn ×a3; wherein a1, a2 and a3 are weight coefficients with fixed values, and the values of a1, a2 and a3 are all larger than zero;
obtaining a device risk coefficient BXn of the monitoring area according to the device risk value;
the calculation process of the equipment risk coefficient specifically comprises the following steps:
when SXn epsilon (0, X1), the equipment risk coefficient BXn is q1;
when SXn e (X1, X2), then the device risk factor BXn is q2;
when SXn epsilon (X2 and infinity), the equipment risk coefficient BXn is q3; wherein X1 and X2 are equipment risk thresholds with fixed values, X1 is less than X2, q1, q2 and q3 are fixed values, and q1 is less than q2 and less than q3;
the equipment monitoring module feeds equipment risk coefficient BXn of the monitored area back to a server, and the server sends equipment risk coefficient BXn to a risk assessment module;
the environment monitoring module is used for monitoring the environment condition of a monitoring area, and the monitoring process is specifically as follows:
obtaining a standard temperature value T0, a standard humidity value RH0 and a standard air flow velocity V0 of a factory workshop through a database;
then acquiring a temperature value Tn, a humidity value RHn, an air flow velocity Vn, suspended particle amount Sn and toxic gas content Nn of a monitoring area;
specifically, the temperature value, the humidity value, the air flow rate, the suspended particle amount and the toxic gas content in the monitoring area are obtained by adding and summing data in a plurality of environmental samples;
calculating to obtain a temperature deviation value delta Tn, a humidity deviation value delta RHn and an air flow speed deviation value delta Vn of a monitoring area in a factory workshop according to the formula delta Tn=Tn-T0I, delta RHn=RHn-RH 0I and delta Vn=Vn-V0I;
by the formulaCalculating to obtain an environmental risk coefficient JFn of a monitoring area in a factory workshop; wherein e is a natural constant;
the environment monitoring module sends the environment risk coefficient JFn of the monitored area to a server, and then the server sends the environment risk coefficient JFn to a risk assessment module;
the supply and marketing analysis module is used for analyzing the supply and marketing conditions of a factory workshop, and the analysis process is specifically as follows:
acquiring a daily throughput Sd and a daily sales Vd of the last week of a factory workshop, wherein d is the number of the day of the last week, d=1, 2, … …, m, m=7;
calculating according to a formula ZXd =sd-Vd to obtain the daily deviation ZXd of each day of the week of the factory workshop;
if Σ ZXd is greater than 0, indicating that the last round of sale supply state is a sale-stagnation, and using a sale-stagnation risk calculation scheme;
if Σ ZXd is less than 0, the last round of sale supply state is described as out of sale, and the out of sale risk calculation scheme is used
If Σ ZXd is equal to 0, then it is explained that the sales risk factor is 0; wherein Σ ZXd is the amount of weekly deviation between the production volume and sales volume of the factory shop for one week;
in this embodiment, the risk of stagnation calculation scheme is specifically as follows:
when Sigma ZXd is more than 0 and less than or equal to Y1, the factory workshop is a three-level diapause risk, and the corresponding sales risk coefficient XF is w1;
when Y1 is less than Σ ZXd and less than or equal to Y2, the factory workshop is a secondary diapause risk, and the corresponding marketing risk coefficient XF is w2;
when Y2 is less than Σ ZXd, the factory workshop is the first-level risk of diapause, and the corresponding risk factor XF of supply and sales is w3; wherein Y1 and Y2 are preset deviation values of positive numbers, Y1 is more than 0 and less than Y2, w3, w2 and w1 are fixed values, and w3 is more than w2 and less than w1;
in this embodiment, the out-of-stock risk calculation scheme is specifically as follows:
when Y3 is less than or equal to Σ ZXd and less than 0, the factory workshop is a three-level out-of-stock risk, and the corresponding sales risk coefficient XF is w4;
when Y4 is less than or equal to Σ ZXd and less than Y3, the factory workshop is a secondary out-of-stock risk, and the corresponding supply and sales risk coefficient XF is w5;
when Σ ZXd is smaller than Y4, the factory workshop is the first-level out-of-stock risk, and the corresponding supply and sales risk coefficient XF is w6; wherein Y3 and Y4 are preset deviation values of negative numbers, Y4 is less than Y3 and less than 0, w4, w5 and w6 are fixed values, and w4 is less than w5 and less than w6;
the supply and sales analysis module feeds back a supply and sales risk coefficient XF of the factory workshop to the server, and the server sends the supply and sales risk coefficient XF to the risk assessment module;
the risk assessment module is used for assessing the industrial production risk of the factory workshop, and the assessment process is specifically as follows:
acquiring the calculated equipment risk coefficient BXn of the monitoring area, the environment risk coefficient JFn of the monitoring area and the supply and sales risk coefficient XF of the factory workshop;
calculating a risk coefficient value FXn of the monitoring area through a formula FXn = BXn ×c1+ JFn ×c2, wherein the weight coefficients of c1 and c2 with fixed values are respectively larger than zero;
generating a corresponding signal according to the risk coefficient value, generating a regional safety signal when the risk coefficient value is smaller than or equal to the risk coefficient threshold value, and generating a regional abnormal signal when the risk coefficient value is larger than the risk coefficient threshold value;
acquiring a preset supply and sales risk coefficient of a factory workshop, generating a supply and sales abnormal signal if the supply and sales risk coefficient is larger than the preset supply and sales risk coefficient, and generating a supply and sales normal signal if the supply and sales risk coefficient is smaller than or equal to the preset supply and sales risk coefficient;
the risk assessment module feeds back an area safety signal, an area abnormal signal, a supply and sale abnormal signal or a supply and sale normal signal to the server, if the server receives the area safety signal or the supply and sale normal signal, no operation is performed, if the server receives the area abnormal signal or the supply and sale abnormal signal, an alarm instruction is generated and loaded to the alarm module, and the alarm module receives the alarm instruction and then performs alarm work;
in the implementation, the alarm module is related equipment such as an audible and visual alarm and the like arranged in a factory workshop and a monitoring area in the factory workshop;
compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of firstly, carrying out regional division on a factory workshop by using a regional division module to obtain a plurality of monitoring regions, monitoring equipment conditions of the monitoring regions by using an equipment monitoring module, obtaining equipment risk coefficients of the monitoring regions, sending the equipment risk coefficients to a risk assessment module, monitoring the environment conditions of the monitoring regions by using an environment monitoring module, obtaining the environment risk coefficients of the monitoring regions, sending the environment risk coefficients to the risk assessment module, finally analyzing the sales conditions of the factory workshop by using a sales analysis module, obtaining the sales risk coefficients of the factory workshop, sending the sales risk coefficients to the risk assessment module, and assessing industrial production risks of the factory workshop by using the risk assessment module to generate regional safety signals, regional abnormal signals, sales abnormal signals or sales normal signals;
the above formulas are all the dimensionality-removed numerical calculations, such as existing weight coefficients, scale coefficients and the like, and the set size is a result value obtained by quantizing each parameter, and the size of the weight coefficients and the scale coefficients is only required to be not influenced as long as the proportional relation between the parameters and the result value is not influenced.
Based on the further conception of the unified invention, a working method of an industrial production risk assessment system based on big data is provided, and the working method specifically comprises the following steps:
step S100, a region dividing module divides a factory workshop into a plurality of monitoring regions, a database stores standard environmental information of the factory workshop and sends the standard environmental information to an environmental monitoring module, a data acquisition module acquires real-time environmental information in the monitoring region of the factory workshop, equipment information of equipment in the monitoring region and supply and sales information of the factory workshop and sends the real-time environmental information, the equipment information and the supply and sales information to a server, the server sends the real-time environmental information to the environmental monitoring module, the server sends the equipment information to the equipment monitoring module, and the server sends the supply and sales information to a supply and sales analysis module;
step S200, a device monitoring module monitors the device condition of a monitoring area, acquires the failure times of devices in the monitoring area and the maintenance time when each failure occurs, acquires the purchase time of the devices in the monitoring area, obtains the purchase time of the devices in the monitoring area by subtracting the purchase time from the current time of a server, acquires the number of the devices in the monitoring area and records the number of the devices as the number of the devices, adds up the failure times of the devices in the monitoring area and divides the number of the devices to obtain the average failure times of the devices in the monitoring area, calculates the maintenance average time and the purchase average time of the devices in the monitoring area, calculates the device risk value of the monitoring area, obtains the device risk coefficient of the monitoring area according to the device risk value, and the device monitoring module feeds the device risk coefficient of the monitoring area back to the server which sends the device risk coefficient to a risk evaluation module;
step S300, an environment monitoring module monitors the environment condition of a monitoring area, a standard temperature value, a standard humidity value and a standard air flow rate of a factory workshop are obtained through a database, then the temperature value, the humidity value, the air flow rate, the suspended particle amount and the toxic gas content of the monitoring area are obtained, the temperature deviation value, the humidity deviation value and the air flow rate deviation value of the monitoring area in the factory workshop are calculated, the environment risk coefficient of the monitoring area in the factory workshop is calculated, the environment risk coefficient of the monitoring area is sent to a server by the environment monitoring module, and then the environment risk coefficient is sent to a risk assessment module by the server;
step S400, a sales analysis module analyzes sales conditions of a factory workshop to obtain daily throughput and daily sales of the factory workshop, calculates daily deviation of each day of the week of the factory workshop, adds up the daily deviation to obtain a circumferential deviation, if the circumferential deviation is greater than zero, indicates that the last week sales state is a sales stagnation, calculates the factory workshop to obtain a three-level sales stagnation risk, a two-level sales stagnation risk or a one-level sales stagnation by using a sales stagnation calculation scheme to obtain a sales risk coefficient corresponding to the factory workshop, if the circumferential deviation is less than zero, indicates that the last week sales state is a sales withdrawal, obtains the factory workshop to be a three-level sales withdrawal risk, a two-level sales withdrawal risk or a one-level sales withdrawal risk by using a sales withdrawal risk calculation scheme, so as to obtain a sales risk coefficient corresponding to the factory workshop, and if the circumferential deviation is equal to zero, indicates that the sales risk coefficient is zero, and the sales risk coefficient of the factory workshop is fed back to a server by the sales analysis module;
in step S500, the risk assessment module assesses the industrial production risk of the factory workshop, acquires the equipment risk coefficient of the monitoring area, the environmental risk coefficient of the monitoring area and the supply and sales risk coefficient of the factory workshop, calculates the risk coefficient value of the monitoring area, generates an area safety signal when the risk coefficient value is smaller than or equal to the risk coefficient threshold, generates an area abnormal signal when the risk coefficient value is larger than the risk coefficient threshold, acquires a preset supply and sales risk coefficient of the factory workshop, generates a supply and sales abnormal signal when the supply and sales risk coefficient is larger than the preset supply and sales risk coefficient, generates a supply and sales normal signal when the supply and sales risk coefficient is smaller than or equal to the preset supply and sales risk coefficient, and feeds back the area safety signal, the area abnormal signal, the supply and sales abnormal signal or the supply and sales normal signal to the server.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The industrial production risk assessment system based on big data is characterized by comprising a region division module, a data acquisition module, an equipment monitoring module, an environment monitoring module, a supply and sales analysis module, a risk assessment module, a database and a server, wherein the region division module is used for dividing a factory workshop into a plurality of monitoring regions; the database is used for storing standard environment information of a factory workshop and sending the standard environment information to the environment monitoring module; the data acquisition module is used for acquiring real-time environment information in a monitoring area in a factory workshop, equipment information of equipment in the monitoring area and supply and sales information of the factory workshop and sending the real-time environment information to the environment monitoring module, the equipment information to the equipment monitoring module and the supply and sales information to the supply and sales analysis module;
the equipment monitoring module is used for monitoring equipment conditions of the monitoring area, obtaining equipment risk coefficients of the monitoring area and feeding the equipment risk coefficients back to the server, and the server sends the equipment risk coefficients to the risk assessment module;
the environment monitoring module is used for monitoring the environment condition of the monitoring area, obtaining an environment risk coefficient of the monitoring area and sending the environment risk coefficient to the server, and then the server sends the environment risk coefficient to the risk assessment module;
the supply and sales analysis module is used for analyzing supply and sales conditions of the factory workshop, obtaining supply and sales risk coefficients of the factory workshop, feeding the supply and sales risk coefficients back to the server, and sending the supply and sales risk coefficients to the risk assessment module by the server;
the risk assessment module is used for assessing the industrial production risk of the factory workshop and generating an area safety signal, an area abnormal signal, a supply and sales abnormal signal or a supply and sales normal signal.
2. The industrial production risk assessment system based on big data of claim 1, wherein the standard environmental information is a standard temperature value, a standard humidity value and a standard air flow rate of the factory floor;
the equipment information is the number of faults of equipment in the monitoring area, the maintenance time and the purchase time when each fault occurs;
the real-time environment information is a temperature value, a humidity value, an air flow rate, suspended particle amount and toxic gas content in the monitoring area;
the supply and sales information is the daily production amount and daily sales amount of the week in the factory shop.
3. The industrial production risk assessment system based on big data according to claim 2, wherein the monitoring process of the equipment monitoring module is specifically as follows:
acquiring the number of faults of equipment in a monitoring area and maintaining time length when each fault occurs;
obtaining purchase time of the equipment in the monitoring area, and subtracting the purchase time from the current time of the server to obtain purchase time of the equipment in the monitoring area;
the number of the devices in the monitoring area is obtained and recorded as the number of the devices, the number of the faults of the devices in the monitoring area is added and divided by the number of the devices to obtain the number of the average faults of the devices in the monitoring area, and the maintenance average time and the purchase average time of the devices in the monitoring area are obtained through calculation in the same way;
and calculating the equipment risk value of the monitoring area, and obtaining the equipment risk coefficient of the monitoring area according to the equipment risk value.
4. A big data based industrial production risk assessment system according to claim 3, wherein the monitoring process of said environmental monitoring module is specifically as follows:
obtaining a standard temperature value, a standard humidity value and a standard air flow rate of a factory workshop through a database;
then acquiring a temperature value, a humidity value, an air flow rate, suspended particle quantity and toxic gas content of a monitoring area;
firstly, calculating a temperature deviation value, a humidity deviation value and an air flow speed deviation value of a monitoring area in a factory workshop;
and then calculating the environmental risk coefficient of the monitoring area in the factory workshop.
5. The industrial production risk assessment system based on big data according to claim 4, wherein the analysis process of the supply and sales analysis module is specifically as follows:
acquiring daily throughput and daily sales of the week in a factory workshop;
calculating the daily deviation amount of each day of the week in the factory workshop, and adding and summing the daily deviation amounts to obtain the week deviation amount;
if the circumferential deviation is greater than zero, indicating that the last round of sales state is a diapause, and calculating to obtain a sales risk coefficient of a factory workshop by using a diapause risk calculation scheme;
if the circumferential deviation is smaller than zero, the last round of sales state is described as out-of-stock, and the sales risk coefficient of the factory workshop is calculated by using an out-of-stock risk calculation scheme;
if the circumferential deviation is equal to zero, the sales risk factor is zero.
6. The big data based industrial production risk assessment system of claim 5, wherein the diapause risk calculation scheme is specifically as follows:
the circumferential deviation amount is compared with a preset deviation amount, and the factory workshop is judged to be a third-level stagnation risk, a second-level stagnation risk or a first-level stagnation risk;
and obtaining corresponding sales risk coefficients of the factory workshop according to the three-level sales risk, the two-level sales risk or the first-level sales risk.
7. The big data based industrial production risk assessment system of claim 5, wherein the out-of-stock risk calculation scheme is specifically as follows:
the circumferential deviation amount is compared with a preset deviation amount, and the factory workshop is judged to be a third-level out-of-stock risk, a second-level out-of-stock risk or a first-level out-of-stock risk;
and obtaining a corresponding sales risk coefficient of the factory workshop according to the third-level sales risk, the second-level sales risk or the first-level sales risk.
8. The industrial production risk assessment system based on big data according to claim 5, wherein the risk assessment module has the following assessment procedures:
acquiring an equipment risk coefficient of a monitoring area, an environment risk coefficient J of the monitoring area and a supply and sales risk coefficient of a factory workshop;
calculating a risk coefficient value of the monitoring area;
generating a regional safety signal when the risk coefficient value is smaller than or equal to the risk coefficient threshold value, and generating a regional abnormality signal when the risk coefficient value is larger than the risk coefficient threshold value;
and then acquiring a preset supply and sales risk coefficient of a factory workshop, if the supply and sales risk coefficient is larger than the preset supply and sales risk coefficient, generating a supply and sales abnormal signal, and if the supply and sales risk coefficient is smaller than or equal to the preset supply and sales risk coefficient, generating a supply and sales normal signal.
9. The industrial production risk assessment system according to claim 6, further comprising an alarm module, wherein the risk assessment module feeds back an area safety signal, an area abnormality signal, a supply and sales abnormality signal or a supply and sales normal signal to the server, if the server receives the area safety signal or the supply and sales normal signal, no operation is performed, if the server receives the area abnormality signal or the supply and sales abnormality signal, an alarm instruction is generated and loaded to the alarm module, and the alarm module performs an alarm operation after receiving the alarm instruction.
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