Industrial process optimization information system based on big data analysis

Abstract

The invention relates to the technical field of big data analysis, in particular to an industrial process optimization information system based on big data analysis, which comprises an uploading module used for receiving production process information of production equipment in a production chain and storing the production process information; the monitoring module is used for traversing the production flow information stored in the uploading module and monitoring the health situation of the operation state of the production equipment and the change situation of the production demand by applying the production flow information; the production process information of the production equipment in the production chain is collected, healthy operation and production requirements of the production equipment are recognized, a certain prediction effect is brought to production faults of the production equipment, and therefore predictive fault management of the production equipment is achieved, and meanwhile, on the basis of the recognition result of the production requirements, the production efficiency is improved. The method can assist productivity managers in adaptively planning production tasks of produced products, ensures centralized processing of the production tasks, improves the production capacity of a production chain, and optimizes automatic production benefits of the production chain.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to an industrial process optimization information system based on big data analysis. Background Art

[0002] The production industry covers many fields. For example, the manufacturing industry is responsible for processing raw materials into various products; the processing industry focuses on further processing of semi-finished products, etc. In the production process, it often involves the interaction of multiple production equipment to achieve the output of the final product.

[0003] The invention patent with application number 202311566704.0 discloses a data processing management method based on big data, including: obtaining engineering result management parameter data, and conducting effectiveness analysis based on data governance to form engineering management parameter analysis result data; obtaining extraction processing information and process processing information of each of the engineering result management parameters, and performing optimization comparison parameter analysis to form optimization comparison analysis information; according to the optimization comparison analysis, performing optimization analysis based on data governance to form process efficiency optimization guidance data; wherein, obtaining engineering result management parameter data, and conducting effectiveness analysis based on data governance to form engineering management parameter analysis result data, includes: determining the processing flow of different engineering result management parameters, and setting an effective analysis cycle; within the effective analysis cycle, counting the output frequency of all the engineering result management parameters to form a process output frequency A. n , where n represents the number of different engineering result management parameters.

[0004] This application aims to solve the problem that "the content of current engineering management data is diverse and complex, and obtaining a final engineering management result data often requires integration with different original parameter data during the formation process. However, there is currently no reasonable and efficient data collection and use method for obtaining engineering management result data in combination with different original parameter data."

[0005] However, for industrial production scenarios, the production targets are circulated and processed on the production line and finally output as finished products. Although existing technologies have achieved unmanned automatic production, the management of multiple production equipment and the production planning and regulation of production targets still require manual control.

[0006] To this end, we proposed an industrial process optimization information system based on big data analysis. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an industrial process optimization information system based on big data analysis, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] Industrial process optimization information system based on big data analysis, including:

[0010] The upload module is used to receive the production process information of the production equipment in the production chain and store the production process information; the monitoring module is used to traverse the production process information stored in the upload module, and use the production process information to monitor the health status of the production equipment and the changing trend of production demand; the identification module is used to set the health judgment logic of the production equipment operation status and the safety judgment logic of the production demand, receive the monitoring results of the health status of the production equipment operation status and the monitoring results of the changing trend of production demand in the monitoring module, and identify whether the production equipment is healthy and whether the production demand is safe based on the two sets of monitoring results combined with the judgment logic; the sniffing module is used to receive the identification results of whether the production equipment is healthy and whether the production demand is safe in the identification module, and sniff the production equipment pointed to by the identification results; the feedback module is used to receive the sniffing results of the sniffing module and output the sniffing results in the sniffing module; the message module is used to generate system operation messages and store the operation messages.

[0011] Furthermore, the upload module is provided with submodules at the lower level, including:

[0012] A cleaning unit is used to traverse the production process information uploaded in the upload module and clean the production process information;

[0013] A storage unit is used to receive the production process information of the cleaning process completed in the cleaning unit and store the production process information;

[0014] Among them, the cleaning unit is configured with cleaning logic, and the cleaning unit performs cleaning processing on the production process information based on the cleaning logic. Except for the first time that the uploading module receives the production process information, the cleaning unit performs two cleaning processings on the production process information newly received by the uploading module. The cleaning processing target of the first operation of the cleaning unit is the production process information newly received by the uploading module, and the cleaning processing target of the second operation is the production process information that has been cleaned in the previous operation and the production process information stored in the storage unit.

[0015] Furthermore, the production process information includes: production product parameter information and production equipment operation parameter information, and the production product parameter information and production equipment operation parameter information in the production process information correspond to each other one by one;

[0016] The cleaning logic for the production process information in the cleaning unit is expressed as follows:

[0017]

[0018] Where: F is the consistency judgment value of the production process information; |A∩B| is the intersection of the set of elements in the production process information A and the combination of elements in the production process information B; |A∪B| is the union of the set of elements in the production process information A and the combination of elements in the production process information B;

[0019] Among them, after the judgment value F is calculated based on formula (1), it is judged by formulas (2) and (3). When the judgment value F meets formula (2), the source timestamps of production process information A and production process information B are identified, and the production process information with the closest source timestamp is selected as the retention target, and the other production process information is selected as the cleaning target, and the deletion process is performed. When the judgment value F meets formula (3), the production process information with fewer elements in the two production process information is selected as the cleaning target, and the deletion process is performed.

[0020] Furthermore, when the monitoring module traverses the production process information, the traversal target is the production process information stored in the storage unit. The monitoring module monitors the changing trend of production demand based on the production product parameter information in the production process information, and monitors the health status of the production equipment operation status based on the production equipment operation parameter information in the production process information.

[0021] The monitoring logic of the change trend of the production demand is expressed as:

[0022]

[0023] Where: χ1 is the performance value of production demand change trend; n is the number of groups of production product parameter information in the production process information; g i , g i+1 is the number of products produced in the product parameter information of the i-th group and the i+1-th group; t i ,t i+1 The execution time of the product production tasks in the product parameter information of the i-th group and the i+1-th group;

[0024] Among them, |t i -t i+1 | represents the interval between the execution time of the production tasks in the i-th group and the i+1-th group of production product parameter information. The larger the production demand change trend performance value χ1 is, the higher the production demand is. On the contrary, the lower the production demand is, the g i , g i+1 ,t i ,t i+1 All are derived from production product parameter information.

[0025] Furthermore, the monitoring logic of the health status of the production equipment operation status is expressed as:

[0026]

[0027] Where: χ2 is the health status performance value of the production equipment operation status; m is the number of groups of production equipment operation parameter information in the production process information; σ j , σ j+1 is the qualified rate of products produced in the operating parameter information of the production equipment in the jth group and j+1 group; p j 、p j+1 The production frequency of the products in the operating parameter information of the production equipment in the jth group and the j+1th group;

[0028] The larger the health performance value χ2 of the operating status of the production equipment is, the healthier the operating status of the production equipment is. On the contrary, the unhealthier the operating status of the production equipment is, the j , σ j+1 、p j 、p j+1 All of them are derived from the operating parameter information of production equipment.

[0029] Furthermore, the monitoring module is internally provided with submodules, including:

[0030] A visualization unit is used to receive the production demand change situation performance values ​​and the production equipment operation status health situation performance values ​​continuously monitored by the monitoring module, and create trend graphs representing the production demand change situation and the production equipment operation status health situation based on the cumulatively received production demand change situation performance values ​​and the production equipment operation status health situation performance values;

[0031] Among them, the visualization unit runs synchronously with the monitoring module, and the monitoring module runs synchronously based on the storage and update of the production process information in the storage unit. The horizontal axis in the trend graph represents time, and the vertical axis represents the performance value of the production demand change trend or the performance value of the healthy status of the production equipment operation status.

[0032] Furthermore, the healthy determination logic of the production equipment operation status in the identification module is:

[0033]

[0034] Where: χ2(new-2), χ2(new-1), χ2(new) are the latest three sets of production equipment operation status health status performance values; χ2(norr) is the production equipment operation status health judgment value;

[0035] When equation (1) and equation (2) are both true, it means that the production equipment is in a healthy state. Otherwise, it means that the production equipment is in an unhealthy state. χ2(norr) is defined by the system user, and χ2(new-2), χ2(new-1), and χ2(new) are derived from their corresponding trend charts.

[0036] The production demand safety determination logic in the identification module is:

[0037]

[0038] Where: χ1(new-2), χ1(new-1), χ1(new) are the latest three sets of production demand change trend performance values; χ1(norr) is the production demand safety judgment value;

[0039] When equation (1) and equation (2) are both true, it means that the production demand is safe. Otherwise, it means that the production demand is unsafe. χ1(norr) is defined by the system user, and χ1(new-2), χ1(new-1), and χ1(new) are derived from their corresponding trend charts.

[0040] Furthermore, each group of production process information received by the upload module is marked with the number of the source production equipment. When the sniffing module receives an unhealthy or unsafe identification result, it synchronously obtains the identification result and outputs the applied production process information in the identification module, and further determines the production equipment pointed to by the identification result according to the corresponding mark of the production process information;

[0041] The feedback target of the feedback module is a storage unit set at the lower level of the upload module, and the system end user reads the production equipment fed back by the feedback module in the storage unit;

[0042] Among them, the content of the feedback output by the feedback module is the corresponding mark of the production equipment sniffed by the sniffing module.

[0043] Furthermore, the system operation message content generated in the message module is: the sniffing results accumulated by the historical operation of the sniffing module, and the trend chart representing the changing trend of production demand and the healthy trend of the operation status of the production equipment;

[0044] Among them, the system end user accesses the system and reads the system operation message in the message module.

[0045] Furthermore, the upload module is interactively connected to a cleaning unit and a storage unit at a lower level through a wireless network, the upload module is interactively connected to a monitoring module through a wireless network, the monitoring module is interactively connected to the storage unit through a wireless network, the monitoring module is interactively connected to a visualization unit through a wireless network, the monitoring module is interactively connected to an identification module, a sniffing module and a feedback module through a wireless network, the feedback module is interactively connected to the storage unit through a wireless network, and the feedback module is interactively connected to a message module through a wireless network.

[0046] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0047] The present invention provides an industrial process optimization information system based on big data analysis. During operation, the system collects production process information of production equipment in the production chain to identify the health of the production equipment and production needs. Based on the identification results, it brings a certain prediction effect for the production failures of the production equipment, thereby realizing predictive fault management of the production equipment. At the same time, based on the identification results of production needs, it can assist productivity management personnel to adaptively plan the production tasks of production products, ensure centralized processing of production tasks, improve the production capacity of the production chain, and optimize the automated production benefits of the production chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 Schematic diagram of the structure of the information system for industrial process optimization based on big data analysis. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0051] The present invention will be further described below in conjunction with the embodiments.

[0052] Example:

[0053] The industrial process optimization information system based on big data analysis in this embodiment is as follows: Figure 1 As shown, including:

[0054] The upload module is used to receive the production process information of the production equipment in the production chain and store the production process information;

[0055] The upload module has submodules, including:

[0056] A cleaning unit is used to traverse the production process information uploaded in the upload module and clean the production process information;

[0057] A storage unit is used to receive the production process information of the cleaning process completed in the cleaning unit and store the production process information;

[0058] Among them, the cleaning unit is configured with a cleaning logic, and the cleaning unit performs cleaning processing on the production process information based on the cleaning logic. Except for the first time when the uploading module receives the production process information, the cleaning unit performs two cleaning processing on the production process information newly received by the uploading module. The cleaning processing target of the first operation of the cleaning unit is the production process information newly received by the uploading module, and the cleaning processing target of the second operation is the production process information that has been cleaned in the previous operation and the production process information stored in the storage unit;

[0059] The production process information includes: production product parameter information and production equipment operation parameter information. The production product parameter information and production equipment operation parameter information in the production process information correspond to each other.

[0060] The cleaning logic for production process information in the cleaning unit is expressed as:

[0061]

[0062] Where: F is the consistency judgment value of the production process information; |A∩B| is the intersection of the set of elements in the production process information A and the combination of elements in the production process information B; |A∪B| is the union of the set of elements in the production process information A and the combination of elements in the production process information B;

[0063] Among them, after the judgment value F is calculated based on formula (1), it is judged by formulas (2) and (3). When the judgment value F meets formula (2), the source timestamps of production process information A and production process information B are identified, and the production process information with the closest source timestamp is selected as the retention target, and the other production process information is selected as the cleaning target, and the deletion process is performed. When the judgment value F meets formula (3), the production process information with fewer elements in the two production process information is selected as the cleaning target, and the deletion process is performed;

[0064] The monitoring module is used to traverse the production process information stored in the upload module, and use the production process information to monitor the health status of the production equipment and the changes in production demand;

[0065] The monitoring module is internally provided with submodules, including:

[0066] A visualization unit is used to receive the production demand change situation performance values ​​and the production equipment operation status health situation performance values ​​continuously monitored by the monitoring module, and create trend graphs representing the production demand change situation and the production equipment operation status health situation based on the cumulatively received production demand change situation performance values ​​and the production equipment operation status health situation performance values;

[0067] Among them, the visualization unit runs synchronously with the monitoring module, and the monitoring module runs synchronously based on the storage and update of the production process information in the storage unit. The horizontal axis of the trend chart represents time, and the vertical axis represents the performance value of the production demand change situation or the performance value of the healthy situation of the production equipment operation status;

[0068] The identification module is used to set the health judgment logic of the production equipment operation status and the safety judgment logic of the production demand, receive the monitoring results of the health situation of the production equipment operation status and the monitoring results of the production demand change situation in the monitoring module, and identify whether the production equipment is healthy and whether the production demand is safe based on the two sets of monitoring results combined with the judgment logic;

[0069] The healthy judgment logic of the production equipment operation status in the identification module is:

[0070]

[0071] Where: χ2(new-2), χ2(new-1), χ2(new) are the latest three sets of production equipment operation status health status performance values; χ2(norr) is the production equipment operation status health judgment value;

[0072] When equation (1) and equation (2) are both true, it means that the production equipment is in a healthy state. Otherwise, it means that the production equipment is in an unhealthy state. χ2(norr) is defined by the system user, and χ2(new-2), χ2(new-1), and χ2(new) are derived from their corresponding trend charts.

[0073] The production demand safety decision logic in the identification module is:

[0074]

[0075] Where: χ1(new-2), χ1(new-1), χ1(new) are the latest three sets of production demand change trend performance values; χ1(norr) is the production demand safety judgment value;

[0076] When equation (1) and equation (2) are both true, it means that the production demand is safe. Otherwise, it means that the production demand is unsafe. χ1(norr) is defined by the system user, and χ1(new-2), χ1(new-1), and χ1(new) are derived from their corresponding trend charts.

[0077] Through the above logic formula, the logic of determining the health status of the production equipment in the identification module is further limited.

[0078] When the monitoring module traverses the production process information, the traversal target is the production process information stored in the storage unit. The monitoring module monitors the changing trend of production demand based on the production product parameter information in the production process information, and monitors the health status of the production equipment operation status based on the production equipment operation parameter information in the production process information;

[0079] The monitoring logic of the changing situation of production demand is expressed as:

[0080]

[0081] Where: χ1 is the performance value of production demand change trend; n is the number of groups of production product parameter information in the production process information; g i , g i+1 is the number of products produced in the product parameter information of the i-th group and the i+1-th group; t i ,t i+1 The execution time of the product production tasks in the product parameter information of the i-th group and the i+1-th group;

[0082] Among them, |t i -t i+1 | represents the interval of the execution time of the product production tasks in the i-th group and the i+1-th group of production product parameter information. The larger the production demand change trend performance value χ1 is, the higher the production demand is. Conversely, the lower the production demand is, g i , g i+1 ,t i ,t i+1 All are derived from production product parameter information;

[0083] The monitoring logic of the health status of production equipment operation is expressed as:

[0084]

[0085] Where: χ2 is the health status performance value of the production equipment operation status; m is the number of groups of production equipment operation parameter information in the production process information; σ j , σ j+1 is the qualified rate of products produced in the operating parameter information of the production equipment in the jth group and j+1 group; p j 、p j+1 The production frequency of the products in the operating parameter information of the production equipment in the jth group and the j+1th group;

[0086] Among them, the larger the health performance value of the production equipment operation status χ2, the healthier the production equipment operation status is, and vice versa, the unhealthier the production equipment operation status is, σ j , σ j+1 、p j 、p j+1 All are derived from the operating parameter information of production equipment;

[0087] Through the above logical formula, the changing trend of production demand and the healthy status of production equipment operation status are calculated and output in digital form, providing necessary data support for the creation of trend charts representing the changing trend of production demand and the healthy status of production equipment operation status.

[0088] The sniffing module is used to receive the identification results of whether the production equipment is healthy and whether the production demand is safe in the identification module, and sniff the production equipment pointed to by the identification results;

[0089] A feedback module is used to receive the sniffing result of the sniffing module and output the sniffing result in the sniffing module;

[0090] The message module is used to generate system operation messages and store the operation messages;

[0091] The upload module is interactively connected to the cleaning unit and the storage unit through a wireless network, the upload module is interactively connected to the monitoring module through a wireless network, the monitoring module is interactively connected to the storage unit through a wireless network, the monitoring module is interactively connected to the visualization unit through a wireless network, the monitoring module is interactively connected to the identification module, the sniffing module and the feedback module through a wireless network, the feedback module is interactively connected to the storage unit through a wireless network, and the feedback module is interactively connected to the message module through a wireless network.

[0092] In this embodiment, the upload module runs to receive the production process information of the production equipment in the production chain, and stores the production process information; the cleaning unit synchronously traverses the production process information uploaded in the upload module, and performs cleaning processing on the production process information; the storage unit receives the production process information that has completed the cleaning processing in the cleaning unit in real time, and stores the production process information; the monitoring module post-operates to traverse the production process information stored in the upload module, and uses the production process information to monitor the healthy state of the operation status of the production equipment and the changing state of production demand; the visualization unit synchronously receives the production demand change situation performance value and the production equipment operation status healthy state performance value monitored by the monitoring module continuously; based on the accumulated received production demand change situation performance value and the production equipment operation status healthy state performance value Situation performance value, create trend graphs representing the changing situation of production demand and the healthy situation of production equipment operation status respectively, the identification module further sets the healthy judgment logic of production equipment operation status and the safe judgment logic of production demand, receives the monitoring results of the healthy situation of production equipment operation status and the monitoring results of production demand change situation in the monitoring module, and identifies whether the production equipment is healthy and whether the production demand is safe based on the two sets of monitoring results combined with the judgment logic, and then the sniffing module receives the identification results of whether the production equipment is healthy and whether the production demand is safe in the identification module, sniffs the production equipment pointed by the identification result, and receives the sniffing result of the sniffing module through the feedback module, outputs the sniffing result in the sniffing module, and finally generates the system operation message by the message module, and stores the operation message;

[0093] Through the operation of the system in the above embodiment, the operation optimization of production equipment in the industrial automation production scenario is effectively improved, ensuring that the production equipment can receive more predictive fault maintenance. At the same time, based on the system, further data reference is provided to the users of the production equipment management end, which brings a certain degree of optimization effect to the production planning of production product production tasks.

[0094] like Figure 1 As shown, each group of production process information received by the upload module is marked with the number of the source production equipment. When the sniffing module receives an unhealthy or unsafe identification result, it synchronously obtains the identification result and outputs the applied production process information in the identification module, and further determines the production equipment pointed to by the identification result according to the corresponding mark of the production process information;

[0095] The feedback target of the feedback module is the storage unit set at the lower level of the upload module, and the system end user reads the production equipment fed back by the feedback module in the storage unit;

[0096] The content of the feedback module's output feedback is the corresponding mark of the production equipment sniffed by the sniffing module;

[0097] The system operation message content generated in the message module is: the sniffing results accumulated by the historical operation of the sniffing module, and the trend chart showing the changing trend of production demand and the healthy trend of the operation status of production equipment;

[0098] Among them, the system end user accesses the system and reads the system operation message in the message module.

[0099] The above settings provide further operation data and logic support for the operation of the system in the above embodiments, ensuring stable operation of the system in the above embodiments and providing optimization effect for the daily production of the production chain composed of production equipment.

[0100] In summary, during operation, the system in the above embodiment collects production process information of production equipment in the production chain to identify the health of production equipment and production needs, thereby achieving a certain prediction effect for production failures of production equipment based on the identification results, thereby realizing predictive fault management of production equipment. At the same time, based on the identification results of production needs, it can assist productivity management personnel to adaptively plan the production tasks of production products, ensure centralized processing of production tasks, improve the production capacity of the production chain, and optimize the automated production benefits of the production chain.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An industrial process optimization information system based on big data analysis, characterized by: include: The upload module is used to receive the production process information of the production equipment in the production chain and store the production process information; The monitoring module is used to traverse the production process information stored in the upload module, and use the production process information to monitor the health status of the production equipment and the changes in production demand; The identification module is used to set the health judgment logic of the production equipment operation status and the safety judgment logic of the production demand, receive the monitoring results of the health situation of the production equipment operation status and the monitoring results of the production demand change situation in the monitoring module, and identify whether the production equipment is healthy and whether the production demand is safe based on the two sets of monitoring results combined with the judgment logic; The sniffing module is used to receive the identification results of whether the production equipment is healthy and whether the production demand is safe in the identification module, and sniff the production equipment pointed to by the identification results; A feedback module is used to receive the sniffing result of the sniffing module and output the sniffing result in the sniffing module; The message module is used to generate system operation messages and store the operation messages.

2. The industrial process optimization information system based on big data analysis according to claim 1 is characterized in that: The upload module is provided with submodules at the lower level, including: A cleaning unit is used to traverse the production process information uploaded in the upload module and perform cleaning processing on the production process information; A storage unit, used to receive the production process information of the cleaning process completed in the cleaning unit, and store the production process information; Among them, the cleaning unit is configured with cleaning logic, and the cleaning unit performs cleaning processing on the production process information based on the cleaning logic. Except for the first time that the uploading module receives the production process information, the cleaning unit performs two cleaning processings on the production process information newly received by the uploading module. The cleaning processing target of the first operation of the cleaning unit is the production process information newly received by the uploading module, and the cleaning processing target of the second operation is the production process information that has been cleaned in the previous operation and the production process information stored in the storage unit.

3. The industrial process optimization information system based on big data analysis according to claim 2 is characterized in that: The production process information includes: production product parameter information and production equipment operation parameter information, and the production product parameter information and production equipment operation parameter information in the production process information correspond to each other one by one; The cleaning logic for the production process information in the cleaning unit is expressed as: Where: F is the consistency judgment value of the production process information; |A∩B| is the intersection of the set of elements in the production process information A and the combination of elements in the production process information B; |A∪B| is the union of the set of elements in the production process information A and the combination of elements in the production process information B; Among them, after the judgment value F is calculated based on formula (1), it is judged by formulas (2) and (3). When the judgment value F meets formula (2), the source timestamps of production process information A and production process information B are identified, and the production process information with the closest source timestamp is selected as the retention target, and the other production process information is selected as the cleaning target, and the deletion process is performed. When the judgment value F meets formula (3), the production process information with fewer elements in the two production process information is selected as the cleaning target, and the deletion process is performed.

4. The industrial process optimization information system based on big data analysis according to claim 1 is characterized in that: When the monitoring module traverses the production process information, the traversal target is the production process information stored in the storage unit. The monitoring module monitors the changing trend of production demand based on the production product parameter information in the production process information, and monitors the health status of the production equipment operation status based on the production equipment operation parameter information in the production process information; The monitoring logic of the change trend of the production demand is expressed as: Where: χ1 is the performance value of production demand change trend; n is the number of groups of production product parameter information in the production process information; g i , g i+1 is the number of products produced in the product parameter information of the i-th group and the i+1-th group; t i ,t i+1 The execution time of the product production tasks in the product parameter information of the i-th group and the i+1-th group; Among them, |t i -t i+1 | represents the interval between the execution time of the production tasks in the i-th group and the i+1-th group of production product parameter information. The larger the production demand change trend performance value χ1 is, the higher the production demand is. On the contrary, the lower the production demand is, the g i , g i+1 ,t i ,t i+1 All are derived from production product parameter information.

5. The industrial process optimization information system based on big data analysis according to claim 1 is characterized in that: The monitoring logic of the health status of the production equipment operation status is expressed as: Where: χ2 is the health status performance value of the production equipment operation status; m is the number of groups of production equipment operation parameter information in the production process information; σ j , σ j+1 is the qualified rate of products produced in the operating parameter information of the production equipment in the jth group and j+1 group; p j 、p j+1 The production frequency of the products in the operating parameter information of the production equipment in the jth group and the j+1th group; The larger the health performance value χ2 of the operating status of the production equipment is, the healthier the operating status of the production equipment is. On the contrary, the unhealthier the operating status of the production equipment is, the j , σ j+1 、p j 、p j+1 All of them are derived from the operating parameter information of production equipment.

6. The industrial process optimization information system based on big data analysis according to claim 1 is characterized in that: The monitoring module is internally provided with submodules, including: A visualization unit is used to receive the production demand change situation performance values ​​and the production equipment operation status health situation performance values ​​continuously monitored by the monitoring module, and create trend graphs representing the production demand change situation and the production equipment operation status health situation based on the cumulatively received production demand change situation performance values ​​and the production equipment operation status health situation performance values; Among them, the visualization unit runs synchronously with the monitoring module, and the monitoring module runs synchronously based on the storage and update of the production process information in the storage unit. The horizontal axis in the trend graph represents time, and the vertical axis represents the performance value of the production demand change trend or the performance value of the healthy status of the production equipment operation status.

7. The industrial process optimization information system based on big data analysis according to claim 1 is characterized in that: The healthy judgment logic of the production equipment operation status in the identification module is: Where: χ2(new-2), χ2(new-1), χ2(new) are the latest three sets of production equipment operation status health status performance values; χ2(norr) is the production equipment operation status health judgment value; When equation (1) and equation (2) are both true, it means that the production equipment is in a healthy state. Otherwise, it means that the production equipment is in an unhealthy state. χ2(norr) is defined by the system user, and χ2(new-2), χ2(new-1), and χ2(new) are derived from their corresponding trend charts. The production demand safety determination logic in the identification module is: Where: χ1(new-2), χ1(new-1), χ1(new) are the latest three sets of production demand change trend performance values; χ1(norr) is the production demand safety judgment value.

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