Data calculation management method and system based on biological blood relationship

Abstract

The present invention discloses a data calculation management method based on biological blood relationship and a system thereof, the method comprising: obtaining production data from industrial sensors, obtaining industrial calculation logic and pre-calculation logic corresponding to the production data from process processors, the production data comprising field data and intermediate data obtained by calculating the field data through pre-calculation logic; dividing the production data into native data and derived data according to the source relationship, the field data being native data, and the intermediate data and the results of the industrial calculation logic being derived data; replacing the derived data with the native data and pre-calculation logic for calculating the derived data, marking the corresponding blood relationship for the native data and related derived data involved in the calculation; when the latest intermediate data is missing when calculating the derived data, replacing the intermediate data with the native data corresponding to the blood relationship and its pre-calculation logic, and then calculating the derived data; this is beneficial to reducing the probability of the calculation falling into infinite waiting.

Description

Technical Field

[0001] The present invention relates to the field of data computing technology, and more specifically, to a data computing management method and system based on biological blood relationship. Background Art

[0002] With the continuous development of science and technology, industrial process control has gradually become data-based and intelligent, and automatic control has become more and more common. In the process of automatic control, it is necessary to continuously obtain data collected on-site and calculate the control results based on the data.

[0003] In the process of industrial automatic control, data calculation problems are also frequent. For example, in the calculation of indicators in the production execution management system, the problem of data calculation failure is often encountered. The specific reasons for the above problems are described in simple language, such as: A indicator = B indicator + 5, B indicator = C indicator + D indicator. If the standard formula is configured with the correct calculation logic, there is no problem. Among them, B indicator needs to wait for the calculation results of C indicator and D indicator before it can be calculated. If the calculation logic of B indicator = C indicator + D indicator is calculated, the formula before the calculation logic of calculating B indicator due to on-site process parameters falls into an infinite loop, and B indicator cannot be calculated, which will cause A indicator to be unable to be calculated, causing the automatic control to fall into an infinite waiting state. Summary of the invention

[0004] In order to reduce the probability of falling into an infinite waiting state due to the inability to calculate some data during the automatic control process of the production execution management system, the present application provides a data calculation management method and system based on biological blood relationship.

[0005] In the first aspect, the present application provides a data calculation management method based on biological blood relationship, and the technical solution is as follows:

[0006] A data calculation management method based on biological blood relationship, based on multiple process processors and multiple industrial sensors connected to each other, wherein the process processors are connected to the industrial sensors, comprising the following steps:

[0007] Acquire production data from the industrial sensor, and acquire industrial calculation logic and pre-calculation logic corresponding to the production data from the process processor, wherein the production data includes directly collected field data and intermediate data obtained by calculating the field data through the pre-calculation logic;

[0008] The production data is divided into primary data and derived data according to the source relationship, the field data is the primary data, and the intermediate data and the result of the industrial calculation logic are both derived data;

[0009] The derivative data in the industrial calculation logic is replaced with the native data for calculating the derivative data and the pre-calculation logic or industrial calculation logic corresponding to the native data, and the native data involved in the calculation and the related derivative data are marked with corresponding blood relationships, and the content of the blood relationship includes the corresponding relationship between the native data and the derivative data and the pre-calculation logic or industrial calculation logic corresponding to the native data;

[0010] Based on a request to calculate derived data, if the latest intermediate data is missing when calculating the derived data, the intermediate data is replaced with native data corresponding to the blood relationship and pre-calculation logic or industrial calculation logic corresponding to the native data, and then the derived data is calculated; and,

[0011] If the native data is updated, the derived data corresponding to the blood relationship of the native data is automatically updated.

[0012] By adopting the above technical solution, the data in the industrial automatic control process is marked according to the blood relationship in biology, and the data is divided into native data and derived data and marked. When the derived data operator of the industrial calculation logic is missing or not updated in time, it is replaced with the corresponding native data and its pre-calculation logic according to the blood relationship mark, which effectively solves the problem of falling into an infinite waiting state due to the inability to calculate some data in the automatic control process. At the same time, when updating data on the spot, it is not necessary to update all data, only the derived data with blood relationship needs to be updated, which saves computing resources and reduces the probability of data calculation problems caused by the artificial introduction of computing levels during the execution of the production execution management system.

[0013] Optionally, the step of calculating the derivative data further includes:

[0014] while calculating the derived data, issuing a request to obtain new native data having a corresponding blood relationship; and,

[0015] If new native data with corresponding blood relationship is obtained within a preset time, the derived data is calculated for a second time.

[0016] By adopting the above technical solution, the acquisition of native data has a certain time period. If the calculation time of the derived data is extended due to waiting for other data, the time point of obtaining new native data can be reached. At this time, the new native data can be used to recalculate the derived data. At this time, other data has just been waited for, and there is no need to wait again and delay the calculation time, thereby improving the accuracy of the calculation results.

[0017] Optionally, the step of marking the corresponding blood relationship includes:

[0018] The blood relationship is stored in the process processor;

[0019] The other process processors request the process processor for the raw data or derived data related to the industrial sensor data connected to the process processor;

[0020] The process processor returns the native data related to the request content and the blood relationship corresponding to the native data according to the request of other process processors; and

[0021] The other process processors update corresponding derived data inside the other process processors according to the returned native data and the blood relationship.

[0022] By adopting the above technical solution, if other process processors request native data or derived data, the native data and the corresponding blood relationship will be returned to allow other process processors to calculate the derived data and evenly calculate the load. At the same time, it can also avoid the process processor being unable to execute the return step because the computing power is locked while waiting for the calculation of other derived data.

[0023] Optionally, the updating step includes: calculating the derived data according to the pre-calculated logic or industrial calculation logic in the native data and the blood relationship; and,

[0024] The request for obtaining the derived data in other process processors is replaced by a request for obtaining the native data related to the derived data.

[0025] By adopting the above technical solution, not only the derivative data is updated, but also the request instructions in other process processors are updated, thereby simplifying the instruction content of subsequent repeated operations and improving the algorithm efficiency of industrial automatic control.

[0026] Optionally, the process processor requests the derivative data from an adjacent process processor;

[0027] If the adjacent process processor has native data and a lineage relationship corresponding to the derived data, then the native data and the lineage relationship corresponding to the derived data are returned;

[0028] If the adjacent process processor only has the blood relationship corresponding to the derived data, the blood relationship corresponding to the derived data is returned, and the process processor requests the native data from the more distant process processor.

[0029] By adopting the above technical solution, the latest blood relationship can be transmitted to more process processors through propagation. After multiple requests, only the original data needs to be transmitted to realize the distributed calculation of derivative data, achieve the balance of computing power, and reduce the probability of locking between different computing logics.

[0030] In the second aspect, the present application provides a data computing and management system based on biological blood relationship, and the technical solution is as follows:

[0031] A data computing and management system based on biological blood relationship includes a plurality of process processors and a plurality of industrial sensors connected to each other by data, wherein the process processor is connected to the industrial sensors by data, and the process processor includes the following modules:

[0032] an acquisition module, electrically connected to the plurality of process processors, for acquiring production data from the industrial sensors, and acquiring industrial calculation logic and pre-calculation logic corresponding to the production data from the process processors, wherein the production data includes directly collected field data and intermediate data obtained by calculating the field data through the pre-calculation logic;

[0033] A classification module, connected to the acquisition module data, is used to classify the production data into primary data and derived data according to the source relationship, the field data is the primary data, and the intermediate data and the result of the industrial calculation logic are both derived data;

[0034] A marking module, which is data-connected to the acquisition module and the classification module, and is used to replace the derived data in the industrial calculation logic with the native data for calculating the derived data and the pre-calculation logic or industrial calculation logic corresponding to the native data, and to mark the corresponding blood relationship between the native data involved in the calculation and the related derived data, wherein the content of the blood relationship includes the corresponding relationship between the native data and the derived data and the pre-calculation logic corresponding to the native data;

[0035] A replacement module, connected to the marking module data, is used to replace the intermediate data with the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data when the derived data is calculated and the latest intermediate data is missing based on the request for calculating the derived data, and then calculate the derived data; and

[0036] The update module is connected to the replacement module data and is used to automatically update the derivative data corresponding to the blood relationship of the native data when the native data is updated.

[0037] By adopting the above technical solution, the data in the industrial automatic control process is marked according to the blood relationship in biology, and the data is divided into native data and derived data and marked. When the derived data operator of the industrial calculation logic is missing or not updated in time, it is replaced with the corresponding native data and its pre-calculation logic according to the blood relationship mark, which effectively solves the problem of falling into an infinite waiting state due to the inability to calculate some data in the automatic control process.

[0038] Optionally, the replacement module further includes:

[0039] an acquisition unit, configured to issue a request for acquiring new native data having a corresponding blood relationship while calculating the derivative data; and

[0040] The calculation unit is connected to the data of the acquisition unit, and is used to recalculate the derived data when new native data with corresponding blood relationship is acquired within a preset time.

[0041] By adopting the above technical solution, the acquisition of native data has a certain time period. If the calculation time of the derived data is extended due to waiting for other data, the time point of obtaining new native data can be reached. At this time, the new native data can be used to recalculate the derived data. At this time, other data has just been waited for, and there is no need to wait again and delay the calculation time, thereby improving the accuracy of the calculation results.

[0042] Optionally, the process processor further includes:

[0043] a storage unit, for storing the blood relationship in the process processor;

[0044] a first request unit, data-connected to the storage unit, for requesting native data or derived data related to the industrial sensor data-connected to other process processors;

[0045] a first feedback unit, data-connected to the first request unit, for returning the native data related to the request content and the blood relationship corresponding to the native data according to the request of the other process processors; and

[0046] An updating unit is connected to the data of the first feedback unit, and is used to update the corresponding internal derivative data according to the returned native data and the blood relationship.

[0047] By adopting the above technical solution, if other process processors request native data or derived data, the native data and the corresponding blood relationship will be returned to allow other process processors to calculate the derived data and evenly calculate the load. At the same time, it can also avoid the process processor being unable to execute the return step because the computing power is locked while waiting for the calculation of other derived data.

[0048] Optionally, the updating unit includes:

[0049] A calculation subunit, configured to calculate the derived data according to the pre-calculation logic or industrial calculation logic in the native data and the blood relationship; and

[0050] A replacement subunit is data-connected to the calculation subunit, and is used to replace a request for obtaining the derived data with a request for obtaining the native data related to the derived data.

[0051] By adopting the above technical solution, not only the derivative data is updated, but also the request instructions in other process processors are updated, thereby simplifying the instruction content of subsequent repeated operations and improving the algorithm efficiency of industrial automatic control.

[0052] Optionally, the process processor further includes:

[0053] A second request unit, configured to request the derivative data from the adjacent process processor;

[0054] A second feedback unit, data-connected to the second request unit, is used to feedback the native data and the blood relationship corresponding to the derived data when there is native data and the blood relationship corresponding to the derived data in the adjacent process processor;

[0055] The third feedback unit is data-connected to the second feedback unit, and is used to feedback the blood relationship corresponding to the derived data when only the blood relationship corresponding to the derived data exists in the adjacent process processor, and to request the native data from the more distant process processor.

[0056] By adopting the above technical solution, the latest blood relationship can be transmitted to more process processors through propagation. After multiple requests, only the original data needs to be transmitted to realize the distributed calculation of derivative data, achieve the balance of computing power, and reduce the probability of locking between different computing logics. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a system block diagram of an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a method flow of an embodiment of the present invention;

[0059] Figure 3 is a block diagram of each module in the process processor according to an embodiment of the present invention;

[0060] Figure 4 FIG. 4 is a block diagram of each unit in a process processor according to an embodiment of the present invention.

[0061] Figure numerals: 100, process processor; 101, storage unit; 102, first request unit; 103, first return unit; 104, update unit; 1041, calculation subunit; 1042, replacement subunit; 105, second request unit; 106, second return unit; 107, third return unit; 110, acquisition module; 120, classification module; 130, marking module; 140, replacement module; 141, acquisition unit; 142, calculation unit; 150, update module; 200, industrial sensor; 300, management background. DETAILED DESCRIPTION

[0062] The following is combined with Figure 1-4 This application is described in further detail.

[0063] Example:

[0064] A data computation management method based on biological kinship, such as Figure 1 As shown, based on multiple industrial sensors 200 and multiple process processors 100 that are data-connected to each other, the process processor 100 is data-connected to the industrial sensors 200. The method is applied in a process workshop, which has multiple process steps, each process step corresponds to a process processor 100, and different processes correspond to different process processors 100. The process processor 100 can use a single-chip microcomputer, PLC or smart phone terminal, and the process processor 100 can be connected to the management background 300 through a wireless module or a field bus to receive control instructions or data from the management background 300 and also send data to the management background 300. The industrial sensor 200 may include sensors such as temperature sensors, humidity sensors, and material sensors. The process processor 100 is also connected to multiple actuators, and the actuators may include common process equipment such as discharge equipment, weighing equipment, stirring equipment, and discharge equipment.

[0065] like Figure 2 As shown, the method comprises the following steps:

[0066] The process processor 100 obtains production data from the industrial sensor 200. The production data may include data containing remaining materials or data on material consumption within a set time period. The management backend 300 obtains the industrial calculation logic and pre-calculation logic corresponding to the production data from the process processor 100; the process calculation logic may be a calculation formula for calculating the amount of material consumed per unit time, or a calculation formula for calculating the material consumed per unit area, or a calculation formula for calculating other required parameters; the pre-calculation logic may be a formula for unit conversion of material data or a calculation formula for converting data into required parameters. The production data includes directly collected field data and intermediate data obtained by calculating the field data through the pre-calculation logic. The field data may include data read by the production sensor. The field data may be a string of outputs. The pre-calculation logic needs to calculate intermediate data such as [32% alkali X series class production, 0], [32% alkali X series class production, -8], etc. [32% alkali X series class production, 0] is the current 32% alkali X series class production, and [32% alkali X series class production, -8] is the 32% alkali X series class production 8 hours ago. The result of the industrial calculation logic may include [32% alkali X series shift output], [32% alkali X series shift output] is the output of an 8-hour shift, and the content of the industrial calculation logic may include: [32% alkali X series shift output] = [32% alkali X series shift output, 0] - [32% alkali X series shift output, -8].

[0067] According to the source relationship, production data is divided into native data and derived data. Field data is native data, and intermediate data and the results of industrial calculation logic are derived data. The formula of industrial calculation logic can include derived data.

[0068] The derivative data in the industrial calculation logic is replaced with the native data and pre-calculation logic that calculate the derivative data, and the corresponding blood relationship is marked on the native data and related derivative data involved in the calculation. The content of the blood relationship includes the correspondence between the native data and the derivative data, and the pre-calculation logic or industrial calculation logic corresponding to the native data. The content of the blood relationship may also include the correspondence between the native data and the derivative data, and the pre-calculation logic and industrial calculation logic corresponding to the native data. The blood relationship is stored in the process processor 100, and the blood relationship is input into the process processor 100 of the corresponding process by the technicians of the management workshop.

[0069] The process processor 100 requests the derived data from the adjacent process processor 100. If there is native data and blood relationship corresponding to the derived data in the adjacent process processor 100, the native data and blood relationship corresponding to the derived data are returned. The complete process production line has multiple different processes and process workshops, each process corresponds to a process processor 100, and when the current process sensor involves the parameters of other processes during operation, it will obtain the required parameters from the process processor 100 of other processes, that is, other process processors 100 will request the process processor 100 for the native data or derived data related to the industrial sensor 200 connected to the data of the process processor 100. The current process processor 100 returns the native data related to the request content and the blood relationship corresponding to the native data according to the request of other process processors 100, and other process processors 100 update the corresponding derivative data inside other process processors 100 according to the returned native data and blood relationship. The technician only needs to input the blood relationship in one process processor 100. As the program runs, the corresponding blood relationship will appear in other process processors 100 that are related to the blood relationship; so that other process processors 100 can also calculate the derived data, without causing a large number of process processors 100 to fall into an infinite loop state with high load when the calculation logic layer of the derived data is confused. If multiple process processors 100 request derived data, the load can be evenly calculated to avoid the requested process processor 100 affecting the current process processor 100 due to too much logic for calculating derived data. At the same time, it can also avoid the process processor 100 waiting for the calculation of other derived data to lock the computing power and fail to execute the return step. If there is only the blood relationship corresponding to the derived data in the adjacent process processor 100, but no latest native data, the blood relationship corresponding to the derived data is returned, and the process processor 100 requests the native data from the more distant process processor 100. The latest blood relationship is transmitted to more process processors 100 by propagation. After multiple requests, only the native data needs to be transmitted to realize the distributed calculation of the derived data, realize the balance of computing power, and reduce the probability of locking between different computing logics.

[0070] The instructions in the process processor 100 send out a request to calculate the derived data. When the process processor 100 calculates the derived data, it sends out a request to obtain new native data with corresponding blood relationship. If the new native data with corresponding blood relationship is obtained within the preset time, the derived data is calculated twice. The acquisition of native data has a certain time period. If the calculation time is extended due to waiting for other data during the calculation of the derived data, there is a probability that the time point when the new native data is obtained can be reached. At this time, the new native data can be used to calculate the derived data twice. At this time, there is a probability that other data has just been waited for, so that there is no need to wait again and delay the calculation time, thereby improving the accuracy of the calculation result. For example, P and G are two process processors 100 with the same internal blood relationship. The industrial sensor 200 connected to P and G has a sampling period of 500 milliseconds for native data. It takes 30 milliseconds to calculate the derived data within P, but it takes 300 milliseconds to wait for other required parameters during the process of P calculating the derived data. In the process of P calculating, new native data is requested from G, and the transmission of new native data takes 20 milliseconds. If P can obtain the latest native data from G during the entire process of calculating the derived data, the new native data can be used to recalculate the derived data, so that the time for obtaining the latest native data overlaps with the time P waits for other required parameters, and the calculated data can also maintain accuracy.

[0071] If a process processor 100 is missing the latest intermediate data when calculating derived data, the process processor 100 can replace the intermediate data with the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data, or replace it with the pre-calculation logic and industrial calculation logic corresponding to the native data. If the process processor 100 does not have the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data, it will request the required data from the adjacent process processor 100, and then calculate the derived data after obtaining the required data sent back.

[0072] If the native data in the process processor 100 is updated, the process processor 100 automatically updates the derivative data corresponding to the blood relationship of the native data. The updating step includes: the current process processor 100 calculates the derivative data according to the pre-calculated logic in the blood relationship between the native data, and the new derivative data is used for the calculation of the current process processor 100. The current process processor 100 replaces the request for obtaining the derivative data in other process processors 100 with the request for obtaining the native data related to the derivative data, and uses the replacement request to change the returned data, without requiring the requested process processor 100 to calculate the logic that is not currently needed by the requested process processor 100 multiple times; if there is new native data to calculate the derivative data, not only the derivative data is updated, but also the request instructions in other process processors 100 are updated, thereby simplifying the instruction content of subsequent repeated operations and improving the algorithm efficiency of industrial automatic control.

[0073] The effects of this embodiment include: dividing the data of the process processor 100 and the industrial sensor 200 in the process production line into native data and derived data, marking them, and then combining the industrial calculation logic and the pre-calculation logic to form a blood relationship. When the derivative data operator of the industrial calculation logic is missing or not updated in time, the missing data is replaced with the corresponding native data and its pre-calculation logic or intermediate data according to the blood relationship mark, which helps to solve the problem of falling into an infinite waiting state due to the inability to calculate some data during the automatic control process on the process production line, or the problem of falling into an infinite waiting state due to the inability to calculate some data in the process processor 100 due to the artificial setting of the wrong calculation level. At the same time, the process processor 100 does not need to update all data when updating the on-site data. The process processor 100 only needs to update the derived data with a blood relationship, saving the computing resources of the process processor 100.

[0074] Application Case 1:

[0075] Calculate the index [Ferric chloride consumption per shift], one shift is 8 hours;

[0076] Calculation formula: Ferric chloride consumption per shift = [Ferric chloride consumption per shift]*0.01 / [32% alkali output per 100 shifts]*1000;

[0077] [32% alkali Y series production, 0] represents the current time; [32% alkali Y series production, -8] represents the current time pushed forward 8 hours.

[0078] The original hierarchical calculation logic:

[0079] First level (raw data):

[0080] [32% alkali X series class yield, 0]; [32% alkali X series class yield, -8];

[0081] [32% alkali Y series class yield, -8]; [32% alkali X series class yield, -8];

[0082] [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0083] [Ferric chloride consumption table code FT_0104BZ, 0]; [Ferric chloride consumption table code FT_0104BZ, -8];

[0084] [Ferric chloride consumption table code FT_0104AZ, 0]; [Ferric chloride consumption table code FT_0104AZ, -8].

[0085] Second level (intermediate data):

[0086] [32% alkali X series class yield] = [32% alkali X series class yield, 0] - [32% alkali X series class yield, -8];

[0087] [32% alkali Y series class output] = [32% alkali Y series class output, 0] - [32% alkali X series class output, -8];

[0088] [32% alkali finished product concentration shift value] = [1201 tank of sodium hydroxide in HY evaporation tank area, -8].

[0089] Level 3 (intermediate data):

[0090] [Ferric chloride shift consumption] = [Ferric chloride shift consumption table code FT_0104BZ, 0] - [Ferric chloride shift consumption table code FT_0104BZ, -8] + [Ferric chloride shift consumption table code FT_0104AZ, 0] - [Ferric chloride shift consumption table code FT_0104AZ, -8].

[0091] [32% alkali output per 100 shifts] = [32% alkali X series output in one shift] + [32% alkali Y series output in one shift] + [32% alkali finished product concentration in one shift].

[0092] Level 4 (results data):

[0093] Ferric chloride consumption per shift = [ferric chloride consumption per shift]*0.01 / [32% alkali output per hundred shifts]*1000.

[0094] The problem with the above calculation process is that each calculation needs to be performed at a level, and when a level cannot be calculated, the total result will be wrong. If there are changes in indicators during the above calculation process, the algorithm needs to recalculate all indicators and cannot determine which indicators to update accurately.

[0095] The following algorithm is modified according to blood relationship.

[0096] Label all metrics as either native or derived.

[0097] The original data is:

[0098] [32% alkali X series class yield, 0]; [32% alkali X series class yield, -8];

[0099] [32% alkali Y series class yield, -8]; [32% alkali X series class yield, -8];

[0100] [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0101] [Ferric chloride consumption table code FT_0104BZ, 0]; [Ferric chloride consumption table code FT_0104BZ, -8];

[0102] [Ferric chloride consumption table code FT_0104AZ, 0]; [Ferric chloride consumption table code FT_0104AZ, -8];

[0103] Create indicator formulas and classify the original formulas into derived formulas, new native formulas and blood relationships.

[0104] 32% Alkali X Series Class Output:

[0105] Derivative data formula: [32% alkali X series class production, 0]-[32% alkali X series class production, -8];

[0106] Original data formula: [32% alkali X series class yield, 0]-[32% alkali X series class yield, -8];

[0107] Blood relationship: [32% alkali X series class yield, 0], [32% alkali X series class yield, -8];

[0108] 32% Alkali Y Series Class Output:

[0109] Derived data formula: [32% alkali Y series class production, 0]-[32% alkali Y series class production, -8];

[0110] Original data formula: [32% alkali Y series class production, 0]-[32% alkali Y series class production, -8];

[0111] Blood relationship: [32% alkali Y series class output, 0], [32% alkali Y series class output, -8]; 32% alkali finished product concentration class value:

[0112] Derived data formula: [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0113] Original data formula: [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0114] Blood relationship: [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0115] Ferrous chloride consumption:

[0116] Derived data formula: [Ferric chloride shift consumption table code FT_0104BZ, 0]-[Ferric chloride shift consumption table code FT_0104BZ, -8]+[Ferric chloride shift consumption table code FT_0104AZ, 0]-[Ferric chloride shift consumption table code FT_0104AZ, -8].

[0117] Original data formula: [Ferric chloride shift consumption table code FT_0104BZ, 0]-[Ferric chloride shift consumption table code FT_0104BZ, -8]+[Ferric chloride shift consumption table code FT_0104AZ, 0]-[Ferric chloride shift consumption table code FT_0104AZ, -8];

[0118] Blood relations include: [ferric chloride shift consumption table code FT_0104BZ, 0], [ferric chloride shift consumption table code FT_0104BZ, -8], [ferric chloride shift consumption table code FT_0104AZ, 0], [ferric chloride shift consumption table code FT_0104AZ, -8];

[0119] 32% alkali output per hundred shifts:

[0120] Derived data formula: [32% alkali X series shift output] + [32% alkali Y series shift output] + [32% alkali finished product concentration shift value].

[0121] Original data formula: ([32% alkali X series production, 0]-[32% alkali X series production, -8]) + ([32% alkali Y series production, 0]-[32% alkali Y series production, -8]) + [1201 tank of sodium hydroxide in HY evaporation tank area, -8];

[0122] The blood relations include: [32% alkali X series class output, 0], [32% alkali X series class output, -8], [32% alkali Y series class output, 0], [32% alkali Y series class output, -8], [HY evaporation tank area 1201 tank of sodium hydroxide, -8];

[0123] Ferric chloride consumption per shift:

[0124] Derived data formula: [Ferric chloride consumption per shift]*0.01 / [32% alkali output per hundred shifts]*1000.

[0125] Original data formula: ([Ferric chloride shift consumption table code FT_0104BZ, 0]-[Ferric chloride shift consumption table code FT_0104BZ, -8]+[Ferric chloride shift consumption table code FT_0104AZ, 0]-[Ferric chloride shift consumption table code FT_0104AZ, -8])*0.01 / (([32% alkali X series shift production, 0]-[32% alkali X series shift production, -8])+([32% alkali Y series shift production, 0]-[32% alkali Y series shift production, -8])+[1201 tank of sodium hydroxide in HY evaporation tank area, -8]);

[0126] The blood relationship includes: [32% alkali X series production, 0], [32% alkali X series production, -8], [32% alkali Y series production, 0], [32% alkali Y series production, -8], [HY evaporation tank area 1201 tank of sodium hydroxide, -8], [ferric chloride shift consumption table code FT_0104BZ, 0], [ferric chloride shift consumption table code FT_0104BZ, -8], [ferric chloride shift consumption table code FT_0104AZ, 0], [ferric chloride shift consumption table code FT_0104AZ, -8];

[0127] When calculating [Ferric chloride shift consumption], first calculate the derived formula, and check whether the two indicators [Ferric chloride shift consumption] and [32% alkali per 100-shift output] have results. If there are results, directly calculate the value and the calculation ends. If one of the indicators [Ferric chloride shift consumption] and [32% alkali per 100-shift output] has no value, directly calculate the native formula.

[0128] Case 2:

[0129] In the on-site process automation control system, there is a probability that communication failure will cause the [HYX series 270 sodium hydroxide] indicator value to be incorrect, requiring manual modification.

[0130] [HYX Series 270 Sodium Hydroxide] After the indicator value is modified, the dependent indicators need to be recalculated. According to the previous logic, it can only be found one level up, and the recursion and calculation amount are very huge. Moreover, it is not certain whether the dependent indicators need to be changed, and it is unpredictable whether there is a deadlock. After introducing the blood relationship, the following transformation is made:

[0131] The [HYX series 270 sodium hydroxide] indicator is marked as native data.

[0132] [X series 270-32 alkali concentration (sodium hydroxide) - average];

[0133] Derivative formula: @avg (HYX is 270 sodium hydroxide);

[0134] Original formula: @avg(HYX is 270 sodium hydroxide);

[0135] Blood relations include: HYX series 270 sodium hydroxide;

[0136] [X series yield formula - (assay)];

[0137] Derivative formula: [X series electrolytic cell alkali flow rate - difference] * 1.305;

[0138] Original formula: [X series electrolytic cell alkali flow rate - difference] * 1.305;

[0139] Blood relations include: [X series electrolytic cell alkali output flow rate - difference];

[0140] [X is the percent yield formula - (assay)];

[0141] Derivative formula: [X series yield formula - (assay)] * [X series 270-32 alkali concentration (sodium hydroxide) - average] / 100;

[0142] Original formula: [X series electrolytic cell alkali flow rate - difference]*1.305*@avg(HYX series 270 sodium hydroxide) / 100;

[0143] Blood relations include: HYX series 270 sodium hydroxide, [X series electrolytic cell alkali flow rate - difference];

[0144] Calculation: Due to the change of [HYX Series 270 Sodium Hydroxide], all indicators of [HYX Series 270 Sodium Hydroxide] are directly calculated according to the original data formula. Because the result value can be calculated once, there is no recursion and no mutual dependence, batch processing technology can be introduced to improve production efficiency.

[0145] This embodiment also discloses a data calculation management system based on biological blood relationship, such as Figure 3 and Figure 4 As shown, it includes a plurality of industrial sensors 200 and a plurality of process processors 100 which are data-connected to each other. The process processor 100 is data-connected to the industrial sensors 200. The process processor 100 includes the following modules:

[0146] The acquisition module 110 is electrically connected to multiple process processors 100, and is used to acquire production data from the industrial sensor 200, and acquire industrial calculation logic and pre-calculation logic corresponding to the production data from the process processor 100. The production data includes directly collected field data and intermediate data obtained by calculating the field data through pre-calculation logic.

[0147] The classification module 120 is connected to the data of the acquisition module 110, and is used to classify the production data into primary data and derived data according to the source relationship. The field data is the primary data, and the intermediate data and the results of the industrial calculation logic are all derived data.

[0148] The marking module 130 is data-connected to the acquisition module 110 and the classification module 120, and is used to replace the derived data in the industrial calculation logic with the native data for calculating the derived data and the pre-calculation logic or industrial calculation logic corresponding to the native data, and to mark the corresponding blood relationship between the native data involved in the calculation and the related derived data. The content of the blood relationship includes the correspondence between the native data and the derived data and the pre-calculation logic corresponding to the native data.

[0149] The replacement module 140 is data-connected to the marking module 130, and is used to replace the intermediate data with the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data based on the request for calculating the derived data and when the latest intermediate data is missing, and then calculate the derived data. The replacement module 140 also includes: an acquisition unit 141, which is used to issue a request to obtain new native data with corresponding blood relationship while calculating the derived data. The calculation unit 142 is data-connected to the acquisition unit 141, and is used to recalculate the derived data when the new native data with corresponding blood relationship is obtained within a preset time.

[0150] The update module 150 is data-connected to the replacement module 140 and is used to automatically update the derivative data corresponding to the lineage relationship of the original data when the original data is updated.

[0151] The process processor 100 further includes:

[0152] The storage unit 101 is used to store the blood relationship in the process processor 100;

[0153] A first request unit 102 , data-connected to the storage unit 101 , for requesting native data or derived data related to an industrial sensor 200 data-connected to other process processors 100 ;

[0154] The first return unit 103 is data-connected to the first request unit 102 and is used to return the native data related to the request content and the blood relationship corresponding to the native data according to the request of other process processors 100; and

[0155] The updating unit 104 is data-connected to the first feedback unit 103, and is used to update the corresponding internal derivative data according to the returned native data and the blood relationship. The updating unit 104 includes: a computing subunit 1041, which is used to calculate the derivative data according to the pre-calculation logic or industrial calculation logic in the native data and the blood relationship. A substitution subunit 1042 is data-connected to the computing subunit 1041, and is used to replace the request for obtaining the derivative data with the request for obtaining the native data related to the derivative data.

[0156] The process processor 100 further includes:

[0157] A second request unit 105, configured to request derivative data from an adjacent process processor 100;

[0158] The second feedback unit 106 is data-connected to the second request unit 105 and is used to feedback the native data and the blood relationship corresponding to the derived data when there are native data and the blood relationship corresponding to the derived data in the adjacent process processor 100;

[0159] The third feedback unit 107 is data-connected to the second feedback unit 106 , and is used to feedback the blood relationship corresponding to the derived data when there is only a blood relationship corresponding to the derived data in the adjacent process processor 100 , and request native data from the more distant process processor 100 .

[0160] The embodiments of this specific implementation method are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data calculation management method based on biological kinship, based on a plurality of industrial sensors (200) and a plurality of process processors (100) that are data-connected to each other, wherein the process processors (100) are data-connected to the industrial sensors (200), characterized in that: The method comprises the following steps: Acquiring production data from the industrial sensor (200), and acquiring industrial calculation logic and pre-calculation logic corresponding to the production data from the process processor (100), the production data comprising directly collected field data and intermediate data obtained by calculating the field data through the pre-calculation logic; The production data is divided into primary data and derived data according to the source relationship, the field data is the primary data, and the intermediate data and the result of the industrial calculation logic are both derived data; The derivative data in the industrial calculation logic is replaced with the native data for calculating the derivative data and the pre-calculation logic or industrial calculation logic corresponding to the native data, and the native data involved in the calculation and the related derivative data are marked with corresponding blood relationships, and the content of the blood relationship includes the corresponding relationship between the native data and the derivative data and the pre-calculation logic or industrial calculation logic corresponding to the native data; Based on the request to calculate the derived data, if the latest intermediate data is missing when calculating the derived data, the intermediate data is replaced with the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data, and then the derived data is calculated; as well as, If the native data is updated, the derived data corresponding to the blood relationship of the native data is automatically updated.

2. A method for data calculation and management based on biological blood relationship according to claim 1, characterized in that: The step of calculating the derivative data also includes: while calculating the derived data, issuing a request to obtain new native data having a corresponding blood relationship; and, If new native data with corresponding blood relationship is obtained within a preset time, the derived data is calculated for a second time.

3. The method for data calculation and management based on biological blood relationship according to claim 1, characterized in that: The step of marking the corresponding blood relationship comprises: The blood relationship is stored in the process processor (100); The other process processors (100) request the process processor (100) for raw data or derived data related to the industrial sensor (200) data-connected to the process processor (100); The process processor (100) returns the native data related to the request content and the blood relationship corresponding to the native data according to the request of other process processors (100); and The other process processors (100) update corresponding derivative data inside the other process processors (100) according to the returned native data and the blood relationship.

4. The method for data calculation and management based on biological blood relationship according to claim 3, characterized in that: The updating step includes: calculating the derived data according to the pre-calculated logic or industrial calculation logic in the blood relationship between the native data; and, The request for obtaining the derived data in other process processors (100) is replaced by a request for obtaining the native data related to the derived data.

5. The method for data calculation and management based on biological blood relationship according to claim 1, characterized in that: The process processor (100) requests the derivative data from an adjacent process processor (100); If the adjacent process processor (100) has native data and a blood relationship corresponding to the derived data, then returning the native data and the blood relationship corresponding to the derived data; If the adjacent process processor (100) only has the blood relationship corresponding to the derived data, the blood relationship corresponding to the derived data is returned, and the process processor (100) requests the native data from the more distant process processor (100).

6. A data computing and management system based on biological blood relationship, characterized by: The invention comprises a plurality of industrial sensors (200) and a plurality of process processors (100) which are data-connected to each other, wherein the process processor (100) is data-connected to the industrial sensors (200), and the process processor (100) comprises the following modules: an acquisition module (110), electrically connected to the plurality of process processors (100), and configured to acquire production data from the industrial sensor (200), and to acquire industrial calculation logic and pre-calculation logic corresponding to the production data from the process processor (100), wherein the production data includes directly collected field data and intermediate data obtained by calculating the field data through the pre-calculation logic; A classification module (120), connected to the acquisition module (110), is used to classify the production data into primary data and derived data according to the source relationship, the field data being the primary data, and the intermediate data and the result of the industrial calculation logic being the derived data; A marking module (130) is data-connected to the acquisition module (110) and the classification module (120), and is used to replace the derived data in the industrial calculation logic with the native data for calculating the derived data and the pre-calculation logic or industrial calculation logic corresponding to the native data, and to mark the corresponding blood relationship between the native data and the related derived data involved in the calculation, wherein the content of the blood relationship includes the corresponding relationship between the native data and the derived data and the pre-calculation logic or industrial calculation logic corresponding to the native data; A replacement module (140), connected to the marking module (130), is used to replace the intermediate data with the native data corresponding to the blood relationship and the pre-calculation logic or industrial calculation logic corresponding to the native data when the derivative data is calculated and the latest intermediate data is missing based on the request for calculating the derivative data, and then calculate the derivative data; as well as, The update module (150) is data-connected to the replacement module (140) and is used to automatically update the derivative data corresponding to the blood relationship of the native data when the native data is updated.

7. The data calculation and management system based on biological blood relationship according to claim 6, characterized in that: The replacement module (140) further includes: An acquisition unit (141) is used to issue a request for acquiring new native data having a corresponding blood relationship while calculating the derivative data; and The calculation unit (142) is data-connected to the acquisition unit (141) and is used to recalculate the derived data when new native data with corresponding blood relationship is acquired within a preset time.

8. The data calculation and management system based on biological blood relationship according to claim 6, characterized in that: The process processor (100) further comprises: A storage unit (101), used for storing the blood relationship in the process processor (100); A first request unit (102), data-connected to the storage unit (101), for requesting raw data or derived data related to the industrial sensor (200) data-connected to other process processors (100); A first feedback unit (103) is data-connected to the first request unit (102), and is used to feedback the native data related to the request content and the blood relationship corresponding to the native data according to the request of other process processors (100); and The updating unit (104) is data-connected to the first feedback unit (103) and is used to update the corresponding internal derivative data according to the returned native data and the blood relationship.

9. The data calculation and management system based on biological blood relationship according to claim 8, characterized in that: The updating unit (104) comprises: A calculation subunit (1041), configured to calculate the derived data according to the pre-calculation logic or industrial calculation logic in the native data and the blood relationship; and The replacement subunit (1042) is data-connected to the calculation subunit (1041), and is used to replace the request for obtaining the derived data with a request for obtaining the native data related to the derived data.

10. The data calculation and management system based on biological blood relationship according to claim 6, characterized in that: The process processor (100) further comprises: A second request unit (105), configured to request the derivative data from the adjacent process processor (100); A second feedback unit (106) is data-connected to the second request unit (105), and is used to feedback the native data and the blood relationship corresponding to the derived data when there is native data and the blood relationship corresponding to the derived data in the adjacent process processor (100); The third feedback unit (107) is data-connected to the second feedback unit (106) and is used to feedback the blood relationship corresponding to the derived data when only the blood relationship corresponding to the derived data exists in the adjacent process processor (100), and to request the native data from the more distant process processor (100).

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