CN117194732A - Industrial Internet trusted data communication method and system - Google Patents
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Abstract
The invention relates to the technical field of data transmission optimization, and particularly discloses an industrial Internet trusted data communication method and system, wherein the method comprises the steps of inquiring a participation table of a production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue; performing time sequence matching on the data queues, and creating a data graph at each moment based on the data queues after time sequence matching; comparing the data graphs, and determining a node data graph according to the data graphs; and counting a node data graph, and determining data to be uploaded based on the node data graph. The invention establishes the data queue connected with all the equipment and staff, obtains the port data through the data queue, creates the data graph according to the port data, then compares the plurality of data graphs by means of the existing image comparison technology, determines the node data graph, and takes the node data graph as the center to obtain the change characteristics of other data graphs, thereby simplifying the original large amount of data and relieving the data transmission pressure.
Description
Technical Field
The invention relates to the technical field of data transmission optimization, in particular to an industrial Internet trusted data communication method and system.
Background
The industrial Internet is a brand new industrial production mode simply, various data and information of industries and enterprises are processed and analyzed through a platform by the aid of comprehensive interconnection of people, machines and objects, and one person or a plurality of persons can manage production, operation, purchase, sales and even research and development designs of the whole factory, so that the industrial production mode is an industrial mode for turning over the traditional manufacturing mode and the production organization mode, and the industrial development direction in the future is also realized.
In the management process of the industrial Internet, the generated data volume is extremely large, most of the data are data generated in a normal working state, and although the data have certain analysis value, the analysis value is not high, the cost of manpower and material resources required for acquiring the data is high, and the technical problem that how to select the data with the analysis value and transmit the data to a quality inspection end is solved by the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide an industrial Internet trusted data communication method and system for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of industrial internet trusted data communication, the method comprising:
inquiring a participation table of a production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue;
performing time sequence matching on the data queues, and creating a data graph at each moment based on the data queues after time sequence matching;
comparing the data graphs, and determining a node data graph according to the data graphs;
and counting a node data graph, and determining data to be uploaded based on the node data graph.
As a further scheme of the invention: the step of inquiring the participation table of the production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue comprises the following steps:
inquiring demand equipment of a production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
acquiring the relative position relation between the equipment end and the staff end, and creating a data queue set according to the relative position relation;
establishing a connection channel between a device end and an employee end corresponding to a data queue, and acquiring port data in real time; the port data contains a time tag;
the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
As a further scheme of the invention: the step of performing time sequence matching on the data queues and creating the data graph at each moment based on the data queues after time sequence matching comprises the following steps:
counting the data quantity of each data queue in the data queue set, and calculating the average data quantity;
calculating the difference value between the data quantity of each data queue and the average data quantity, and adjusting the data acquisition frequency of each data queue according to the difference value;
reading port data from each data queue according to time, and inputting a preset dimension conversion model to obtain three-dimensional data;
converting the three-dimensional data into image data according to a preset data arrangement rule;
and counting all image data based on the relative position relation of the data queue set to obtain a data graph.
As a further scheme of the invention: the step of comparing the data graphs and determining the node data graphs according to the data graphs comprises the following steps:
sequencing the data graphs according to time, and extracting the data graphs according to a preset time extraction rule;
sequentially calculating the similarity of each data graph and other data graphs to obtain a similarity vector;
and selecting the data graph according to the similarity vector as a node data graph.
As a further scheme of the invention: the step of sequentially calculating the similarity of each data graph and other data graphs to obtain a similarity vector comprises the following steps:
determining the segmentation density of the data graph according to the performance parameters of the classifier;
sequentially determining each data graph as a first data graph, and sequentially reading other data graphs as a second data graph after the first data graph is determined;
inputting the first data graph and the second data graph into a preset comparison model containing segmentation density to obtain similarity;
and counting the similarity of all the second data graphs corresponding to the first data graph to obtain a similarity vector.
As a further scheme of the invention: the comparison model is as follows:
；
wherein m and n are the unit sizes for determining the segmentation density respectively;is the mean value of the data in a certain unit in the first data graph; />Is the mean value of the data in a certain unit in the second data graph; x and y are the center points of a certain cell; />The symbol is->In shorthand form; />Is the value of a certain cell midpoint (x+i, y+j) in the first data diagram; />Is the value of a point (x+i, y+j) in a cell in the second data map; NCC is the similarity of two corresponding units.
As a further scheme of the invention: the step of selecting the data graph according to the similarity vector as the node data graph comprises the following steps:
marking similarity in a similarity vector according to a preset similarity threshold value, and synchronously calculating the number of marks;
when the number of marks reaches a preset number threshold, reading a first data graph corresponding to the similarity vector and a second data graph corresponding to the similarity of the marks;
taking the read first data graph as a node data graph;
performing logic operation on the read first data graph and the second data graph, determining a difference range, and inserting the second data graph;
the second data graph containing the difference range is not used as the node data graph.
The technical scheme of the invention also provides an industrial Internet trusted data communication system, which comprises:
the data acquisition module is used for inquiring a participation table of the production task, establishing a data queue based on the participation table and acquiring port data based on the data queue;
the data diagram determining module is used for carrying out time sequence matching on the data queues, and creating a data diagram at each moment based on the data queues after the time sequence matching;
the node data determining module is used for comparing the data graphs and determining a node data graph according to the data graphs;
and the data statistics module is used for counting the node data graph and determining the data to be uploaded based on the node data graph.
As a further scheme of the invention: the data acquisition module comprises:
the port creation unit is used for inquiring the demand equipment of the production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
the queue set creation unit is used for acquiring the relative position relation between the equipment end and the employee end and creating a data queue set according to the relative position relation;
the connection establishment unit is used for establishing a connection channel between the equipment end and the employee end corresponding to the data queue and acquiring port data in real time; the port data contains a time tag;
the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
As a further scheme of the invention: the data diagram determining module comprises:
the average value calculation unit is used for counting the data quantity of each data queue in the data queue set and calculating the average value data quantity;
the frequency adjusting unit is used for calculating the difference value between the data quantity of each data queue and the average data quantity and adjusting the data acquisition frequency of each data queue according to the difference value;
the first conversion unit is used for reading port data from each data queue according to time, inputting a preset dimension conversion model and obtaining three-dimensional data;
a second conversion unit for converting the three-dimensional data into image data according to a preset data arrangement rule;
and the data statistics unit is used for counting all image data based on the relative position relation of the data queue set to obtain a data graph.
Compared with the prior art, the invention has the beneficial effects that: the invention establishes the data queue connected with all the equipment and staff, obtains the port data through the data queue, creates the data graph according to the port data, then compares the plurality of data graphs by means of the existing image comparison technology, determines the node data graph, and takes the node data graph as the center to obtain the change characteristics of other data graphs, thereby simplifying the original large amount of data and relieving the data transmission pressure.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a flow diagram of an industrial internet trusted data communication method.
Fig. 2 is a first sub-flowchart of an industrial internet trusted data communication method.
Fig. 3 is a second sub-flowchart block diagram of an industrial internet trusted data communication method.
Fig. 4 is a third sub-flowchart of the industrial internet trusted data communication method.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flow chart of an industrial internet trusted data communication method, and in an embodiment of the invention, the method includes:
step S100: inquiring a participation table of a production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue;
the participation table comprises a data end and a data generation area, wherein the data end is used for representing a port for generating data, the data generation area is used for representing an active area of the port, the area of equipment data is a fixed point in general, and the area of employee data is a range.
The participation table is used for representing equipment and staff required for completing a production task, the equipment and the staff are respectively provided with corresponding data acquisition modules in advance, and the data acquisition modules can acquire data generated in the process of completing the production task in real time and are called port data.
Step S200: performing time sequence matching on the data queues, and creating a data graph at each moment based on the data queues after time sequence matching;
in the process of completing production tasks, different equipment or staff generate different data frequencies, and only data at the same moment can be uniformly analyzed, so that the data acquisition frequencies of all the data queues are required to be ensured to be approximately the same, and the data acquisition frequencies are called time sequence matching; and reading data from the data queues after time sequence matching in turn, and establishing a data graph for reflecting the whole production state.
Step S300: comparing the data graphs, and determining a node data graph according to the data graphs;
the data graphs contain time information, each moment corresponds to one data graph, most of data in a stable production task are repeated data, the similarity of the data graphs is correspondingly high, when the data reach a certain node of the production task, the data change, the corresponding data graphs also change synchronously, and based on the data graphs, the data graphs with large changes can be selected and are called node data graphs.
Step S400: counting a node data graph, and determining data to be uploaded based on the node data graph;
after generating a node data graph, taking the node data graph as a center, acquiring the difference between the data graph and the node data graph at adjacent moments, recording the difference with smaller data quantity between the node data graph and the node data graph, and uploading the difference to a quality inspection terminal; compared with the traditional global uploading scheme, the process greatly reduces the data quantity to be transmitted while ensuring certain data integrity.
FIG. 2 is a first sub-flowchart of an industrial Internet trusted data communication method, wherein the steps of querying a participation table of a production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue include:
step S101: inquiring demand equipment of a production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
under the existing intelligent production background, production tasks are completed by intelligent equipment, wherein the intelligent equipment is the demand equipment in the content; querying a demand employee by taking demand equipment as a center, thereby determining all equipment ends and employee ends; generally, the update frequency of the employee side is higher than that of the device side.
Step S102: acquiring the relative position relation between the equipment end and the staff end, and creating a data queue set according to the relative position relation;
the position relation of the equipment end is fixed, staff corresponds to the equipment end and moves in a certain range, the relative position relation of all ports can be obtained according to the equipment end, and the logic position relation (number) of the data queue is determined according to the relative position relation; it should be noted that, one data queue corresponds to one equipment end or employee end, and in the technical scheme of the present invention, the equipment end and the employee end are regarded as constituent units of the whole workshop, and no specific subdivision is needed.
Step S103: establishing a connection channel between a device end and an employee end corresponding to a data queue, and acquiring port data in real time; the port data contains a time tag;
after the data queue is established, establishing a connection channel between the data queue and the equipment end, and establishing a connection channel between the data queue and the staff end, wherein port data can be obtained based on the connection channel; when acquiring port data, the acquisition time is synchronously recorded.
Specifically, the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
FIG. 3 is a second sub-flowchart of the industrial Internet trusted data communication method, wherein the step of performing time sequence matching on the data queues and creating a data graph at each moment based on the time sequence matched data queues includes:
step S201: counting the data quantity of each data queue in the data queue set, and calculating the average data quantity;
the dequeued data of the data queue is used for generating a data graph, and the processing speed is low, so that the data volume in the data queue is gradually increased; and counting the data quantity in all the data queues, and calculating the average data quantity.
Step S202: calculating the difference value between the data quantity of each data queue and the average data quantity, and adjusting the data acquisition frequency of each data queue according to the difference value;
when the difference is positive, the data amount in the data queue is more, the data acquisition frequency needs to be reduced, and when the difference is negative, the data amount in the data queue is smaller, and the data acquisition frequency needs to be increased; this correction process may cause the data in the various data queues to be in a similar state.
In addition, all data acquisition frequencies are synchronously adjusted according to the average data quantity.
Step S203: reading port data from each data queue according to time, and inputting a preset dimension conversion model to obtain three-dimensional data;
the data structure of different port data is not unique, a plurality of port data are multi-source, and the analysis process is very difficult, so that the data needs to be preprocessed firstly, and the preprocessing mode is to convert the port data into three-dimensional data.
Step S204: converting the three-dimensional data into image data according to a preset data arrangement rule;
the three-dimensional data can be represented by an image, and the coordinates of the image and the values of the pixel points share three dimensions; the process of mapping the three-dimensional data into the image data is a simple data reading process, and the image data can be obtained after the data arrangement rule is determined by the staff.
Step S205: counting all image data based on the relative position relation of the data queue set to obtain a data graph;
and counting all image data, and splicing according to the relative position relation in the content to obtain a total image at the moment, which is called a data image.
FIG. 4 is a third sub-flowchart of an industrial Internet trusted data communication method, wherein the steps of comparing the data graphs and determining a node data graph according to the data graphs include:
step S301: sequencing the data graphs according to time, and extracting the data graphs according to a preset time extraction rule;
the data graphs contain time labels, and the data graphs can be ordered according to the time sequence; for the ordered data graphs, presetting an extraction mode by a worker, and extracting the data graphs; the time extraction rule is typically to extract a data map for a certain period of time.
Step S302: sequentially calculating the similarity of each data graph and other data graphs to obtain a similarity vector;
and sequentially analyzing each data graph, and calculating the similarity between the data graph and all other data graphs to obtain a similarity vector.
Step S303: selecting a data graph according to the similarity vector as a node data graph;
and analyzing the similarity, and judging the importance of each data graph so as to select a certain data graph as a node data graph.
Further, the step of sequentially calculating the similarity of each data graph and other data graphs to obtain a similarity vector includes:
determining the segmentation density of the data graph according to the performance parameters of the classifier;
sequentially determining each data graph as a first data graph, and sequentially reading other data graphs as a second data graph after the first data graph is determined;
inputting the first data graph and the second data graph into a preset comparison model containing segmentation density to obtain similarity;
and counting the similarity of all the second data graphs corresponding to the first data graph to obtain a similarity vector.
The above-mentioned content defines the similarity calculation process, firstly, the performance (performance parameter of classifier) of the arithmetic unit for calculating the similarity is required to be obtained, and the segmentation density is determined according to the performance parameter, so as to segment the data graph into a plurality of data blocks, wherein the smaller the size of the data blocks, the lower the performance requirement is; the total similarity can be fitted through the similarity of the plurality of data blocks; then, through a nested loop model, the similarity between the data graphs can be calculated, and the similarity vector of each data graph is obtained.
Wherein, the comparison model is:
；
wherein m and n are the unit sizes for determining the segmentation density respectively;is the mean value of the data in a certain unit in the first data graph; />Is the mean value of the data in a certain unit in the second data graph; x and y are the center points of a certain cell; />The symbol is->In shorthand form; />Is the value of a certain cell midpoint (x+i, y+j) in the first data diagram; />Is the value of a point (x+i, y+j) in a cell in the second data map; NCC is the similarity of two corresponding units; the similarity of the first data map and the second data map can be calculated through the similarity of all the corresponding units.
In an example of the present invention, the step of selecting the data graph according to the similarity vector as the node data graph includes:
marking similarity in a similarity vector according to a preset similarity threshold value, and synchronously calculating the number of marks;
the similarity vector represents the similarity between a certain data graph and all other data graphs, higher similarity can be marked in the similarity vector according to a preset similarity threshold, and the number of marks is required to be synchronously recorded while the similarity is marked.
When the number of marks reaches a preset number threshold, reading a first data graph corresponding to the similarity vector and a second data graph corresponding to the similarity of the marks;
the number of labels is analyzed to indicate that the number of data graphs sufficiently similar to the data graph (first data graph) is sufficiently large if the number of labels is sufficiently large, and the data graph can be used as a node data graph.
Taking the read first data graph as a node data graph;
performing logic operation on the read first data graph and the second data graph, determining a difference range, and inserting the second data graph;
on the basis of determining the node data graphs, comparing the first data graph and the second data graph (the data graphs corresponding to the marked similarity) with high enough similarity, judging which areas have differences, and marking the differences in the corresponding second data graph; at this time, the original data graph can be converted into a node data graph and a difference range, so that the data volume is greatly reduced.
The second data graph containing the difference range is not used as the node data graph, and the meaning of the second data graph is that if one data graph becomes the second data graph of the other node data graphs (the first data graph), analysis is not performed in the subsequent similarity vector analysis process.
In a preferred embodiment of the present invention, there is provided an industrial internet trusted data communication system, the system comprising:
the data acquisition module is used for inquiring a participation table of the production task, establishing a data queue based on the participation table and acquiring port data based on the data queue;
the data diagram determining module is used for carrying out time sequence matching on the data queues, and creating a data diagram at each moment based on the data queues after the time sequence matching;
the node data determining module is used for comparing the data graphs and determining a node data graph according to the data graphs;
and the data statistics module is used for counting the node data graph and determining the data to be uploaded based on the node data graph.
Further, the data acquisition module includes:
the port creation unit is used for inquiring the demand equipment of the production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
the queue set creation unit is used for acquiring the relative position relation between the equipment end and the employee end and creating a data queue set according to the relative position relation;
the connection establishment unit is used for establishing a connection channel between the equipment end and the employee end corresponding to the data queue and acquiring port data in real time; the port data contains a time tag;
the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
Specifically, the data map determining module includes:
the average value calculation unit is used for counting the data quantity of each data queue in the data queue set and calculating the average value data quantity;
the frequency adjusting unit is used for calculating the difference value between the data quantity of each data queue and the average data quantity and adjusting the data acquisition frequency of each data queue according to the difference value;
the first conversion unit is used for reading port data from each data queue according to time, inputting a preset dimension conversion model and obtaining three-dimensional data;
a second conversion unit for converting the three-dimensional data into image data according to a preset data arrangement rule;
and the data statistics unit is used for counting all image data based on the relative position relation of the data queue set to obtain a data graph.
The functions that can be achieved by the industrial internet trusted data communication method are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the industrial internet trusted data communication method.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the computer device is merely exemplary and is not intended to be limiting, and that more or fewer components than the foregoing description may be included, or certain components may be combined, or different components may be included, for example, input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms include or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method of industrial internet trusted data communication, the method comprising:
inquiring a participation table of a production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue;
performing time sequence matching on the data queues, and creating a data graph at each moment based on the data queues after time sequence matching;
comparing the data graphs, and determining a node data graph according to the data graphs;
and counting a node data graph, and determining data to be uploaded based on the node data graph.
2. The method of claim 1, wherein the step of querying a participation table for the production task, establishing a data queue based on the participation table, and acquiring port data based on the data queue comprises:
inquiring demand equipment of a production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
acquiring the relative position relation between the equipment end and the staff end, and creating a data queue set according to the relative position relation;
establishing a connection channel between a device end and an employee end corresponding to a data queue, and acquiring port data in real time; the port data contains a time tag;
the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
3. The method of claim 1, wherein the step of performing time sequence matching on the data queues and creating the data map for each time based on the time sequence matched data queues comprises:
counting the data quantity of each data queue in the data queue set, and calculating the average data quantity;
calculating the difference value between the data quantity of each data queue and the average data quantity, and adjusting the data acquisition frequency of each data queue according to the difference value;
reading port data from each data queue according to time, and inputting a preset dimension conversion model to obtain three-dimensional data;
converting the three-dimensional data into image data according to a preset data arrangement rule;
and counting all image data based on the relative position relation of the data queue set to obtain a data graph.
4. The method of claim 1, wherein the step of comparing the data graphs and determining a node data graph from the data graph comprises:
sequencing the data graphs according to time, and extracting the data graphs according to a preset time extraction rule;
sequentially calculating the similarity of each data graph and other data graphs to obtain a similarity vector;
and selecting the data graph according to the similarity vector as a node data graph.
5. The method for industrial internet trusted data communication of claim 4, wherein said step of sequentially calculating the similarity of each data graph to the other data graphs to obtain a similarity vector comprises:
determining the segmentation density of the data graph according to the performance parameters of the classifier;
sequentially determining each data graph as a first data graph, and sequentially reading other data graphs as a second data graph after the first data graph is determined;
inputting the first data graph and the second data graph into a preset comparison model containing segmentation density to obtain similarity;
and counting the similarity of all the second data graphs corresponding to the first data graph to obtain a similarity vector.
6. The industrial internet trusted data communication method of claim 5, wherein said comparison model is:
；
wherein m and n are the unit sizes for determining the segmentation density respectively;is the mean value of the data in a certain unit in the first data graph; />Is the mean value of the data in a certain unit in the second data graph; x and y are the center points of a certain cell; />The symbol is->In shorthand form; />Is the value of a certain cell midpoint (x+i, y+j) in the first data diagram; />Is the value of a point (x+i, y+j) in a cell in the second data map; NCC is the similarity of two corresponding units.
7. The method of claim 5, wherein the step of selecting the data graph as the node data graph according to the similarity vector comprises:
marking similarity in a similarity vector according to a preset similarity threshold value, and synchronously calculating the number of marks;
when the number of marks reaches a preset number threshold, reading a first data graph corresponding to the similarity vector and a second data graph corresponding to the similarity of the marks;
taking the read first data graph as a node data graph;
performing logic operation on the read first data graph and the second data graph, determining a difference range, and inserting the second data graph;
the second data graph containing the difference range is not used as the node data graph.
8. An industrial internet trusted data communication system, the system comprising:
the data acquisition module is used for inquiring a participation table of the production task, establishing a data queue based on the participation table and acquiring port data based on the data queue;
the data diagram determining module is used for carrying out time sequence matching on the data queues, and creating a data diagram at each moment based on the data queues after the time sequence matching;
the node data determining module is used for comparing the data graphs and determining a node data graph according to the data graphs;
and the data statistics module is used for counting the node data graph and determining the data to be uploaded based on the node data graph.
9. The industrial internet trusted data communication system of claim 8, wherein said data acquisition module comprises:
the port creation unit is used for inquiring the demand equipment of the production task, determining demand staff according to the demand equipment, and creating an equipment end and a staff end according to the demand equipment and the demand staff;
the queue set creation unit is used for acquiring the relative position relation between the equipment end and the employee end and creating a data queue set according to the relative position relation;
the connection establishment unit is used for establishing a connection channel between the equipment end and the employee end corresponding to the data queue and acquiring port data in real time; the port data contains a time tag;
the port data of the equipment end is data generated by the electronic equipment in the equipment end, and the port data of the staff end comprises position data and input data of the staff.
10. The industrial internet trusted data communication system of claim 8, wherein said data diagram determination module comprises:
the average value calculation unit is used for counting the data quantity of each data queue in the data queue set and calculating the average value data quantity;
the frequency adjusting unit is used for calculating the difference value between the data quantity of each data queue and the average data quantity and adjusting the data acquisition frequency of each data queue according to the difference value;
the first conversion unit is used for reading port data from each data queue according to time, inputting a preset dimension conversion model and obtaining three-dimensional data;
a second conversion unit for converting the three-dimensional data into image data according to a preset data arrangement rule;
and the data statistics unit is used for counting all image data based on the relative position relation of the data queue set to obtain a data graph.
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