Quality supervision and evaluation method and system based on metal material

Abstract

The invention discloses an evaluation method and system based on metal material quality supervision, and relates to the technical field of metal material quality evaluation, and the method comprises the steps: extracting a target block, adjacent blocks 1, 2, 3 and 4 from a block set obtained through segmentation of a target metal material; performing independence analysis on relevance influence between the adjacent blocks with the detection item data difference in the four adjacent blocks and the target block under the condition that the positions of the adjacent blocks are not adjacent, and extracting a first prediction block and a second prediction block under the condition that the positions of the adjacent blocks are adjacent, and extracting the first prediction block and the second prediction block according to the first prediction block and the second prediction block; and counting fourth to-be-judged data and fifth to-be-judged data, judging whether the quality of the target block is qualified or not according to the fourth to-be-judged data and the fifth to-be-judged data, and outputting a quality qualification evaluation result or a quality disqualification evaluation result. According to the metal material quality supervision and evaluation method and system provided by the invention, the accuracy of metal material quality evaluation work can be improved.

Description

Technical Field

[0001] This application relates to the technical field of metal material quality assessment, and particularly to a method and system for metal material quality supervision and assessment. Background Art

[0002] The quality supervision and assessment of metal materials is a key step in ensuring material quality and product safety. It involves comprehensive considerations in multiple aspects, including chemical composition, physical properties, mechanical properties, microstructure, and processing performance. Through strict quality supervision and assessment, not only can product quality and safety be improved, but also technological innovation can be promoted, market competitiveness can be enhanced, public safety and health can be guaranteed, and sustainable development can be supported.

[0003] In traditional technologies, the detection and assessment of the quality of metal materials often involve comprehensive evaluation of data as a whole, without considering the mutual correlation effects between multiple adjacent regions at different positions. As a result, the final quality assessment result of the metal material is only inaccurate as a whole. Summary of the Invention

[0004] To overcome the deficiencies of the above-mentioned prior art, this application provides a method and system for metal material quality supervision and assessment.

[0005] In a first aspect, a method for metal material quality supervision and assessment provided by this application includes:

[0006] Dividing a target metal material to be quality-assessed into several regional blocks to obtain a block set, extracting a target block, an adjacent block one, an adjacent block two, an adjacent block three, and an adjacent block four from the block set, and detecting and extracting a difference data set;

[0007] When difference data belonging to the target region, the adjacent block one, and the adjacent block three can be respectively extracted from the difference data set to obtain target difference data, adjacent difference data one, and adjacent difference data three, and there is no difference data belonging to the adjacent block two and the adjacent block four in the difference data set, determine whether the quality of the target block is qualified, and output a quality qualified assessment result or a quality unqualified assessment result;

[0008] When difference data belonging to the target region, the adjacent block one, and the adjacent block two can be respectively extracted from the difference data set to obtain target difference data, adjacent difference data one, and adjacent difference data two, and there is no difference data belonging to the adjacent block three and the adjacent block four in the difference data set, determine whether the quality of the target block is qualified, and output a quality qualified assessment result or a quality unqualified assessment result;

[0009] Extract a first predicted block adjacent to the position of an adjacent block one from the block set, extract a second predicted block adjacent to the position of an adjacent block two from the block set, and count the to-be-judged data four and to-be-judged data five according to the first predicted block and the second predicted block;

[0010] Judge whether the quality of the target block is qualified according to the to-be-judged data four and to-be-judged data five, and output a quality qualified evaluation result or a quality unqualified evaluation result.

[0011] Preferably, divide the target metal material to be quality-evaluated into several regional blocks to obtain a block set, and select a target block from the block set;

[0012] According to the target block, screen out an adjacent block one, an adjacent block two, an adjacent block three, and an adjacent block four from the block set. The middle area surrounded by the adjacent block one, the adjacent block two, the adjacent block three, and the adjacent block four is the target area;

[0013] Detect all the detection item data of the target block, the adjacent block one, the adjacent block two, the adjacent block three, and the adjacent block four to obtain a detection data set;

[0014] Preset a comparison data threshold, and extract a difference data set that is less than or greater than the comparison data threshold from the detection data set.

[0015] Preferably, when the difference data of the target area, the adjacent block one, and the adjacent block three can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data one, and the adjacent difference data three, and there is no difference data of the adjacent block two and the adjacent block four in the difference data set, then sum the target difference data and the adjacent difference data one to obtain the to-be-judged data one, and sum the target difference data and the adjacent difference data two to obtain the to-be-judged data two;

[0016] Preset a first abnormal determination interval value. If both the to-be-judged data one and the to-be-judged data two are within the first abnormal determination interval value, then determine that the quality evaluation of the target block is qualified and output a quality qualified evaluation result;

[0017] If one of the to-be-judged data one and the to-be-judged data two is greater than the first abnormal determination interval value, then determine that the quality evaluation of the target block is unqualified and output a quality unqualified evaluation result.

[0018] Preferably, when the difference data of the target area, the adjacent block one, and the adjacent block two can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data one, and the adjacent difference data two, and there is no difference data of the adjacent block three and the adjacent block four in the difference data set, then sum the adjacent difference data one and the adjacent difference data two to obtain the to-be-judged data three;

[0019] If the to-be-determined data three is within the first abnormal determination interval value, it is determined that the quality assessment of the target block is qualified, and a quality qualified assessment result is output;

[0020] If the to-be-determined data three is greater than the first abnormal determination interval value, it is determined that the quality assessment of the target block is unqualified, and a quality unqualified assessment result is output.

[0021] Preferably, if the to-be-determined data three is at the critical point of the maximum value in the first abnormal determination interval value, then according to the adjacent block one and the adjacent block two, the first predicted block adjacent to the adjacent block one is extracted from the block set, and the second predicted block adjacent to the adjacent block two is extracted from the block set;

[0022] Perform data of all detection items on the first predicted block and the second predicted block to obtain a preprocessed data set;

[0023] Extract the difference data belonging to the first predicted block and the second predicted block respectively from the preprocessed data set to obtain preprocessed data one and preprocessed data two;

[0024] Sum the preprocessed data one, the target difference data, and the adjacent difference data one to obtain the to-be-determined data four, and sum the preprocessed data two, the target difference data, and the adjacent difference data two to obtain the to-be-determined data five.

[0025] Preferably, a second abnormal determination interval value is preset. If both the to-be-determined data four and the to-be-determined data five are within the second abnormal determination interval value, it is determined that the quality assessment of the target block is qualified, and a quality qualified assessment result is output;

[0026] If there is one kind of to-be-determined data among the to-be-determined data four and the to-be-determined data five that is greater than the second abnormal determination interval value, it is determined that the quality assessment of the target block is unqualified, and a quality unqualified assessment result is output.

[0027] In a second aspect, a quality supervision and evaluation system based on metal materials includes:

[0028] A detection area intercepting unit for dividing the target metal material to be quality-assessed into several regional blocks to obtain a block set, extracting the target block, the adjacent block one, the adjacent block two, the adjacent block three, and the adjacent block four from the block set, and detecting and extracting a difference data set;

[0029] A first condition judgment unit for determining whether the quality of the target block is qualified when the difference data belonging to the target area, the adjacent block one, and the adjacent block three can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data one, and the adjacent difference data three, and there is no difference data belonging to the adjacent block two and the adjacent block four in the difference data set, and outputting a quality qualified assessment result or a quality unqualified assessment result;

[0030] A second condition judgment unit, configured to determine whether the quality of the target block is qualified when the differential data belonging to the target area, the adjacent block 1, and the adjacent block 2 can be separately extracted from the differential dataset to obtain the target differential data, the adjacent differential data 1, and the adjacent differential data 2, and there is no differential data belonging to the adjacent block 3 and the adjacent block 4 in the differential dataset, and output a quality qualified evaluation result or a quality unqualified evaluation result;

[0031] An auxiliary measurement block extraction unit, configured to extract a first predicted block adjacent to the adjacent block 1 from the block set, extract a second predicted block adjacent to the adjacent block 2 from the block set, and count the data to be judged 4 and the data to be judged 5 according to the first predicted block and the second predicted block;

[0032] A third condition judgment unit, configured to determine whether the quality of the target block is qualified according to the data to be judged 4 and the data to be judged 5, and output a quality qualified evaluation result or a quality unqualified evaluation result.

[0033] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0034] By extracting four adjacent blocks adjacent to the target block from the segmented block set and performing statistical analysis on the data with differences in the detection items to which they belong, and determining whether the quality of the target block is qualified according to whether the sum of the target differential data and the differential data belonging to other adjacent blocks is greater than the first abnormal determination interval value, where through case-by-case judgment, that is, if there are two adjacent blocks with differential data among the four adjacent blocks, whether these two adjacent blocks are adjacent in position or not adjacent in position, and perform differential analysis according to the conditions of adjacent or non-adjacent, so as to perform a more comprehensive analysis under diverse conditions, avoiding the traditional technology that only makes an overall comprehensive quality assessment analysis, that is, there is a single information processing method resulting in a large error in the analysis result, reducing the work efficiency of the evaluation process. Through the above processing method, the quality assessment result verification process under different conditions is indirectly performed, so as to enhance the accuracy and reliability of the final quality assessment result, make full use of the data information, and greatly further analyze the mutual correlation between the information. Description of the Drawings

[0035] Figure 1 It is a step block diagram of a method for quality supervision and evaluation of metal materials mainly embodied in this embodiment.

[0036] Figure 2 It is a structural block diagram of a system for quality supervision and evaluation of metal materials mainly embodied in this embodiment. Detailed Embodiment

[0037] The present invention will be further described in detail below in conjunction with the following embodiments.

[0038] Referring to Figure 1 , a quality supervision and evaluation method based on metal materials, the method comprising the following steps:

[0039] S1. Divide the target metal material to be quality-evaluated into several regional blocks to obtain a block set, extract a target block, an adjacent block one, an adjacent block two, an adjacent block three, and an adjacent block four from the block set, and detect and extract a difference data set.

[0040] S2. When the difference data belonging to the target area, the adjacent block one, and the adjacent block three can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data one, and the adjacent difference data three, and there is no difference data belonging to the adjacent block two and the adjacent block four in the difference data set, determine whether the quality of the target block is qualified, and output a quality qualified evaluation result or a quality unqualified evaluation result.

[0041] S3. When the difference data belonging to the target area, the adjacent block one, and the adjacent block two can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data one, and the adjacent difference data two, and there is no difference data belonging to the adjacent block three and the adjacent block four in the difference data set, determine whether the quality of the target block is qualified, and output a quality qualified evaluation result or a quality unqualified evaluation result.

[0042] S4. Extract a first predicted block adjacent to the adjacent block one in position from the block set, extract a second predicted block adjacent to the adjacent block two in position from the block set, and count the data to be judged four and the data to be judged five according to the first predicted block and the second predicted block.

[0043] S5. Determine whether the quality of the target block is qualified according to the data to be judged four and the data to be judged five, and output a quality qualified evaluation result or a quality unqualified evaluation result.

[0044] Specifically, by extracting four adjacent blocks adjacent to the target block position from the segmented block set and performing data statistics on the data with differences in the detection items to which they belong, and determining the quality qualification of the target block according to whether the sum of the target difference data and the difference data to which the other adjacent blocks belong is greater than the first abnormal determination interval value. Among them, by judging in different cases, that is, if there are two adjacent blocks with difference data among the four adjacent blocks, whether these two adjacent blocks are adjacent in position or not adjacent in position, and performing differential analysis according to the conditions of adjacency or non - adjacency, so as to conduct a more comprehensive analysis under diverse conditions, avoiding the traditional technology that only conducts overall comprehensive quality assessment and analysis, that is, there is a single information processing method resulting in a large error in the analysis result, reducing the work efficiency of the assessment process. Through the above - mentioned processing method, the quality assessment result verification processing under different conditions is indirectly carried out, so as to enhance the accuracy and reliability of the final quality assessment result, make full use of the data information, and greatly further analyze the mutual correlation between information.

[0045] Specifically, step S1 includes the following sub - steps:

[0046] Divide the target metal material to be quality - assessed into several regional blocks to obtain a block set, and select a target block from the block set.

[0047] According to the target block, select adjacent block one, adjacent block two, adjacent block three, and adjacent block four from the block set. The middle area surrounded by adjacent block one, adjacent block two, adjacent block three, and adjacent block four is the target area.

[0048] Detect the data of all detection items of the target block, adjacent block one, adjacent block two, adjacent block three, and adjacent block four to obtain a detection data set.

[0049] Preset a comparison data threshold, and extract a difference data set in the detection data set that is less than or greater than the comparison data threshold.

[0050] Specifically, such as a block set (the areas of the multiple divided blocks can be equal or unequal), a target block (in this article, one of the blocks is refined for analysis, and so on. Subsequently, the other divided blocks are successively used as target blocks for the next quality assessment and analysis process), adjacent block one, adjacent block two, adjacent block three, and adjacent block four (if they are a1, a2, a3, and a4 respectively, where a1 is adjacent to a2 and a4 respectively, but a2 and a4 are not adjacent, a2 is adjacent to a1 and a3 respectively, but a1 and a3 are not adjacent, and so on. There is no need to elaborate here), a detection data set (the detected data involves several aspects of characteristic information: chemical composition - element content status, physical property status - strength, hardness, and toughness, crystal structure status, size status, presence or absence of damage status - cracks and pores, etc. A number of detection items are uniformly marked with percentages here for easy analysis), a comparison data threshold (that is, a critical judgment threshold used to initially determine whether there are differential values in the detection items, so as to initially determine which blocks may have quality problems and which blocks are normal. Moreover, for the initially determined normal blocks, the processing steps can be reduced subsequently, and further conditional analysis can be performed on the blocks that may have quality problems, thereby reducing the influence error caused by interfering information on the processing steps. If the comparison data threshold is 75%-85%), a differential data set (for example, the strength and hardness of the target block belong to is less than the comparison data threshold. If the strength and hardness is 70%, then the differential data of the target block belong to is 5% for strength and hardness. If the data of other detection items of the target block belong to is the same as the comparison data threshold, then only the strength and hardness will be further analyzed subsequently).

[0051] Specifically, step S2 includes the following sub-steps:

[0052] When the differential data of the target area, adjacent block one, and adjacent block three can be separately extracted from the differential data set to obtain target differential data, adjacent differential data one, and adjacent differential data three, and there is no differential data of adjacent block two and adjacent block four in the differential data set, then the target differential data and the adjacent differential data one are summed to obtain a data to be judged one, and the target differential data and the adjacent differential data two are summed to obtain a data to be judged two.

[0053] A first abnormal determination interval value is preset. If both the data to be judged one and the data to be judged two are within the first abnormal determination interval value, then it is determined that the quality assessment of the target block is qualified, and a quality qualified assessment result is output.

[0054] If one of the data to be judged one and the data to be judged two is greater than the first abnormal determination interval value, then it is determined that the quality assessment of the target block is unqualified, and a quality unqualified assessment result is output.

[0055] Specifically, for target difference data, adjacent difference data 1 and adjacent difference data 3 (such as 5% in strong hardness difference, 2% in size difference, and 3% in strong hardness difference respectively), judgment data 1 (i.e., the sum of difference values is 7%), judgment data 2 (i.e., the sum of difference values is 8%), and the first abnormal judgment interval value (i.e., the allowable data difference degree value, within which the overall quality of the metal material is not affected, such as 5% - 10%). If both judgment data 1 and judgment data 2 are within the first abnormal judgment interval value (i.e., judgment data 1 is 7% and judgment data 2 is 8%), it can be determined that the difference value detection items of adjacent block 1, adjacent block 2, and the adjacent target block do not affect the metal quality, so it can be judged that the quality assessment of the target block is qualified. If one of judgment data 1 and judgment data 2 is greater than the first abnormal judgment interval value (for example, judgment data 1 is 11%), it can be determined that the quality of the target block is abnormal. Because the data difference combination of two adjacent blocks will cause quality abnormality in the difference detection items of the target block or adjacent block 1, that is, it will expand the abnormality degree of the difference detection items of the target block or adjacent block 1, so it is judged that the quality assessment result of the target block is unqualified. It should be noted that adjacent block 1 and adjacent block 3 to which adjacent difference data 1 and adjacent difference data 3 belong are not adjacent, so separate analysis and processing are carried out for their respective combinations with the target block.

[0056] Specifically, step S3 includes the following sub - steps:

[0057] When the difference data belonging to the target area, adjacent block 1, and adjacent block 2 can be respectively extracted from the difference data set to obtain target difference data, adjacent difference data 1, and adjacent difference data 2, and there is no difference data belonging to adjacent block 3 and adjacent block 4 in the difference data set, then adjacent difference data 1 and adjacent difference data 2 are summed to obtain judgment data 3.

[0058] If judgment data 3 is within the first abnormal judgment interval value, it is determined that the quality assessment of the target block is qualified, and a quality - qualified assessment result is output.

[0059] If judgment data 3 is greater than the first abnormal judgment interval value, it is determined that the quality assessment of the target block is unqualified, and a quality - unqualified assessment result is output.

[0060] Specifically, for the target difference data, adjacent difference data 1, and adjacent difference data 2 (which are the same as the explanations in the previous step and will not be elaborated here. If they are a 3% difference in hardness, a 2% difference in element content, and a 2% difference in size respectively), it should be noted that: the adjacent blocks 1 and 2 to which the adjacent difference data 1 and adjacent difference data 2 belong are adjacent in position. Therefore, an overall comprehensive analysis and processing of their combination with the target block is carried out. If the data to be judged 3 is within the first abnormal judgment interval value, it is determined that the quality assessment of the target block is qualified. If the data to be judged 3 is greater than the first abnormal judgment interval value, it is determined that the quality assessment of the target block is unqualified (which is the same as the explanations for the data to be judged 1 and data to be judged 2 and will not be elaborated here).

[0061] Specifically, step S4 includes the following sub-steps:

[0062] If the data to be judged 3 is at the critical point of the maximum value in the first abnormal judgment interval value, then according to adjacent block 1 and adjacent block 2, the first predicted block adjacent to the position of adjacent block 1 is extracted from the block set, and the second predicted block adjacent to the position of adjacent block 2 is extracted from the block set.

[0063] Perform data for all inspection items on the first predicted block and the second predicted block to obtain a preprocessed data set.

[0064] Extract the difference data belonging to the first predicted block and the second predicted block respectively from the preprocessed data set to obtain preprocessed data 1 and preprocessed data 2.

[0065] Sum up the preprocessed data 1, the target difference data, and the adjacent difference data 1 to obtain the data to be judged 4, and sum up the preprocessed data 2, the target difference data, and the adjacent difference data 2 to obtain the data to be judged 5.

[0066] Specifically, if the data to be judged 3 is at the critical point of the maximum value in the first abnormal judgment interval value (if the target difference data, adjacent difference data 1, and adjacent difference data 2 are respectively: a 6% difference in hardness, a 2% difference in element content, and a 2% difference in size, then the data to be judged 3 is 10%, and it is impossible to directly determine the quality status of the target block, which may be qualified or unqualified. At this time, a further auxiliary verification analysis is required), the first predicted block, the second predicted block (if they are b1 and b2, it should be noted that: the middle area surrounded by the four blocks b1, a4, a2, and the target block is a1), the preprocessed data set (which is the same as the explanation of the above inspection data set and will not be elaborated here), the data to be judged 4, and the data to be judged 5 (which is the same as the explanation of the data to be judged 3 and will not be elaborated here. If they are 11% and 13% respectively).

[0067] Specifically, step S5 includes the following sub-steps:

[0068] Preset the second abnormal determination interval value. If both the data to be judged four and the data to be judged five are within the second abnormal determination interval value, it is determined that the quality assessment of the target block is qualified, and a quality qualified assessment result is output.

[0069] If there is a kind of data to be judged among the data to be judged four and the data to be judged five that is greater than the second abnormal determination interval value, it is determined that the quality assessment of the target block is unqualified, and a quality unqualified assessment result is output.

[0070] Specifically, for example, preset the second abnormal determination interval value (larger than the first abnormal determination interval value and having the same interpretation as the first abnormal determination interval value, if it is 11%-15%). If both the data to be judged four and the data to be judged five are within the second abnormal determination interval value, it is determined that the quality assessment of the target block is qualified (if the data to be judged four and the data to be judged five are 11% and 13% respectively, it is determined that the quality assessment of the target block is qualified). If the data to be judged four and the data to be judged five are 16% and 13% respectively, it is determined that the quality assessment of the target block is unqualified. Because the combined influence of the data differences of the detection items of two adjacent blocks will cause the quality abnormality of the difference detection items of the target block or the adjacent block one, that is, it will expand the abnormal degree of the detection data difference items of the target block or the adjacent block one, so it is judged that the quality assessment result of the target block is unqualified.

[0071] A quality supervision and evaluation system based on metal materials, by applying a quality supervision and evaluation method based on metal materials as described above, includes a detection area intercepting unit, a first condition judgment unit, a second condition judgment unit, an auxiliary measurement block extraction unit and a third condition judgment unit, referring to Figure 2, the target metal material to be quality - evaluated is segmented into several regional blocks by the detection - area intercepting unit to obtain a block set. The target block, adjacent block 1, adjacent block 2, adjacent block 3, and adjacent block 4 are extracted from the block set, and a difference data set is detected and extracted. The first condition - judging unit judges that when the difference data belonging to the target area, adjacent block 1, and adjacent block 3 can be respectively extracted from the difference data set to obtain target difference data, adjacent difference data 1, and adjacent difference data 3, and there is no difference data belonging to adjacent block 2 and adjacent block 4 in the difference data set, it determines whether the quality of the target block is qualified and outputs a quality - qualified evaluation result or a quality - unqualified evaluation result. The second condition - judging unit judges that when the difference data belonging to the target area, adjacent block 1, and adjacent block 2 can be respectively extracted from the difference data set to obtain target difference data, adjacent difference data 1, and adjacent difference data 2, and there is no difference data belonging to adjacent block 3 and adjacent block 4 in the difference data set, it determines whether the quality of the target block is qualified and outputs a quality - qualified evaluation result or a quality - unqualified evaluation result. The auxiliary - measurement block extraction unit extracts a first predicted block adjacent to the position of adjacent block 1 from the block set, extracts a second predicted block adjacent to the position of adjacent block 2 from the block set, and statistics data to be judged 4 and data to be judged 5 according to the first predicted block and the second predicted block. The third condition - judging unit determines whether the quality of the target block is qualified according to data to be judged 4 and data to be judged 5 and outputs a quality - qualified evaluation result or a quality - unqualified evaluation result.

[0072] The above are all preferred embodiments of this application, and the protection scope of this application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for metal material quality supervision and evaluation, characterized in that: The following steps are involved: The target metal material to be quality assessed is divided into a number of area blocks to obtain a block set, a target block, an adjacent block 1, an adjacent block 2, an adjacent block 3 and an adjacent block 4 are extracted from the block set, and a difference data set is extracted by detection; When the difference data of the target area, the adjacent block 1 and the adjacent block 3 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 3, and the difference data set does not contain the difference data of the adjacent block 2 and the adjacent block 4, determining whether the quality of the target block is qualified, and outputting a qualified quality assessment result or an unqualified quality assessment result; When the difference data of the target area, the adjacent block 1 and the adjacent block 2 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 2, and the difference data set does not contain the difference data of the adjacent block 3 and the adjacent block 4, it is determined whether the quality of the target block is qualified, and a qualified quality assessment result or an unqualified quality assessment result is output; Extracting a first prediction block adjacent to a first adjacent block from the block set, extracting a second prediction block adjacent to a second adjacent block from the block set, and statistically obtaining a fourth to-be-judged data and a fifth to-be-judged data based on the first prediction block and the second prediction block; According to the fourth data to be judged and the fifth data to be judged, it is determined whether the quality of the target block is qualified, and a qualified quality assessment result or an unqualified quality assessment result is output.

2. A method for metal material quality supervision and evaluation according to claim 1, characterized in that: The target metal material to be quality assessed is divided into a number of area blocks to obtain a block set, a target block, an adjacent block 1, an adjacent block 2, an adjacent block 3 and an adjacent block 4 are extracted from the block set, and a step of detecting and extracting a difference data set is specifically as follows: Dividing the target metal material to be quality assessed into a plurality of area blocks to obtain a block set, and selecting a target block from the block set; According to the target block, adjacent block 1, adjacent block 2, adjacent block 3 and adjacent block 4 are selected from the block set, and the middle area surrounded by adjacent block 1, adjacent block 2, adjacent block 3 and adjacent block 4 is the target area; Detect all detection item data of the target block, adjacent block one, adjacent block two, adjacent block three and adjacent block four to obtain a detection data set; A comparison data threshold is preset, and a difference data set that is smaller than or larger than the comparison data threshold is extracted from the detection data set.

3. A method for metal material quality supervision and assessment according to claim 2, characterized in that: When the difference data of the target area, the adjacent block 1 and the adjacent block 3 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 3, and the difference data set does not contain the difference data of the adjacent block 2 and the adjacent block 4, the step of determining whether the quality of the target block is qualified and outputting a qualified quality assessment result or an unqualified quality assessment result is specifically as follows: When the difference data of the target area, the adjacent block 1 and the adjacent block 3 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 3, and the difference data set does not contain the difference data of the adjacent block 2 and the adjacent block 4, the target difference data and the adjacent difference data 1 are summed to obtain the data to be determined 1, and the target difference data and the adjacent difference data 2 are summed to obtain the data to be determined 2; A first abnormality determination interval value is preset, and if the first data to be determined and the second data to be determined are both within the first abnormality determination interval value, it is determined that the quality assessment of the target block is qualified, and a qualified quality assessment result is output; If one of the data to be judged 1 and the data to be judged 2 is greater than the first abnormal judgment interval value, it is determined that the quality assessment of the target block is unqualified, and an unqualified quality assessment result is output.

4. A method for metal material quality supervision and assessment according to claim 3, characterized in that: When the difference data of the target area, the adjacent block 1 and the adjacent block 2 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 2, and the difference data set does not contain the difference data of the adjacent block 3 and the adjacent block 4, the step of determining whether the quality of the target block is qualified and outputting the qualified quality assessment result or the unqualified quality assessment result is specifically as follows: When the difference data of the target area, the adjacent block 1 and the adjacent block 2 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 2, and the difference data set does not contain the difference data of the adjacent block 3 and the adjacent block 4, then the adjacent difference data 1 and the adjacent difference data 2 are summed to obtain the data to be judged 3; If the third data to be judged is within the first abnormal judgment interval value, the quality assessment of the target block is judged to be qualified, and a qualified quality assessment result is output; If the third data to be judged is greater than the first abnormal judgment interval value, it is judged that the quality assessment of the target block is unqualified, and an unqualified quality assessment result is output.

5. A method for metal material quality supervision and assessment according to claim 4, characterized in that: The steps of extracting a first prediction block adjacent to a first adjacent block from the block set, extracting a second prediction block adjacent to a second adjacent block from the block set, and calculating a fourth to-be-judged data and a fifth to-be-judged data according to the first prediction block and the second prediction block are specifically as follows: If the third data to be judged is at the critical point of the maximum value in the first abnormal judgment interval value, then according to the adjacent block one and the adjacent block two, a first prediction block adjacent to the adjacent block one is extracted from the block set, and a second prediction block adjacent to the adjacent block two is extracted from the block set; Processing all detection item data of the first prediction block and the second prediction block to obtain a preprocessed data set; Extracting difference data belonging to the first prediction block and the second prediction block respectively from the preprocessing data set to obtain preprocessing data 1 and preprocessing data 2; The preprocessed data 1, the target difference data and the adjacent difference data 1 are summed to obtain the data to be determined 4, and the preprocessed data 2, the target difference data and the adjacent difference data 2 are summed to obtain the data to be determined 5.

6. A method for metal material quality supervision and assessment according to claim 5, characterized in that: The step of determining whether the quality of the target block is qualified according to the fourth data to be determined and the fifth data to be determined, and outputting a qualified quality assessment result or an unqualified quality assessment result is specifically as follows: A second abnormality determination interval value is preset, and if the data to be determined four and the data to be determined five are both within the second abnormality determination interval value, it is determined that the quality assessment of the target block is qualified, and a qualified quality assessment result is output; If one of the data to be judged four and the data to be judged five has a value greater than the second abnormal judgment interval value, it is determined that the quality assessment of the target block is unqualified, and an unqualified quality assessment result is output.

7. A metal material quality supervision and evaluation system, characterized in that: The system is used to implement a metal material quality supervision and evaluation method according to any one of claims 1 to 6, comprising: A detection area interception unit is used to divide the target metal material to be quality evaluated into a number of area blocks to obtain a block set, extract the target block, adjacent block one, adjacent block two, adjacent block three and adjacent block four from the block set, and detect and extract a difference data set; A first condition judgment unit is used to determine whether the quality of the target block is qualified, and output a qualified quality assessment result or an unqualified quality assessment result when the difference data of the target area, the adjacent block 1 and the adjacent block 3 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 3, and the difference data set does not contain the difference data of the adjacent block 2 and the adjacent block 4; The second condition judgment unit is used to determine whether the quality of the target block is qualified when the difference data of the target area, the adjacent block 1 and the adjacent block 2 can be respectively extracted from the difference data set to obtain the target difference data, the adjacent difference data 1 and the adjacent difference data 2, and the difference data set does not contain the difference data of the adjacent block 3 and the adjacent block 4, and output a qualified quality assessment result or an unqualified quality assessment result; The auxiliary test block extraction unit is used to extract a first prediction block adjacent to the first adjacent block from the block set, extract a second prediction block adjacent to the second adjacent block from the block set, and calculate the fourth and fifth to-be-determined data based on the first prediction block and the second prediction block; The third condition judgment unit is used to judge whether the quality of the target block is qualified according to the fourth data to be judged and the fifth data to be judged, and output a qualified quality assessment result or an unqualified quality assessment result.

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