CN118839138B - Intelligent evaluation method and system for freezing effect of spiral quick freezer - Google Patents
Intelligent evaluation method and system for freezing effect of spiral quick freezer Download PDFInfo
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- 238000007710 freezing Methods 0.000 title claims abstract description 135
- 230000008014 freezing Effects 0.000 title claims abstract description 127
- 230000000694 effects Effects 0.000 title claims abstract description 78
- 238000011156 evaluation Methods 0.000 title claims description 110
- 239000011159 matrix material Substances 0.000 claims abstract description 97
- 238000012545 processing Methods 0.000 claims abstract description 72
- 238000012544 monitoring process Methods 0.000 claims abstract description 51
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000004458 analytical method Methods 0.000 claims abstract description 26
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- 238000005728 strengthening Methods 0.000 claims description 17
- 238000004519 manufacturing process Methods 0.000 claims description 16
- 238000013507 mapping Methods 0.000 claims description 16
- 230000003044 adaptive effect Effects 0.000 claims description 15
- 230000003993 interaction Effects 0.000 claims description 9
- 238000002425 crystallisation Methods 0.000 claims description 8
- 230000008025 crystallization Effects 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 7
- 239000011229 interlayer Substances 0.000 claims description 7
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- 230000002159 abnormal effect Effects 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 238000005111 flow chemistry technique Methods 0.000 claims description 2
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- 238000007726 management method Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000005057 refrigeration Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
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Abstract
The invention provides an intelligent assessment method and system for freezing effect of a spiral instant freezer, which relate to the technical field of data processing, and are characterized in that a desired index matrix is determined based on a pre-control program, monitoring and sensing data is determined, a real-control index matrix is obtained, matrix differentiation measurement is carried out, a main assessment result is determined, mechanical transmission performance assessment is carried out, a secondary assessment result is determined, cross-correlation analysis is carried out, the problem that a complete assessment mode is lacked systematically, and the intelligent assessment method is inadequately intelligent, the analysis depth and the completeness are insufficient, so that the assessment accuracy of the freezing effect is limited, the technical problem of limitation of transportation management of subsequent equipment is caused, freezing stage division and hierarchical characteristic reinforcement processing of monitoring data are carried out by taking freezing dimension and mechanical transmission dimension as the main and secondary directions of assessment, real-time freezing characteristics are accurately positioned, comprehensive analysis is carried out by combining transmission characteristics, intelligent systematic assessment analysis of the freezing effect of equipment is realized, and objective consistency of the assessment result is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent assessment method and system for freezing effect of a spiral instant freezer.
Background
With the popularization of quick-frozen foods and the standard requirement of high quality, quick-frozen equipment is used as necessary mechanical freezing equipment, the technical requirement on the quick-frozen equipment is higher and higher, and the spiral quick-frozen machine becomes the current dominant quick-frozen equipment by the characteristics of high efficiency, high quality, no loss and the like. At present, the quality inspection of frozen articles and the quality inspection of equipment operation are mainly carried out at regular intervals, and the freezing effect is determined based on the qualification degree of quality inspection results. The prior art lacks a systematic complete evaluation mode, is not intelligent enough, and has insufficient analysis depth and completeness, so that the evaluation accuracy of the freezing effect is limited, and the transportation and management of subsequent equipment are limited.
Disclosure of Invention
The application provides an intelligent assessment method and an intelligent assessment system for a freezing effect of a spiral instant freezer, which are used for solving the technical problems that in the prior art, a complete assessment mode which is lack of systematicness is insufficient, the analysis depth and the completeness are insufficient, the assessment accuracy of the freezing effect is limited, and the transportation and management of subsequent equipment are limited.
In view of the above problems, the application provides an intelligent assessment method and system for the freezing effect of a spiral instant freezer.
In a first aspect, the present application provides a method for intelligently evaluating a freezing effect of a spiral instant freezer, the method comprising:
determining an expected index matrix for measuring expected freezing effects of target frozen articles based on a pre-control program set by an assembled programmable controller, wherein the expected index matrix identifies a tolerance interval based on technical loss;
Starting the spiral instant freezer and performing freezing parameter control monitoring along with blanking of the target frozen articles to determine monitoring sensing data, wherein a feeding conveying belt and a discharging conveying belt of the spiral instant freezer are connected with a production line;
based on the monitoring sensing data, combining a feature extraction module to perform feature extraction based on the expected index matrix, performing selective feature strengthening processing based on a configured hierarchical data processing algorithm, and performing cascade analysis on interlayer features to determine an index feature cascade network, wherein the index feature cascade network is in a pyramid structure;
based on the index feature cascade network, accurately positioning index feature values and generating a real control index matrix;
based on the expected index matrix and the actual control index matrix, performing matrix differentiation measurement to determine a main evaluation result;
based on the monitoring sensing data, evaluating the mechanical transmission performance of the equipment of the spiral instant freezer, and determining a secondary evaluation result;
And determining the freezing evaluation effect of the spiral instant freezer based on the main evaluation result and the secondary evaluation result by combining cross correlation analysis.
In a second aspect, the present application provides an intelligent assessment system for the freezing effect of a spiral instant freezer, the system comprising:
the system comprises an expected index matrix determining module, a target freezing object determining module and a target freezing object determining module, wherein the expected index matrix determining module is used for determining an expected index matrix for measuring expected freezing effects of target frozen objects based on a pre-control program set by an assembled programmable controller, and the expected index matrix identifies a tolerance interval based on technical loss;
The sensing monitoring module is used for starting the spiral instant freezer along with blanking of the target frozen article and performing freezing parameter control monitoring to determine monitoring sensing data, wherein a feeding conveying belt and a discharging conveying belt of the spiral instant freezer are connected with a production line;
the feature extraction module is used for carrying out feature extraction based on the expected index matrix by combining with the feature extraction module based on the monitoring sensing data, carrying out selective feature strengthening processing based on a configured hierarchical data processing algorithm, carrying out cascade analysis on interlayer features, and determining an index feature cascade network, wherein the index feature cascade network is of a pyramid structure;
The real control index matrix generation module is used for precisely positioning index characteristic values and generating a real control index matrix based on the index characteristic cascade network;
The main evaluation result determining module is used for carrying out matrix differentiation measurement based on the expected index matrix and the actual control index matrix to determine a main evaluation result;
The secondary evaluation result determining module is used for evaluating the mechanical transmission performance of the equipment of the spiral instant freezer based on the monitoring sensing data and determining a secondary evaluation result;
And the freezing evaluation effect determining module is used for determining the freezing evaluation effect of the spiral instant freezer based on the main evaluation result and the secondary evaluation result and combining cross correlation analysis.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
According to the intelligent assessment method for the freezing effect of the spiral quick-freezing machine, based on a pre-control program set by an assembled programmable controller, an expected index matrix for measuring expected freezing effect of a target frozen article is determined, the spiral quick-freezing machine is started and freezing parameter control monitoring is carried out along with blanking of the target frozen article, monitoring sensing data is determined, hierarchical feature extraction mapping and cascading analysis based on the expected index matrix are carried out by combining a feature extraction module, an index feature cascading network is determined, an index feature value is accurately positioned, a real-control index matrix is generated, differential measurement of the expected index matrix and the real-control index matrix is carried out, a main assessment result is determined, mechanical transmission performance assessment is carried out based on the monitoring sensing data, a secondary assessment result is determined, cross-correlation analysis is carried out, and the freezing assessment effect of the spiral quick-freezing machine is determined, the problem that a complete assessment mode lacks systematicness in the prior art is solved, analysis depth and completeness are insufficient, the assessment accuracy of the freezing effect is limited, the technical problem of subsequent equipment operation limitation is caused, the freezing dimension is divided into main and secondary stage with the mechanical transmission dimension as the main dimension is accurately positioned, the main stage is, the characteristic is accurately positioned, the characteristic is accurately and the characteristic is accurately processed, the characteristic analysis is carried out, and the real-time analysis is carried out, and the characteristic analysis is carried out.
Drawings
FIG. 1 is a schematic flow chart of an intelligent assessment method for the freezing effect of a spiral instant freezer;
FIG. 2 is a schematic diagram of a connection flow of a structure in the intelligent assessment method of the freezing effect of the spiral instant freezer;
fig. 3 is a schematic structural diagram of an intelligent assessment system for the freezing effect of the spiral instant freezer.
The reference numerals illustrate that the expected index matrix determining module 11, the sensing monitoring module 12, the characteristic extracting module 13, the real control index matrix generating module 14, the primary evaluation result determining module 15, the secondary evaluation result determining module 16 and the freezing evaluation effect determining module 17.
Detailed Description
The application provides an intelligent assessment method and an intelligent assessment system for the freezing effect of a spiral instant freezer, which are used for determining an expected index matrix based on a pre-control program, performing freezing parameter control monitoring on the spiral instant freezer to determine monitoring sensing data, determining a real control index matrix based on the freezing effect, performing matrix differentiation measurement to determine a main assessment result, performing mechanical transmission performance assessment to determine a secondary assessment result, and performing cross-correlation analysis to determine the freezing assessment effect, so that the technical problems of the prior art that a complete assessment mode is lack of systematicness, the intelligent assessment method is not enough, the analysis depth and the integrity are insufficient, the assessment accuracy of the freezing effect is limited, and the transportation limit of subsequent equipment is caused.
Example 1
As shown in fig. 1 and 2, the application provides an intelligent assessment method for freezing effect of a spiral instant freezer, which comprises the following steps:
s1, determining an expected index matrix for measuring expected freezing effects of frozen articles based on a pre-control program set by an assembled programmable controller, wherein the expected index matrix identifies a tolerance interval based on technical loss;
Wherein, the determining measures the expected index matrix of the expected freezing effect of the frozen object, the application S1 further comprises:
S11, setting a plurality of freezing stages based on the pre-control program and the target frozen article, wherein the freezing stages at least comprise a liquid quick-freezing stage and a crystallization stage;
s12, configuring evaluation indexes for measuring freezing effects, traversing the plurality of freezing stages to determine expected characteristic values of the stage indexes, and obtaining a plurality of groups of index characteristic values;
and S13, constructing an expected index matrix by taking the evaluation index as a matrix row, taking the plurality of freezing stages as a matrix array and taking the plurality of groups of index characteristic values as distribution matrix items.
With the popularization of quick-frozen foods and the standard requirement of high quality, quick-frozen equipment is used as necessary mechanical freezing equipment, the technical requirement on the quick-frozen equipment is higher and higher, and the spiral quick-frozen machine becomes the current dominant quick-frozen equipment by the characteristics of high efficiency, high quality, no loss and the like. According to the intelligent assessment method for the freezing effect of the spiral instant freezer, provided by the application, the freezing dimension and the mechanical transmission dimension are taken as the principal and secondary directions of assessment, the hierarchical characteristic reinforcement processing of freezing stage division and monitoring data is carried out, the real-time freezing characteristic is accurately positioned, the transmission characteristic is combined for comprehensive analysis, the intelligent systematic assessment analysis of the freezing effect of equipment is realized, and the objective consistency of the assessment result is ensured.
The programmable controller is a control center in the operation process of the spiral instant freezer and is used for conducting command operation of equipment. And determining the pre-control program in the programmable controller, namely, the braking control program for performing the spiral quick-freezing operation, wherein the pre-control program is adapted to the target frozen article and is configured by a person skilled in the art based on quick-freezing requirements. Further, based on the pre-control program, a measure of the freezing effect of the target frozen article in the desired state is determined.
Specifically, the freezing stages of the target frozen article are divided, the plurality of freezing stages are determined, and the plurality of freezing stages correspond to a plurality of stepwise pre-control programs. The plurality of freezing stages at least comprise the liquid quick-freezing stage and the crystallization stage, and specific stage criteria are not particularly limited. For each freezing stage, the determination of the measure index of the stepwise freezing effect is performed based on the mapped stepwise pre-control program, for example, for the crystallization stage, including the crystallization size, crystallization uniformity, crystallization rate, etc., optimal freezing control needs to be realized on the basis of guaranteeing the cell moisture, the original quality, etc. of the target frozen article. And determining index characteristic values which are adapted to corresponding stepwise pre-control programs, and integrally determining a plurality of groups of index characteristic values, wherein the plurality of groups of index characteristic values are in one-to-one correspondence with the plurality of freezing stages.
Further, extracting completeness evaluation indexes covering a plurality of freezing stages, taking the completeness evaluation indexes as matrix rows, and performing matrix item distribution on the plurality of groups of index characteristic values by taking the freezing stages as matrix columns, wherein the matrix directions corresponding to evaluation indexes which are not possessed by each freezing stage are subjected to blank arrangement or marking as 0, so that the established expected index matrix is obtained. The tolerance interval based on technical loss is inevitably lost in the freezing process of the equipment, and the tolerance interval is required to be excluded from the characteristic deviation when the index differentiation analysis is carried out subsequently. The expected index matrix is a freezing effect measurement index which accords with the pre-control program and is in an ideal state, and the expected index matrix is used as a reference basis for evaluating the freezing effect of the spiral freezer.
S2, starting the spiral instant freezer along with blanking of the target frozen article, and performing freezing parameter control monitoring to determine monitoring sensing data, wherein a feeding conveying belt and a discharging conveying belt of the spiral instant freezer are connected with a production line;
s3, based on the monitoring sensing data, combining a feature extraction module to perform feature extraction based on the expected index matrix, performing selective feature strengthening processing based on a configured hierarchical data processing algorithm, and performing cascade analysis on interlayer features to determine an index feature cascade network, wherein the index feature cascade network is in a pyramid structure;
the spiral refrigerator is characterized in that the feeding conveying belt and the discharging conveying belt of the spiral refrigerator are connected with the production line to serve as a ring in a production procedure, and the spiral refrigerator is used for carrying out refrigeration and conveying of conveyed objects in cooperation with the production line, and generally, the spiral refrigerator has an efficient refrigeration effect, and the refrigeration time sequence is generally tens of minutes. And the target frozen articles are produced and transmitted in the production line, the feeding conveyor belt is used for circulating the target frozen articles to the spiral instant freezer, the spiral instant freezer is synchronously started along with the blanking completion of the target frozen articles, braking control is carried out on the basis of the pre-control program, the freezing parameter control process is monitored, and time sequence integration and integration are carried out on the monitoring data of the freezing process to serve as the monitoring sensing data. The monitoring sensing data is an acquisition data source for evaluating the freezing effect. Further, based on the monitoring sensing data, in combination with the expected index matrix, data index values for measuring the freezing effect are extracted, selective hierarchy strengthening processing is performed based on the extracted feature state, so that the accuracy of the features is guaranteed, and the index feature cascade network characterized by a pyramid structure is built.
Wherein, the combined feature extraction module performs hierarchical feature extraction mapping and cascade analysis based on the expected index matrix, and the application S3 further includes:
s31, the feature extraction module comprises a plurality of fully-connected network layers, wherein each network layer is configured with a data processing algorithm;
S32, extracting a plurality of source data mapped to each evaluation index by taking the evaluation index as a reference and combining a data screening layer in the characteristic extraction module;
and S33, transferring the multiple source data streams to a rear-mounted multi-stage functional layer, performing feature extraction and hierarchy selective strengthening treatment, and cascading index features based on hierarchy mapping to obtain the index feature cascading network.
Wherein, the feature extraction and hierarchy selective enhancement processing are performed, and the application S33 further includes:
S331, configuring a characteristic intensity threshold for measuring characteristic definition;
and S332, carrying out hierarchical processing on the feature intensity threshold value serving as constraint layer by layer, wherein the feature intensity threshold value is characterized in that the post-functional layer selectively flows to the card, and the features to be enhanced are screened.
Wherein, the application also exists S333, including:
S3331, determining an adaptive feature type based on a data processing algorithm of network configuration;
s3332, mapping the adaptive feature types and the multi-level functional layers to serve as hierarchical data processing constraint;
And S3333, carrying out selective processing of the features to be enhanced based on the hierarchical data processing constraint, and determining hierarchical enhancement features, wherein the selective processing refers to enhancement processing or air stream processing.
The feature extraction module is a built functional model for extracting index features of the monitoring sensing data, and comprises a data screening layer and a multi-stage functional layer, wherein the multi-stage functional layer is respectively configured with different data processing algorithms, such as feature scaling, feature enhancement, contrast enhancement and the like, and hierarchical connection between the data screening layer and the multi-stage functional layer is established to generate the feature extraction module. Further, the monitoring sensing data is input into the feature extraction module, identification extraction of mapping monitoring sensing data of each evaluation index is carried out based on the data screening layer and the evaluation index as a reference, and the multiple source data are determined, wherein the multiple source data are in one-to-one correspondence with the evaluation index.
And further, the multiple source data streams are transferred to a function layer behind the monitoring sensing data, and feature extraction corresponding to each evaluation index is carried out, wherein each evaluation index corresponds to at least one extraction feature, and one-time extraction feature is determined. Further, based on the rear functional layer, the feature strength of the primary extracted features is improved sequentially.
Specifically, the feature intensity threshold for measuring feature definition is configured, and the feature intensity threshold can be customized by a person skilled in the art based on the information completeness requirement of the feature. After the primary extracted feature flow is transferred to a rear functional layer, the feature intensity threshold is used as constraint, the primary extracted feature is checked, an index feature set with the feature intensity smaller than the feature intensity threshold, namely the feature to be strengthened, is determined, and the feature to be strengthened is identified.
Further, based on the data processing algorithm configured by the circulated functional layer, determining a data feature type adapted by the data processing algorithm, for example, an image feature strengthening algorithm, adapting to an image convolution feature, and taking a feature type suitable for the functional layer configuration algorithm as the adaptive feature type. And determining a data processing algorithm configured by each functional layer, determining a corresponding adaptive feature type, establishing mapping between the adaptive feature type and the multi-level functional layer, and determining the mapping as a feature constraint for functional layer data processing and the feature constraint as the hierarchical data processing constraint.
And screening the features to be enhanced in the once extracted features based on the adaptive feature types of the transferred functional layers, determining the identification target features meeting the adaptive feature types, and carrying out enhancement processing on the identification target features based on a configured data processing algorithm. And integrating the identification target features after the strengthening treatment with the rest index features, and flowing to a rear functional layer, and carrying out judgment screening of the feature intensity threshold value, screening of the adaptive feature types of the functional layer and index feature strengthening treatment again. And repeating the steps to sequentially finish the characteristic strengthening treatment of the multi-stage functional layer, finish the comprehensive strengthening treatment of extracting the index characteristic, ensure the pertinence treatment and completeness of the differentiated characteristic by carrying out the hierarchical selective strengthening treatment, ensure that the treated characteristic meets the characteristic strength requirement so as to improve the characteristic definition, facilitate the measurement of the index characteristic value and improve the determination accuracy of the index characteristic value.
The feature intensity threshold is used as a functional layer selective flow checkpoint for screening features to be enhanced, processing is only performed on features with enhancement necessity, and redundant feature data in a data processing process is reduced so as to improve processing efficiency. And taking the adaptive feature type as the hierarchical data processing constraint, and performing selective processing on the feature to be enhanced based on algorithm adaptation degree, namely performing enhancement processing on the index features meeting the adaptive feature type, and performing null stream processing on the index features not meeting the adaptive feature type, namely performing hierarchical stream of data directly without processing. The method comprises the steps of sequentially screening the primary and secondary checkpoints of the index features, accurately limiting the index features of the composite processing requirements, and carrying out algorithm targeted processing.
Further, performing hierarchical attribution and interlayer association mapping on the index features subjected to hierarchical processing, and obtaining the constructed index feature cascade network through cascade processing of the hierarchical index features, wherein the index feature cascade network is a relevant extraction feature which is accurately positioned based on the monitoring sensing data and corresponds to an evaluation index.
S4, based on the index feature cascade network, accurately positioning index feature values and generating a real control index matrix;
S5, based on the expected index matrix and the actual control index matrix, performing matrix differentiation measurement to determine a main evaluation result;
Traversing the index feature cascade network, performing feature matching of the evaluation indexes, determining at least one index feature corresponding to each evaluation index, and determining an index feature value of the evaluation index based on an index feature state. If the evaluation index corresponds to a plurality of index features, weight configuration is carried out based on the feature importance degree, and weighted summation of the plurality of index features is carried out to be used as an index feature value of the evaluation index. And carrying out the same-mode arrangement of the index characteristic values in the mode of the expected index matrix to generate the real control index matrix.
Further, mapping correspondence and differentiation measurement of matrix terms are carried out on the real control index matrix and the expected index matrix, and the main evaluation result is determined based on the differentiation index value. Illustratively, a plurality of individual evaluation results are determined based on the deviation index feature values of the respective evaluation indexes, wherein the individual evaluation results are positively correlated with the degree of deviation. And carrying out weighted average calculation on the plurality of single evaluation results to obtain the main evaluation result, wherein specific weight configuration accords with the index importance degree.
S6, based on the monitoring sensing data, evaluating the mechanical transmission performance of the equipment of the spiral instant freezer, and determining a secondary evaluation result;
And S7, determining the freezing evaluation effect of the spiral instant freezer based on the main evaluation result and the secondary evaluation result and combining cross correlation analysis.
Wherein, carry on the mechanical transmission performance assessment of the apparatus of the said spiral instant freezer, the application S6 also includes:
S61, identifying and screening brake source data based on the monitoring sensing data;
s62, reading production specification information of the spiral instant freezer, and determining effective braking characteristics;
S63, extracting actual braking characteristics based on the braking source data, checking the effective braking characteristics and determining the mechanical transmission state of the equipment;
and S64, configuring standard performance grades, and determining the secondary evaluation result based on the mechanical transmission state.
And further performance evaluation is performed with respect to the mechanical transmission dimension. Specifically, the transmission monitoring data is screened and extracted based on the monitoring sensing data and is used as the braking source data. And further reading production specification information of the spiral instant freezer, and directly identifying and determining based on a production work order, wherein based on the production specification information, braking characteristics of the spiral instant freezer in a standard running state, such as transmission accumulated deviation, mechanical stability, wind circulation smoothness and the like in an allowable range, are determined as the effective braking characteristics. And carrying out corresponding identification extraction in the braking source data by taking the effective braking characteristics as reference, and taking the effective braking characteristics as the actual braking characteristics.
And further mapping and checking the effective braking characteristics and the actual braking characteristics, and determining the mechanical transmission state of the equipment based on the characteristic deviation value, wherein the equipment mechanical performance is an additional evaluation dimension, and the characteristic extraction and the differential analysis are directly carried out on the equipment mechanical performance to improve the efficiency of the evaluation processing. The standard performance grade is a limit grade which is set by a person skilled in the art and used for measuring the mechanical transmission state of different equipment based on the operation regulation of the spiral instant freezer, the standard performance grade is traversed, the mechanical transmission state is matched, the matching grade is determined, and the characteristic deviation degree is combined as the secondary evaluation result. And integrating the main evaluation result and the secondary evaluation result, performing main and secondary correlation analysis, for example, performing correlation mapping on abnormal freezing effect caused by equipment transmission deviation, and obtaining the freezing evaluation effect of the spiral instant freezer, wherein the freezing evaluation result has high consistency with the running condition of the spiral instant freezer.
Wherein, the application also exists in S8, which comprises:
S81, configuring an alarm constraint threshold;
s82, if the freezing evaluation effect does not meet the alarm constraint threshold, carrying out abnormal warning and synchronously generating a man-machine interaction instruction, wherein the differential tracing information is additional output information;
And S83, transmitting the man-machine interaction instruction to a personnel mobile terminal, and performing operation regulation and control of the spiral instant freezer based on the differential traceability information.
And when the freezing evaluation effect does not reach the standard, the operation and maintenance management of the spiral instant freezer is required to be performed in time. Specifically, based on the operation and maintenance standard of the spiral instant freezer, the alarm constraint threshold, namely, the defined range for measuring the normal running state is configured, and when the alarm constraint threshold is located, the equipment is indicated to be in the normal running state. And if the freezing evaluation effect does not meet the alarm constraint threshold, indicating that the freezing effect of the equipment does not reach the standard, carrying out abnormal warning and synchronously generating the man-machine interaction instruction. And performing differential tracing based on the index feature cascade network of the main evaluation result and the feature deviation degree of the secondary evaluation result, determining the acquisition source of the deviation feature, taking the acquisition source as the differential tracing information, and synchronizing the man-machine interaction instruction for output. And determining a worker for carrying out operation and maintenance management of the spiral instant freezer, further transmitting the man-machine interaction instruction to the personnel mobile terminal, and carrying out operation regulation and control of the spiral instant freezer by taking the differential traceability information as a regulation and control basis, so as to ensure the freezing effect of equipment.
The intelligent assessment method for the freezing effect of the spiral instant freezer provided by the application has the following technical effects:
1. The prior art lacks a systematic complete evaluation mode, is not intelligent enough, and has insufficient analysis depth and completeness, so that the evaluation accuracy of the freezing effect is limited, and the transportation of subsequent equipment is limited. And (3) carrying out hierarchical characteristic reinforcement processing of freezing stage division and monitoring data by taking the freezing dimension and the mechanical transmission dimension as the principal and secondary directions of evaluation, precisely positioning real-time freezing characteristics, and carrying out comprehensive analysis by combining the transmission characteristics, so as to realize intelligent systematic evaluation analysis of the freezing effect of the equipment and ensure objective consistency of evaluation results.
2. And combining a feature extraction module with hierarchical functionality, taking a set feature intensity threshold and a data processing algorithm configured in a hierarchical manner as constraints, carrying out feature extraction and hierarchical adaptability selection strengthening processing on the monitored data, ensuring that the processing process is consistent with the requirements of feature extraction, executing feature strengthening processing to ensure the definition and accuracy of features, and improving the accuracy of freezing effect evaluation.
Example two
Based on the same inventive concept as the intelligent assessment method of the freezing effect of the spiral instant freezer in the foregoing embodiment, as shown in fig. 3, the application provides an intelligent assessment system of the freezing effect of the spiral instant freezer, which comprises:
A desired index matrix determining module 11, where the desired index matrix determining module 11 is configured to determine a desired index matrix for measuring a desired freezing effect of a frozen article based on a pre-control program set by an assembled programmable controller, and the desired index matrix identifies a tolerance interval based on a technical loss;
The sensing and monitoring module 12 is used for starting the spiral instant freezer and performing freezing control monitoring along with blanking of the target frozen article to determine monitoring sensing data, wherein a feeding transmission belt and a discharging transmission belt of the spiral instant freezer are connected with a production line;
The feature extraction module 13 is used for carrying out feature extraction based on the expected index matrix by combining the feature extraction module based on the monitoring sensing data, carrying out selective feature strengthening processing based on a configured hierarchical data processing algorithm, carrying out cascade analysis on interlayer features, and determining an index feature cascade network, wherein the index feature cascade network is in a pyramid structure;
The real control index matrix generation module 14 is used for accurately positioning index characteristic values and generating a real control index matrix based on the index characteristic cascade network;
the main evaluation result determining module 15 is configured to perform matrix differentiation measurement based on the expected index matrix and the actual control index matrix, to determine a main evaluation result;
The secondary evaluation result determining module 16 is used for performing mechanical transmission performance evaluation of the equipment of the spiral instant freezer based on the monitoring sensing data, and determining a secondary evaluation result;
The freezing evaluation effect determining module 17, wherein the freezing evaluation effect determining module 17 is used for determining the freezing evaluation effect of the spiral instant freezer based on the primary evaluation result and the secondary evaluation result and combining cross correlation analysis.
Wherein the expected index matrix determining module 11 further includes:
The freezing stage setting module is used for setting a plurality of freezing stages based on the pre-control program and the target frozen article, wherein the plurality of freezing stages at least comprise a liquid quick-freezing stage and a crystallization stage;
the characteristic value acquisition module is used for configuring evaluation indexes for measuring the freezing effect, traversing the plurality of freezing stages to determine expected characteristic values of the periodic indexes and acquiring a plurality of groups of characteristic values of the indexes;
the matrix building module is used for building a desired index matrix by taking the evaluation index as a matrix row, taking the plurality of freezing stages as matrix arrays and taking the plurality of groups of index characteristic values as distribution matrix items.
Wherein the feature extraction module 13 further comprises:
The structure analysis module is used for the feature extraction module and comprises a plurality of fully-connected network layers, wherein each network layer is configured with a data processing algorithm;
The data screening module is used for combining the data screening layer in the characteristic extraction module by taking the evaluation indexes as references to extract multiple source data mapped to each evaluation index;
and the index feature cascade network acquisition module is used for transferring the multiple source data streams into a rear multi-stage functional layer, performing feature extraction and hierarchical selectivity strengthening treatment, and performing cascade of index features based on hierarchical mapping to acquire the index feature cascade network.
Wherein, the index feature cascade network acquisition module further comprises:
The characteristic intensity threshold value configuration module is used for configuring a characteristic intensity threshold value for measuring the characteristic definition;
and the feature screening module to be reinforced is used for carrying out hierarchical processing on the feature strength threshold value serving as constraint layer by layer, and is characterized by a post functional layer selective circulation switch to screen the feature to be reinforced.
Wherein the system further comprises:
the feature type determining module is used for determining an adaptive feature type based on a data processing algorithm of network configuration;
the hierarchical data processing constraint determining module is used for mapping the adaptive feature types and the multi-level functional layers and is used as a hierarchical data processing constraint;
the hierarchical enhancement feature determination module is used for performing selective processing of the feature to be enhanced based on the hierarchical data processing constraint to determine the hierarchical enhancement feature, wherein the selective processing refers to enhancement processing or air flow processing.
Wherein the secondary evaluation result determination module 16 further includes:
The braking source data screening module is used for identifying and screening braking source data based on the monitoring sensing data;
The effective braking characteristic determining module is used for reading the production specification information of the spiral instant freezer and determining effective braking characteristics;
the equipment mechanical transmission state determining module is used for extracting actual braking characteristics based on the braking source data, checking the effective braking characteristics and determining equipment mechanical transmission states;
and the result determining module is used for configuring standard performance grades and determining the secondary evaluation result based on the mechanical transmission state.
Wherein the system further comprises:
the alarm constraint threshold configuration module is used for configuring an alarm constraint threshold;
the instruction generation module is used for carrying out abnormal warning and synchronously generating a man-machine interaction instruction if the freezing evaluation effect does not meet the alarm constraint threshold, wherein the differential tracing information is additional output information;
and the operation regulation and control module is used for transmitting the man-machine interaction instruction to the personnel mobile terminal and carrying out operation regulation and control on the spiral instant freezer based on the differential traceability information.
Through the foregoing detailed description of the intelligent assessment method for the freezing effect of the spiral instant freezer, those skilled in the art can clearly know the intelligent assessment method and system for the freezing effect of the spiral instant freezer in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The intelligent assessment method for the freezing effect of the spiral instant freezer is characterized by comprising the following steps of:
determining an expected index matrix for measuring expected freezing effects of target frozen articles based on a pre-control program set by an assembled programmable controller, wherein the expected index matrix identifies a tolerance interval based on technical loss;
Starting the spiral instant freezer and performing freezing parameter control monitoring along with blanking of the target frozen articles to determine monitoring sensing data, wherein a feeding conveying belt and a discharging conveying belt of the spiral instant freezer are connected with a production line;
based on the monitoring sensing data, combining a feature extraction module to perform feature extraction based on the expected index matrix, performing selective feature strengthening processing based on a configured hierarchical data processing algorithm, and performing cascade analysis on interlayer features to determine an index feature cascade network, wherein the index feature cascade network is in a pyramid structure;
Based on the index feature cascade network, accurately positioning index feature values and generating a real control index matrix, wherein the index feature cascade network is traversed, feature matching of evaluation indexes is carried out, at least one index feature corresponding to each evaluation index is determined, the index feature value of the evaluation index is determined based on index feature states;
based on the expected index matrix and the actual control index matrix, performing matrix differentiation measurement to determine a main evaluation result;
based on the monitoring sensing data, evaluating the mechanical transmission performance of the equipment of the spiral instant freezer, and determining a secondary evaluation result;
Determining the freezing evaluation effect of the spiral instant freezer by combining cross correlation analysis based on the main evaluation result and the secondary evaluation result;
Wherein the determining measures the expected index matrix of expected freezing effect of the target frozen article comprises:
setting a plurality of freezing stages based on the pre-control program and the target frozen article, wherein the plurality of freezing stages at least comprise a liquid quick freezing stage and a crystallization stage;
Configuring evaluation indexes for measuring freezing effects, traversing the plurality of freezing stages to determine expected characteristic values of the stage indexes, and obtaining a plurality of groups of index characteristic values;
And constructing a desired index matrix by taking the evaluation index as a matrix row, taking the plurality of freezing stages as a matrix array and taking the plurality of groups of index characteristic values as distribution matrix items.
2. The method of claim 1, wherein the combined feature extraction module performs a hierarchical feature extraction mapping and cascade analysis based on the desired index matrix, the method comprising:
the feature extraction module comprises a plurality of fully-connected network layers, wherein each network layer is configured with a data processing algorithm;
extracting a plurality of source data mapped to each evaluation index by taking the evaluation index as a reference and combining a data screening layer in the characteristic extraction module;
And transferring the multiple source data streams to a rear-mounted multi-stage functional layer, performing feature extraction and hierarchical selectivity enhancement processing, and cascading index features based on hierarchical mapping to obtain the index feature cascading network.
3. The method of claim 2, wherein the feature extraction and hierarchical selective enhancement process is performed, the method comprising:
Configuring a characteristic intensity threshold for measuring characteristic definition;
And taking the characteristic intensity threshold value as constraint, carrying out hierarchical processing on the functional layers, wherein the hierarchical processing is characterized in that the post functional layer selectively flows to the card, and the characteristics to be reinforced are screened.
4. A method as claimed in claim 3, characterized in that the method comprises:
determining an adaptive feature type based on a data processing algorithm of network configuration;
Mapping the adaptive feature type and the multi-level functional layer as a hierarchical data processing constraint;
and based on the hierarchical data processing constraint, carrying out selective processing of the feature to be enhanced, and determining the hierarchical enhancement feature, wherein the selective processing refers to enhancement processing or air flow processing.
5. The method of claim 1, wherein the assessment of the mechanical transmission performance of the equipment of the spiral freezer is performed, the method comprising:
Identifying and screening brake source data based on the monitoring sensing data;
reading production specification information of the spiral instant freezer, and determining effective braking characteristics;
Extracting actual braking characteristics based on the braking source data, checking the effective braking characteristics and determining the mechanical transmission state of the equipment;
and configuring a standard performance grade, and determining the secondary evaluation result based on the mechanical transmission state.
6. The method of claim 1, characterized in that the method comprises:
Configuring an alarm constraint threshold;
If the freezing evaluation effect does not meet the alarm constraint threshold, carrying out abnormal warning and synchronously generating a man-machine interaction instruction, wherein the differential tracing information is additional output information;
And transmitting the man-machine interaction instruction to a personnel mobile terminal, and performing operation regulation and control of the spiral instant freezer based on the differential traceability information.
7. The intelligent assessment system for the freezing effect of the spiral instant freezer is characterized by being used for executing the intelligent assessment method for the freezing effect of the spiral instant freezer, which is characterized by comprising the following steps:
the system comprises an expected index matrix determining module, a target freezing object determining module and a target freezing object determining module, wherein the expected index matrix determining module is used for determining an expected index matrix for measuring expected freezing effects of target frozen objects based on a pre-control program set by an assembled programmable controller, and the expected index matrix identifies a tolerance interval based on technical loss;
The sensing monitoring module is used for starting the spiral instant freezer along with blanking of the target frozen article and performing freezing parameter control monitoring to determine monitoring sensing data, wherein a feeding conveying belt and a discharging conveying belt of the spiral instant freezer are connected with a production line;
the feature extraction module is used for carrying out feature extraction based on the expected index matrix by combining with the feature extraction module based on the monitoring sensing data, carrying out selective feature strengthening processing based on a configured hierarchical data processing algorithm, carrying out cascade analysis on interlayer features, and determining an index feature cascade network, wherein the index feature cascade network is of a pyramid structure;
the system comprises an actual control index matrix generation module, a weight configuration module, a real control index matrix generation module, a weight configuration module and a weight configuration module, wherein the actual control index matrix generation module is used for precisely positioning index characteristic values based on the index characteristic cascade network and generating an actual control index matrix, wherein the index characteristic cascade network is traversed, characteristic matching of the evaluation indexes is carried out, at least one index characteristic corresponding to each evaluation index is determined, and the index characteristic value of the evaluation index is determined based on the index characteristic state;
The main evaluation result determining module is used for carrying out matrix differentiation measurement based on the expected index matrix and the actual control index matrix to determine a main evaluation result;
The secondary evaluation result determining module is used for evaluating the mechanical transmission performance of the equipment of the spiral instant freezer based on the monitoring sensing data and determining a secondary evaluation result;
And the freezing evaluation effect determining module is used for determining the freezing evaluation effect of the spiral instant freezer based on the main evaluation result and the secondary evaluation result and combining cross correlation analysis.
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| CN117146523A (en) * | 2023-10-31 | 2023-12-01 | 南通市埃姆福制冷科技有限公司 | Freezing parameter control method and system of spiral instant freezer |
| CN117664218A (en) * | 2023-10-20 | 2024-03-08 | 北京市计量检测科学研究院 | Calibration method of vacuum freeze dryer |
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| CN102620517A (en) * | 2012-03-25 | 2012-08-01 | 金春 | Refrigerating system and device based on internet-of-things technology |
| EP3309486A1 (en) * | 2016-10-11 | 2018-04-18 | Liebherr-Hausgeräte Ochsenhausen GmbH | Refrigeration and/or freezer device |
| CN118274554B (en) * | 2024-05-31 | 2024-10-11 | 南通市埃姆福制冷科技有限公司 | Intelligent control method and system for spiral refrigerator |
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| CN117664218A (en) * | 2023-10-20 | 2024-03-08 | 北京市计量检测科学研究院 | Calibration method of vacuum freeze dryer |
| CN117146523A (en) * | 2023-10-31 | 2023-12-01 | 南通市埃姆福制冷科技有限公司 | Freezing parameter control method and system of spiral instant freezer |
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