CN115953099B - Digital twin processing system and method based on multidimensional data and data analysis - Google Patents
Digital twin processing system and method based on multidimensional data and data analysis Download PDFInfo
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Abstract
The invention discloses a digital twin processing system and a digital twin processing method based on multidimensional data and data analysis, and belongs to the technical field of data processing; the targets are monitored and data counted from different dimensions, the attribute coefficients are obtained by integrating various data of different dimensions obtained by the monitored targets, the targets are integrally represented based on the attribute coefficients, and meanwhile, the importance degree of the targets is evaluated and classified according to the attribute coefficients, so that the differential analysis of the targets can be realized, and reliable data support can be provided for the influence evaluation of different targets on different process areas of a transfer center; the method and the device are used for solving the technical problems that in the existing scheme, data integration and analysis of different dimensions are not implemented on the target, and the monitored data and analysis results are mined and expanded to implement deeper digital twin processing, so that the overall effect of the digital twin processing is poor.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a digital twin processing system and method based on multidimensional data and data analysis.
Background
Digital twinning is a process of digitally defining and modeling the composition, characteristics, functions, and performance of a physical entity using information technology. Digital twinning is a technique, process, method, digital twinning is an object, model and data.
The existing digital twin processing scheme of the logistics has certain defects when being implemented, data monitoring and processing of different dimensions are not implemented on the logistics targets in the early stage, differential representation and analysis cannot be implemented on the targets, real-time evaluation and classification of influences on the region where the targets are located are not implemented in the digital twin processing process, and the technical problem of poor overall effect of the digital twin processing is caused.
Disclosure of Invention
The invention aims to provide a digital twin processing system and a digital twin processing method based on multidimensional data and data analysis, which are used for solving the technical problem that the overall effect of digital twin processing is poor because the existing scheme does not integrate and analyze data with different dimensions on a target and dig and expand the monitored data and analysis results to implement deeper digital twin processing.
The aim of the invention can be achieved by the following technical scheme:
a digital twin processing system based on multidimensional data and data analysis comprises a target monitoring module and a transfer supervision module; the target monitoring module is used for carrying out attribute statistics and data integration on the target to be transported to obtain target monitoring data comprising attribute coefficients, corresponding common attribute tags, important attribute tags, valuable attribute tags and attribute data;
the transfer supervision module is used for positioning the transport carrier of the target in real time, and evaluating and dynamically prompting the abnormality existing in the transport process by combining the target monitoring data; comprising the following steps:
marking the time point when the target arrives at the transit center flow area as first time, and marking the time point when the target leaves the flow area as second time; acquiring static time JS and moving time YS of the target according to the first time and the second time;
counting the total number of the transferring targets between the first time and the second time, and acquiring corresponding transferring fluctuation time length according to the total number of the transferring targets
The method comprises the steps of carrying out a first treatment on the surface of the Extracting the numerical values of all the time lengths of the marks, integrating the numerical values in parallel, and calculating and acquiring transfer values corresponding to different flow areas of the target in the transfer center through a transfer formula>
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the transport value +.>
The transport formula of (2) is:
wherein z1, z2 and z3 are different proportionality coefficients, and z3 is more than 1 and less than z2 and less than z1;
attribute coefficients for the target;
when the transfer state of the target is evaluated according to the transfer value, the transfer value is matched with a preset transfer range, and a first transfer signal, a second transfer signal, a third transfer signal, a corresponding normal target, a first selected target and a second selected target are obtained; and carrying out different levels of alarm prompts on coordinates of the first selected target and the second selected target.
Preferably, the working steps of the target monitoring module include:
when the attribute of the target is counted, the attribute type and the corresponding attribute type weight SQ of the target are obtained; acquiring the weight MZ and the volume MT of the target;
acquiring a transportation type of a target and a corresponding transportation type weight YQ;
acquiring a target reserve price BJ;
and arranging and combining the attribute type weight, volume, transportation type weight and warranty price of the target mark according to a preset sequence to obtain attribute data.
Preferably, when the attribute data is integrated, the values of the attribute type weight, the volume, the transportation type weight and the warranty price of the target mark are extracted and integrated in parallel, and the attribute coefficient of the target is obtained through calculation
The method comprises the steps of carrying out a first treatment on the surface of the Attribute coefficient->
The calculation formula of (2) is as follows:
wherein g1, g2, g3 and g4 are preset different ratio coefficients, and g2 is more than 0 and less than 1, g3 is more than 0 and less than 1 and less than g4; when classifying the targets according to the attribute coefficients, matching the attribute coefficients of the targets with a preset attribute threshold value to obtain common attribute tags, important attribute tags or valuable attribute tags.
Preferably, if the attribute coefficient is smaller than the attribute threshold, generating a first attribute signal and associating the corresponding target with a common attribute tag; if the attribute coefficient is not less than Y which belongs to the threshold value and is not greater than the attribute threshold value, Y is a real number which is greater than one hundred, generating a second attribute signal and associating the corresponding target with an important attribute label; if the attribute coefficient is greater than Y of the attribute threshold, generating a third attribute signal and associating the corresponding target with the valuable attribute label.
Preferably, if the transfer value is less than the minimum value of the transfer range, generating a first transfer signal and marking the corresponding target as a normal target;
if the transfer value is not smaller than the minimum value of the transfer range and not larger than the maximum value of the transfer range, generating a second transfer signal and marking the corresponding target as a first selected target;
if the translocation value is greater than the maximum value of the translocation range, a third translocation signal is generated and the corresponding target is marked as a second selected target.
Preferably, the coordinates of the first selected target are obtained, a first-level alarm prompt is carried out, and the total number of slight anomalies in the area of the first selected target is increased by one; acquiring coordinates of a second selected target, carrying out secondary alarm prompt, and adding one to the total number of moderate anomalies in the area of the second selected target; the severity of the secondary alert cue is higher than the severity of the primary alert cue.
Preferably, the system further comprises a region optimization module for dynamically prompting the transfer states of different process regions of the transfer center according to the transfer supervision data of the target.
Preferably, before evaluating the transfer states of different process areas of the transfer center, sequentially counting the total number QZ of mild anomalies and the total number ZZ of moderate anomalies which occur in the different process areas according to a preset arrangement sequence; acquiring a transfer function corresponding to a flow area and a corresponding function weight ZQ; extracting the numerical value parallel vertical integration of the light abnormal total number and the medium abnormal total number of the regional marks and the corresponding functional weights, and obtaining a flow area through calculationTransport coefficients corresponding to domains
。
Preferably, the transport coefficient
The calculation formula of (2) is as follows:
wherein, f1 and f2 are preset different proportion coefficients, and f1 is more than 1 and less than f2;
when the transfer states of different process areas of the transfer center are evaluated according to the transfer coefficients, the transfer coefficients are matched with a preset transfer lower limit threshold and a transfer upper limit threshold, state evaluation data comprising transfer coefficients corresponding to a plurality of areas, a transfer normal label, a transfer mild abnormal label and a transfer moderate abnormal label are obtained, and an alarm prompt and a management prompt are generated according to the transfer mild abnormal label and the transfer moderate abnormal label in the state evaluation data.
In order to solve the problem, the invention also discloses a digital twin processing method based on multidimensional data and data analysis, which comprises the following steps:
carrying out attribute statistics and data integration on the targets to be transported to obtain attribute data corresponding to the targets, carrying out data integration on the attribute data to obtain corresponding attribute coefficients, and analyzing the attribute coefficients to obtain target monitoring data comprising common attribute tags, important attribute tags and valuable attribute tags;
carrying out real-time positioning on a transport carrier of a target, and evaluating and dynamically prompting the abnormality existing in the transport process by combining target monitoring data to obtain transport supervision data comprising a transport value and corresponding normal targets, first selected targets and mild abnormal total numbers and second selected targets and moderate abnormal total numbers;
and dynamically prompting the transfer states of different process areas of the transfer center according to the transfer supervision data of the target.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the target is monitored and data statistics are carried out from different dimensions, so that the monitoring effect of the target can be effectively improved, and data support from different dimensions can be provided for the analysis of the subsequent target; by integrating various data of different dimensions acquired by the monitoring target to obtain attribute coefficients, the target is integrally represented based on the attribute coefficients, and meanwhile, the importance degree of the target is evaluated and classified according to the attribute coefficients, so that the differential analysis of the target can be realized, and reliable data support can be provided for the influence evaluation of different targets on different process areas of the transfer center.
According to the invention, the corresponding transfer values are obtained by simultaneous integration of various time durations of the target in different process areas of the transfer center, and the transfer states of the target in different process areas are evaluated and counted in real time by the transfer values, so that the specific position and the specific state of the target in the transfer center can be intuitively obtained, the data support of the target in the aspect of overall evaluation and control of the subsequent process areas can be provided, and the overall effect of the target in the aspect of monitoring data processing is effectively improved.
According to the invention, the transfer coefficients are obtained by integrating the abnormal target numbers with different degrees in different flow areas of the transfer center, the transfer states of the different flow areas are integrally evaluated and classified according to the transfer coefficients, and dynamic alarm prompt can be carried out according to the classification result, so that a manager can timely and efficiently find and process the abnormality existing in the different flow areas, the mining and expansion of target monitoring data are realized, and the integral effect of digital twin processing of the target can be effectively improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a digital twin processing system based on multidimensional data and data analysis in accordance with the present invention.
FIG. 2 is a block flow diagram of a digital twin processing method based on multidimensional data and data analysis in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the invention is a digital twin processing system based on multidimensional data and data analysis, comprising a target monitoring module, a transfer supervision module, a database and a cloud platform;
digital twinning is a virtual copy of a physical product, process, or system. It collects real-time data about physical items by using sensors, acts as a bridge between the physical world and the digital world, and then uses these data to create a digital copy of the items so that it can be understood, analyzed, manipulated or optimized.
The application scene in the embodiment of the invention is mainly the whole process of monitoring and analyzing and controlling the transportation center of the logistics, and can be expanded to the front end and the rear end of the logistics in the actual application process, and the real-time states of different types of targets are monitored and analyzed to provide data support for evaluating and controlling the states of different process areas of the transportation center, so that the overall effects of digital twin processing and multidimensional data analysis of the logistics transportation center can be effectively achieved.
The target monitoring module is used for carrying out attribute statistics and data integration on the target to be transported, and comprises the following components:
when the attribute of the target is counted, the attribute type of the target is obtained, different attribute types are set to correspond to different attribute type weights, the obtained attribute type is matched with all the attribute types prestored in the database to obtain corresponding attribute type weights, and the corresponding attribute type weights are marked as SQ;
the object can be an object needing express delivery, and the attribute type can be the conventional type of the existing express delivery, including but not limited to clothes, bags, shoes, jewelry hanging, books, furniture, children toys, plug-in household appliances and the like; the purpose of acquiring the attribute type weight corresponding to the attribute type of the target is to implement digital processing on the attribute type of the text class, so that reliable data support can be provided for attribute evaluation, classification and differentiation analysis of the subsequent target; the specific value of the attribute type weight can be customized according to experience;
acquiring the weight and the volume of the target and marking as MZ and MT respectively; the weight is in kilograms and the volume is in cubic centimeters;
the method comprises the steps of obtaining a transportation type of a target, matching the transportation type with a pre-stored transportation type-weight table in a database to obtain a corresponding transportation type weight, and marking the transportation type weight as YQ;
the transportation type-weight table comprises a plurality of different transportation types and corresponding transportation type weights, wherein the different transportation types are preset with one corresponding transportation type weight, and the specific numerical value of the transportation type weight can be customized according to experience; transportation types include, but are not limited to, the current day of the day, the next day of the day, the express mail, and the offer;
acquiring the warranty price of the target and marking the warranty price as BJ; the unit of the price is the element;
the attribute type weight, volume, transportation type weight and warranty price of the target mark are arranged and combined according to a preset sequence to obtain attribute data;
in the embodiment of the invention, the target is monitored and data is counted from different dimensions, so that the monitoring effect of the target can be effectively improved, and data support of different dimensions can be provided for analysis of subsequent targets.
When the attribute data are integrated, the values of the attribute type weight, the volume, the transportation type weight and the warranty price of the target mark are extracted and integrated in parallel, and the attribute coefficient of the target is obtained through calculation
The method comprises the steps of carrying out a first treatment on the surface of the Attribute coefficient->
The calculation formula of (2) is as follows:
wherein g1, g2, g3 and g4 are preset different ratio coefficients, and g2 is more than 0 and less than 1, g3 is more than 0 and less than 1 and less than g4; g1 may take a value of 1.869, g2 may take a value of 0.527, g3 may take a value of 1.204, and g4 may take a value of 2.316;
it should be noted that, the attribute coefficient is a numerical value for integrating different dimension data of the target to integrally evaluate the attribute importance degree thereof; the larger the attribute coefficient is, the larger the importance of the corresponding target is, and the larger the generated influence is;
when classifying targets according to attribute coefficients, matching the attribute coefficients of the targets with preset attribute thresholds;
if the attribute coefficient is smaller than the attribute threshold, generating a first attribute signal and associating the corresponding target with a common attribute label; if the attribute coefficient is not less than Y which belongs to the threshold value and is not greater than the attribute threshold value, Y is a real number which is greater than one hundred, generating a second attribute signal and associating the corresponding target with an important attribute label; if the attribute coefficient is greater than Y of the attribute threshold value, generating a third attribute signal and associating the corresponding target with the valuable attribute label;
the attribute coefficients and the corresponding common attribute tags, important attribute tags, valuable attribute tags and attribute data form target monitoring data, and the target monitoring data are uploaded to a database and a cloud platform.
According to the embodiment of the invention, the attribute coefficients are obtained by integrating the various data of different dimensions acquired by the monitoring target, the target is integrally represented based on the attribute coefficients, and the importance degree of the target is evaluated and classified according to the attribute coefficients, so that the differential analysis of the target can be realized, and reliable data support can be provided for the influence evaluation of different targets on different flow areas of the transfer center.
The transfer supervision module is used for positioning the transport carrier of the target in real time, and evaluating and dynamically prompting the abnormality existing in the transport process by combining the target monitoring data; comprising the following steps:
marking the time point when the target arrives at the transit center flow area as first time, and marking the time point when the target leaves the flow area as second time; both the first time and the second time may be accurate to minutes; acquiring static time and moving time of a target according to the first time and the second time and marking the static time and the moving time as JS and YS respectively; the units of the static time length and the moving time length are all minutes;
counting the total number of the transferring targets between the first time and the second time, and acquiring corresponding transferring fluctuation time length according to the total number of the transferring targets and marking the transferring fluctuation time length as a mark; the unit of the transfer fluctuation time length is minutes, the transfer fluctuation time length corresponding to the total number of transfer targets can be obtained through historical transfer big data, and the transfer fluctuation time length plays a role in reducing errors;
extracting the numerical values of all the time lengths of the marks, integrating the numerical values in parallel, and calculating and acquiring transfer values corresponding to different flow areas of the target in the transfer center through a transfer formula
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the transport value +.>
The transport formula of (2) is:
wherein z1, z2 and z3 are different proportionality coefficients, and z3 is more than 1 and less than z2 and less than z1; z1 may take on a value of 3.204, z2 may take on a value of 2.527, and z3 may take on a value of 1.483;
in the embodiment of the invention, the sequencing flow area is composed of the existing conventional logistics area, including but not limited to a discharging area, a receiving scanning area, a sorting area, a conveying area, a discharging scanning area and a loading area;
it should be noted that the transfer value is a value for integrating each duration of different process areas of the target in the transfer center order to integrally evaluate the transfer state of the process area where the target is located; the larger the transfer value is, the more abnormal the transfer state of the corresponding target in the flow area is;
when evaluating the transfer state of the target according to the transfer value, matching the transfer value with a preset transfer range;
if the transfer value is smaller than the minimum value of the transfer range, judging that the transfer state of the corresponding target is normal, generating a first transfer signal, and marking the corresponding target as a normal target according to the first transfer signal;
if the transfer value is not smaller than the minimum value of the transfer range and not larger than the maximum value of the transfer range, judging that the transfer state of the corresponding target is slightly abnormal, generating a second transfer signal, marking the corresponding target as a first selected target according to the second transfer signal, simultaneously acquiring the coordinate of the first selected target, carrying out primary alarm prompt, and adding one to the total number of the slightly abnormal regions in the region of the first selected target;
if the transfer value is greater than the maximum value of the transfer range, judging that the transfer state of the corresponding target is moderately abnormal, generating a third transfer signal, marking the corresponding target as a second selected target according to the third transfer signal, simultaneously acquiring the coordinate of the second selected target, carrying out secondary alarm prompt, and adding one to the total number of moderately abnormal conditions in the area of the second selected target; the severity of the secondary alarm prompt is higher than the severity of the primary alarm prompt;
the transfer value, the corresponding normal target, the first selected target and the mild anomaly total number, and the second selected target and the moderate anomaly total number form transfer supervision data of the target and are uploaded to a database and a cloud platform.
In the embodiment of the invention, the corresponding transfer values are obtained by carrying out simultaneous integration on various time lengths of the targets in different process areas of the transfer center, and the transfer states of the targets in the different process areas are evaluated and counted in real time by the transfer values, so that the specific positions and the specific states of the targets in the transfer center can be intuitively obtained, the data support of the targets in terms of overall evaluation and management of subsequent process areas can be provided, and the overall effect of the targets in terms of monitoring data processing is effectively improved.
Example two
On the basis of the first embodiment, the method further comprises the following steps:
the region optimization module is used for dynamically prompting the transfer states of different process regions of the transfer center according to the transfer supervision data of the target; comprising the following steps:
before evaluating the transfer states of different process areas of the transfer center, sequentially counting the total number of slight anomalies and the total number of moderate anomalies which occur in the different process areas according to a preset arrangement sequence, and marking the total number of slight anomalies and the total number of moderate anomalies as QZ and ZZ respectively;
acquiring transfer functions corresponding to the flow area, setting different transfer functions to correspond to different function weights, matching the transfer functions corresponding to the acquired area with all transfer functions prestored in a database to acquire corresponding function weights, and marking the corresponding function weights as ZQ;
among other things, transfer functions include, but are not limited to, unloading, receiving scans, sorting, transporting, shipping scans, and loading; the specific numerical value of the function weight can be customized according to experience;
extracting the numerical value parallel vertical integration of the mild abnormal total number and the moderate abnormal total number of the regional marks and the corresponding functional weights, and obtaining the transfer coefficient corresponding to the flow region by calculating
Transport coefficient->
The calculation formula of (2) is as follows:
wherein, f1 and f2 are preset different proportion coefficients, and f1 is more than 1 and less than f2; f1 can take on a value of 1.347 and f2 can take on a value of 2.653;
the transfer coefficient is a value for integrating the number of abnormal targets with different degrees occurring in different process areas of the transfer center to integrally evaluate the transfer state thereof; the larger the transfer coefficient is, the more abnormal the transfer state of the corresponding flow area is;
when the transfer states of different process areas of the transfer center are evaluated according to the transfer coefficients, the transfer coefficients are matched with a preset transfer lower limit threshold value and a transfer upper limit threshold value;
if the transfer coefficient is smaller than the transfer lower limit threshold, judging that the transfer state of the corresponding flow area is normal and associating the transfer state with a transfer normal label;
if the transfer coefficient is not smaller than the transfer lower limit threshold and not larger than the transfer upper limit threshold, judging that the transfer state of the corresponding flow area is slightly abnormal and associating the transfer state with a transfer slightly abnormal label;
if the transfer coefficient is larger than the transfer upper limit threshold, judging that the transfer state of the corresponding flow area is moderately abnormal and associating the transfer state with a moderately abnormal label;
the transfer coefficients corresponding to the areas and the transfer normal labels, the transfer mild abnormal labels and the transfer moderate abnormal labels form state evaluation data;
when dynamically prompting the transfer of different process areas according to the state evaluation data, traversing the state evaluation data to obtain labels corresponding to the process areas;
if the label corresponding to the flow area is a transfer mild abnormal label, generating an alarm prompt;
and if the label corresponding to the flow area is a transit moderate abnormal label, generating a management and control prompt.
In the embodiment of the invention, the transfer coefficient is obtained by integrating the abnormal target numbers with different degrees in different flow areas of the transfer center, the transfer states of the different flow areas are integrally evaluated and classified according to the transfer coefficient, and dynamic alarm prompt can be carried out according to the classification result, so that a manager can timely and efficiently find and process the abnormality existing in the different flow areas, the mining and expansion of target monitoring data are realized, and the integral effect of digital twin processing of the target can be effectively improved;
in addition, the formulas related in the above are all formulas with dimensions removed and numerical values calculated, and are a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and the proportionality coefficient in the formulas and each preset threshold value in the analysis process are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data; the size of the scaling factor is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the scaling factor depends on the number of sample data and the corresponding processing coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
Example III
As shown in fig. 2, the present invention is a digital twin processing method based on multidimensional data and data analysis, comprising:
carrying out attribute statistics and data integration on the targets to be transported to obtain attribute data corresponding to the targets, carrying out data integration on the attribute data to obtain corresponding attribute coefficients, and analyzing the attribute coefficients to obtain target monitoring data comprising common attribute tags, important attribute tags and valuable attribute tags;
carrying out real-time positioning on a transport carrier of a target, and evaluating and dynamically prompting the abnormality existing in the transport process by combining target monitoring data to obtain transport supervision data comprising a transport value and corresponding normal targets, first selected targets and mild abnormal total numbers and second selected targets and moderate abnormal total numbers;
and dynamically prompting the transfer states of different process areas of the transfer center according to the transfer supervision data of the target.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. The digital twin processing system based on multidimensional data and data analysis is characterized by comprising a target monitoring module and a transfer supervision module; the target monitoring module is used for carrying out attribute statistics and data integration on the target to be transported to obtain target monitoring data comprising attribute coefficients, corresponding common attribute tags, important attribute tags, valuable attribute tags and attribute data; comprising the following steps:
when the attribute of the target is counted, the attribute type and the corresponding attribute type weight SQ of the target are obtained; acquiring the weight MZ and the volume MT of the target;
acquiring a transportation type of a target and a corresponding transportation type weight YQ;
acquiring a target reserve price BJ;
the attribute type weight, volume, transportation type weight and warranty price of the target mark are arranged and combined according to a preset sequence to obtain attribute data;
when the attribute data are integrated, the values of the attribute type weight, the volume, the transportation type weight and the warranty price of the target mark are extracted and integrated in parallel, and the attribute coefficient of the target is obtained through calculation
The method comprises the steps of carrying out a first treatment on the surface of the Attribute coefficient->
The calculation formula of (2) is as follows:
if the attribute coefficient is smaller than the attribute threshold, generating a first attribute signal and associating the corresponding target with a common attribute label; if the attribute coefficient is not less than Y which belongs to the threshold value and is not greater than the attribute threshold value, Y is a real number which is greater than one hundred, generating a second attribute signal and associating the corresponding target with an important attribute label; if the attribute coefficient is greater than Y of the attribute threshold value, generating a third attribute signal and associating the corresponding target with the valuable attribute label;
the transfer supervision module is used for positioning the transport carrier of the target in real time, and evaluating and dynamically prompting the abnormality existing in the transport process by combining the target monitoring data; comprising the following steps:
marking the time point when the target arrives at the transit center flow area as first time, and marking the time point when the target leaves the flow area as second time; acquiring static time JS and moving time YS of the target according to the first time and the second time;
counting the total number of the transferring targets between the first time and the second time, and acquiring corresponding transferring fluctuation time length according to the total number of the transferring targets
The method comprises the steps of carrying out a first treatment on the surface of the Extracting the numerical values of all the time lengths of the marks, integrating the numerical values in parallel, and calculating and acquiring transfer values corresponding to different flow areas of the target in the transfer center through a transfer formula>
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the transport value +.>
The transport formula of (2) is:
when the transfer state of the target is evaluated according to the transfer value, the transfer value is matched with a preset transfer range, and a first transfer signal, a second transfer signal, a third transfer signal, a corresponding normal target, a first selected target and a second selected target are obtained; and carrying out different levels of alarm prompts on coordinates of the first selected target and the second selected target.
2. The digital twin processing system based on multidimensional data and data analysis of claim 1, wherein if the diversion value is less than a minimum value of the diversion range, generating a first diversion signal and marking the corresponding target as a normal target;
if the transfer value is not smaller than the minimum value of the transfer range and not larger than the maximum value of the transfer range, generating a second transfer signal and marking the corresponding target as a first selected target;
if the translocation value is greater than the maximum value of the translocation range, a third translocation signal is generated and the corresponding target is marked as a second selected target.
3. The digital twin processing system based on multidimensional data and data analysis of claim 1, wherein the coordinates of the first selected object are obtained and a first level of alert is presented, and the total number of mild anomalies occurring in the area of the first selected object is increased by one; acquiring coordinates of a second selected target, carrying out secondary alarm prompt, and adding one to the total number of moderate anomalies in the area of the second selected target; the severity of the secondary alert cue is higher than the severity of the primary alert cue.
4. A digital twin processing system based on multidimensional data and data analysis as defined in claim 3, further comprising a zone optimization module for dynamically prompting the diversion status of different process zones of the diversion center based on diversion regulatory data of the target.
5. The digital twin processing system based on multidimensional data and data analysis according to claim 4, wherein the total number QZ of mild anomalies and the total number ZZ of moderate anomalies in different flow areas are counted in sequence according to a preset arrangement sequence before evaluating the transfer states of the different flow areas of the transfer center; acquiring a transfer function corresponding to a flow area and a corresponding function weight ZQ; extracting the numerical value parallel vertical integration of the mild abnormal total number and the moderate abnormal total number of the regional marks and the corresponding functional weights, and obtaining the transfer coefficient corresponding to the flow region by calculating
。
6. The digital twin processing system based on multidimensional data and data analysis of claim 5, wherein the transport coefficients
The calculation formula of (2) is as follows:
when the transfer states of different process areas of the transfer center are evaluated according to the transfer coefficients, the transfer coefficients are matched with a preset transfer lower limit threshold and a transfer upper limit threshold, state evaluation data comprising transfer coefficients corresponding to a plurality of areas, a transfer normal label, a transfer mild abnormal label and a transfer moderate abnormal label are obtained, and an alarm prompt and a management prompt are generated according to the transfer mild abnormal label and the transfer moderate abnormal label in the state evaluation data.
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