CN116124268A - Electronic truck scale calibration method based on big data - Google Patents
Electronic truck scale calibration method based on big data Download PDFInfo
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- CN116124268A CN116124268A CN202310357032.6A CN202310357032A CN116124268A CN 116124268 A CN116124268 A CN 116124268A CN 202310357032 A CN202310357032 A CN 202310357032A CN 116124268 A CN116124268 A CN 116124268A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G23/00—Auxiliary devices for weighing apparatus
- G01G23/01—Testing or calibrating of weighing apparatus
- G01G23/012—Testing or calibrating of weighing apparatus with load cells comprising in-build calibration weights
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses an electronic truck scale calibration method based on big data, which relates to the technical field of electronic truck scale calibration and is used for solving the problems that the existing electronic truck scale calibration is mostly calibrated regularly and cannot be calibrated in time according to the use condition of the electronic truck scale calibration, so that more weighing error results exist in weighing the electronic truck scale and the use is influenced; the system is applied to a big data platform, wherein the big data platform comprises a data acquisition module, a database and a data analysis module; according to the invention, the truck scale information of the electronic truck scale is acquired and processed to obtain the weighed times, the average interval duration, the threshold super-total value and the bench integral value, the calibration base value of the electronic truck scale is further output through the model, and when the calibration base value is larger than the set threshold value, the electronic truck scale is calibrated through the calibration module, so that the electronic truck scale is reasonably calibrated according to the use condition.
Description
Technical Field
The invention relates to the technical field of electronic truck scale calibration, in particular to an electronic truck scale calibration method based on big data.
Background
Along with the development of science and technology, the electronic truck scale also has been developed rapidly, and it has simple structure, installation and debugging are convenient, weigh swiftly, show directly perceived to have abundant interface expansion function, remote transmission data, and computer networking unifies advantages such as carrying out enterprise's thing flow management. As a measuring device for trade settlement, the accuracy of the weighing result may directly affect the final result of the measuring process, and thus, it is necessary to know factors affecting accurate weighing of the electronic automobile.
The existing electronic truck scale is calibrated regularly, so that the electronic truck scale cannot be calibrated timely according to the use condition of the electronic truck scale, and the electronic truck scale is weighed, so that more weighing error results exist, and the use is affected.
Disclosure of Invention
The invention aims to solve the problems that the existing electronic truck scale is calibrated regularly, and the electronic truck scale cannot be calibrated in time according to the use condition of the electronic truck scale, so that the weighing of the electronic truck scale has more weighing error results and the use is affected.
The aim of the invention can be achieved by the following technical scheme: an electronic truck scale calibration method based on big data, the method comprising:
collecting truck scale information of an electronic truck scale;
processing the truck scale information to obtain the weighed times, average interval duration, threshold super total value and bench integral value, substituting the values into a preset model to output a calibration value of the electronic truck scale;
when the calibration value is larger than the set threshold value, generating a calibration signal and sending the calibration signal to a calibration module corresponding to the electronic truck scale;
after the calibration module receives the calibration signaling, the electronic truck scale is calibrated, specifically:
firstly, conveying the weight conveying unit to a position corresponding to the electronic truck scale;
secondly, sending a standard electronic automobile balance calibration weight to a corresponding position of the electronic automobile balance, weighing the calibration weight by a weighing sensor in the electronic automobile balance to obtain a weight weighing value, and sending the weight weighing value to a display instrument; repeating for a plurality of times;
finally, after receiving all weight weighing values, the display instrument removes the maximum value and the minimum value from all weight weighing values, calculates the average value of the rest weight weighing values to obtain an average weighing value, compares the average weighing value with the standard value of the calibration weight, calculates an error, and the display unit performs weighing calibration on the electronic automobile scale through the error.
As a preferred embodiment of the present invention, the specific process of processing the truck scale information is:
acquiring a calibration time closest to the current time, and if no calibration time exists, acquiring the initial use time of the truck scale; marking the calibration time closest to the current time or the first use time of the truck scale as a first time;
screening weighing data between a first moment and a current moment, and processing the screened weighing data, wherein the weighing data comprises the following specific steps: counting the number of weighing values and marking the number as the weighed times; sequencing weighing moments corresponding to the weighing values according to the sequence, calculating moment differences between two adjacent weighing moments to obtain interval duration, and calculating average values of all interval duration to obtain average interval duration; comparing all weighing values with a set threshold value, and marking the weighing values larger than the set threshold value as first values; setting a plurality of value ranges, wherein each value range corresponds to a threshold value exceeding value, matching the first numerical value with the plurality of value ranges to obtain a corresponding threshold value exceeding value, and summing all matched threshold value exceeding values to obtain a threshold value exceeding total value; and analyzing the weighing table data to obtain a table integer.
As a preferred embodiment of the present invention, the specific process of analyzing the weight data is:
dividing the surface image of the weighing platform into a plurality of divided pictures, amplifying each divided picture by a plurality of times to form a pixel grid image, carrying out color recognition on each pixel grid in the pixel grid image, comparing the recognized color with a preset color, marking the pixel grid as different Yan Ge when the color is inconsistent with the preset color, and counting the number of different Yan Ge to obtain a pixel grid image which is larger than the total number of different colors; summing the total number of different colors corresponding to all the divided pictures to obtain total number of different colors, and multiplying the total number of different colors by a preset conversion coefficient to convert the total number of different colors to obtain a total value of different colors;
calculating the difference value between the distance value of the same column in the distance measurement data of the weighing platform and a preset distance threshold value to obtain a distance deviation value, summing the distance deviation values to obtain a distance total value of the same column, substituting the value of the extracted distance deviation value into a line diagram according to the corresponding position sequence, marking the point of the distance deviation value in the line diagram as a deviation point, connecting two adjacent deviation points to obtain a deviation line, calculating the slope of the deviation line, taking the absolute value, summing the slopes of all the deviation lines, and taking the value to obtain a deflection total value; respectively summing the deflection total values and the distance total values of all columns, and multiplying the deflection total values and the distance total values by a preset conversion coefficient to obtain a total deflection value and a total difference value;
and drawing circles respectively by taking numerical values corresponding to the different total values, the total deviation values and the total difference values as radiuses, placing the circle centers of the three circles on the same vertical line, sequentially and equidistantly distributing the circle centers, connecting the outer circles of the three circles at intervals, constructing a table body, calculating the volume of the table body, extracting the numerical values of the volume, and marking the numerical values as table integral values.
As a preferred embodiment of the present invention, a signaling processing unit and a bench analysis unit are further disposed in the calibration module;
the signaling processing unit is used for processing the calibration signaling, and the specific processing process is as follows: analyzing the calibration signaling to obtain a bench integral value, the number of the electronic automobile scale and a calibration instruction; comparing the bench integral value with a preset bench integral threshold, and generating a bench instruction and sending the bench instruction to a bench integral analysis unit when the bench integral value is smaller than the preset bench integral threshold;
the whole table analysis unit is used for receiving the table position instruction and analyzing and processing to obtain whole table personnel, generating whole table processing labels and sending the whole table processing labels to intelligent terminals of the whole table personnel, and after the whole table personnel receives the whole table processing labels through the intelligent terminals, the whole table personnel reach corresponding positions of the corresponding electronic truck scales and clean the weighing table surfaces of the electronic truck scales.
As a preferred embodiment of the present invention, the specific process of analyzing the instructions at the stage by the stage analysis unit is:
after receiving the instruction of the platform, acquiring and processing cleaning information of cleaning personnel corresponding to the electronic truck scale;
sending a cleaning request to a cleaning person, receiving a request information result fed back by the cleaning person within a preset time range, and recording the feedback moment of the cleaning person; when the result of the request information is that the processing and positioning of the position are allowed, marking the cleaning personnel as the optimizing personnel;
calculating the time difference between the feedback time of the optimal personnel and the time of sending the cleaning request to obtain the response time of the optimal personnel; calculating the distance between the positioning position and the position of the electronic truck scale to obtain a cleaning distance;
extracting and processing the values of the response time length, the cleaning interval and the cleaning value to obtain a bench sorting value TZ of the optimizing personnel; sorting the priority personnel according to the table sorting value TZ from large to small, marking the priority personnel with the largest sorting as the table sorting personnel, generating a prompt tag and feeding back the prompt tag to a mobile phone terminal of the priority personnel; when the priority personnel feed back the cancel instruction within the preset time range, marking the priority personnel with ordered orders as the whole personnel and generating a prompt label to feed back the prompt label to the mobile phone terminal of the priority personnel, and so on, and simultaneously, the total cancel times qx of the priority personnel feed back the cancel instruction is increased once.
As a preferred embodiment of the present invention, a clear analysis unit is further disposed in the calibration module; the cleaning and analyzing unit is used for collecting and analyzing the time when the cleaning of the electronic truck scale is completed by the whole personnel, and the specific analysis process is as follows: calculating the time difference between the cleaning completion time and the time when the whole processing label is received to obtain the processing completion time of the whole personnel, extracting the value of the processing completion time and dividing the value by the cleaning interval to obtain a time interval ratio, and counting the number of all the processing completion time of the whole personnel to obtain the total processing times; and summing all the time-distance ratios, taking an average value to obtain a time-distance average value, and carrying out normalization processing on the time-distance average value, the total processing times and the total cancelling times to obtain a clear value of the whole staff.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the truck scale information of the electronic truck scale is acquired and processed to obtain the weighed times, the average interval duration, the threshold super-total value and the bench integral value, the calibration base value of the electronic truck scale is further output through the model, and when the calibration base value is larger than the set threshold value, the electronic truck scale is calibrated through the calibration module, so that the electronic truck scale is reasonably calibrated according to the use condition.
2. According to the invention, the bench integral value of the electronic automobile scale is compared through the signaling processing unit, when the bench integral value is smaller than the preset bench integral threshold value, a bench command is generated, the bench command is analyzed and processed through the bench integral analysis unit to obtain a bench integral person, the weighing bench surface of the electronic automobile scale is cleaned through intelligent analysis by the corresponding bench integral person, the cleaning efficiency of the weighing bench surface of the electronic automobile scale is improved, meanwhile, the weighing bench surface is cleaned timely, and the occurrence of dirt and potholes on the weighing bench surface, which causes the friction of the weighing bench, and influences weighing errors, are avoided.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Description of the embodiments
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
Examples
Referring to fig. 1, a big data based electronic truck scale calibration method is applied to a big data platform, wherein the big data platform comprises a data acquisition module, a database and a data analysis module;
the method comprises the steps of collecting truck scale information of an electronic truck scale through a data collecting module and sending the truck scale information to a database for storage; the truck scale information comprises the number, the position, the first use time of the truck scale, the model number, the calibration time, the weighing data and the weighing table data of the electronic truck scale; the weighing data comprise weighing values and corresponding weighing moments of the electronic truck scale; the weighing platform data comprise weighing platform surface images and ranging data of the weighing platform;
the truck scale information in the database is processed through the data analysis module to obtain a calibration value, and the method specifically comprises the following steps:
acquiring a calibration time closest to the current time, and if no calibration time exists, acquiring the initial use time of the truck scale; marking the calibration time closest to the current time or the first use time of the truck scale as a first time;
screening weighing data between a first moment and a current moment, and processing the screened weighing data, wherein the weighing data comprises the following specific steps: counting the number of weighing values and marking the number as the weighed times; sequencing weighing moments corresponding to the weighing values according to the sequence, calculating moment differences between two adjacent weighing moments to obtain interval duration, and calculating average values of all interval duration to obtain average interval duration; comparing all weighing values with a set threshold value, and marking the weighing values larger than the set threshold value as first values; setting a plurality of value ranges, wherein each value range corresponds to a threshold value exceeding value, matching the first numerical value with the plurality of value ranges to obtain a corresponding threshold value exceeding value, and summing all matched threshold value exceeding values to obtain a threshold value exceeding total value;
the weighing table data are analyzed to obtain a table integer value, specifically:
dividing the surface image of the weighing platform into a plurality of divided pictures, amplifying each divided picture by a plurality of times to form a pixel grid image, carrying out color recognition on each pixel grid in the pixel grid image, comparing the recognized color with a preset color, marking the pixel grid as different Yan Ge when the color is inconsistent with the preset color, and counting the number of different Yan Ge to obtain a pixel grid image which is larger than the total number of different colors; summing the total number of different colors corresponding to all the divided pictures to obtain total number of different colors, and multiplying the total number of different colors by a preset conversion coefficient to convert the total number of different colors to obtain a total value of different colors;
calculating the difference value between the distance value of the same column in the distance measurement data of the weighing platform and a preset distance threshold value to obtain a distance deviation value, summing the distance deviation values to obtain a distance total value of the same column, substituting the value of the extracted distance deviation value into a line diagram according to the corresponding position sequence, marking the point of the distance deviation value in the line diagram as a deviation point, connecting two adjacent deviation points to obtain a deviation line, calculating the slope of the deviation line, taking the absolute value, summing the slopes of all the deviation lines, and taking the value to obtain a deflection total value; respectively summing the deflection total values and the distance total values of all columns, and multiplying the deflection total values and the distance total values by a preset conversion coefficient to obtain a total deflection value and a total difference value;
the method comprises the steps of drawing circles respectively by taking numerical values corresponding to an abnormal total value, a total deviation value and a total difference value as radiuses to obtain three circles, placing circle centers of the three circles on the same vertical line, sequentially and equidistantly distributing the circles at intervals, connecting outer circles of the three circles to construct a table body, calculating the volume of the table body, extracting the numerical value of the volume, and marking the numerical value as a table integral value;
extracting the numerical values of the weighed times, the average interval duration, the threshold super-total value and the bench integral value, and marking the numerical values as YC1, PT2, CC3 and TZ4 in sequence; substituting into a preset model
Obtaining a correction value JZ of the electronic automobile scale, wherein qm1, qm2, qm3 and qm4 are all preset weight factors; mu is a correction factor and takes a value of 0.91;
when the calibration value is larger than the set threshold value, generating a calibration signal and sending the calibration signal to a calibration module corresponding to the electronic truck scale;
the calibration module comprises a driving unit, a weight unit, a scanning unit, a signaling processing unit, a bench analysis unit and a clear analysis unit;
the driving unit is used for conveying the weight conveying unit to the position corresponding to the electronic truck scale;
the weight unit is used for sending a standard electronic automobile balance calibration weight to a corresponding position of the electronic automobile balance, a weighing sensor in the electronic automobile balance is used for weighing the calibration weight to obtain a weight weighing value, and the weight weighing value is sent to the display instrument; repeating for a plurality of times;
after receiving all weight weighing values, the display instrument removes the maximum value and the minimum value of all weight weighing values, calculates the average value of the rest weight weighing values to obtain an average weighing value, compares the average weighing value with the standard value of a calibration weight, calculates an error, and the display unit performs weighing calibration on the electronic automobile scale through the error;
the scanning unit is used for scanning the surface of the electronic truck scale and collecting weighing table data.
The electronic automobile balance is calibrated through the calibration module when the calibration value is larger than a set threshold value, so that the electronic automobile balance is reasonably calibrated according to the use condition.
The signaling processing unit processes the calibration signaling, and the specific processing process is as follows: analyzing the calibration signaling to obtain a bench integral value, the number of the electronic automobile scale and a calibration instruction; comparing the bench integral value with a preset bench integral threshold, and generating a bench instruction and sending the bench instruction to a bench integral analysis unit when the bench integral value is smaller than the preset bench integral threshold;
the platform whole analysis unit receives the instructions at the platform and analyzes and processes the instructions to obtain platform whole personnel, and the platform whole analysis unit specifically comprises: after receiving the instruction of the platform, acquiring and processing cleaning information of cleaning personnel corresponding to the electronic truck scale; the cleaning information comprises the name, the mobile phone number and the cleaning value of the cleaning personnel;
sending a cleaning request to a cleaning person, receiving a request information result fed back by the cleaning person within a preset time range, and recording the feedback moment of the cleaning person; when the result of the request information is that the processing and positioning of the position are allowed, marking the cleaning personnel as the optimizing personnel;
calculating the time difference between the feedback time of the optimal personnel and the time of sending the cleaning request to obtain the response time of the optimal personnel; calculating the distance between the positioning position and the position of the electronic truck scale to obtain a cleaning distance;
extracting values of response time, cleaning interval and cleaning position value, and marking the values as XT1, GL2 and QC3 in sequence; setting preset weight factors corresponding to response time, cleaning interval and cleaning position value as ug1, ug2 and ug3, substituting the preset weight factors into a preset formula TZ=QC3×ug3/(XT1×ug1+GL2×ug2) to obtain a bench sorting value TZ of the optimizing personnel; sorting the priority personnel according to the table sorting value TZ from large to small, marking the priority personnel with the largest sorting as the table sorting personnel, generating a prompt tag and feeding back the prompt tag to a mobile phone terminal of the priority personnel; when the priority personnel feed back the cancel instruction within a preset time range, marking the priority personnel with ordered orders as the whole personnel and generating a prompt tag to feed back the prompt tag to a mobile phone terminal of the priority personnel, and so on, and simultaneously increasing the total cancel times qx of the priority personnel feed back the cancel instruction once;
generating a whole processing label and sending the whole processing label to an intelligent terminal of a whole person, and after the whole person receives the whole processing label through the intelligent terminal, the whole person reaches a corresponding position of the corresponding electronic truck scale and cleans the surface of a weighing platform of the electronic truck scale;
the cleaning and analyzing unit collects and analyzes the time when the whole personnel clean the electronic truck scale, and the specific analysis process is as follows: calculating the time difference between the cleaning completion time and the time when the whole processing label is received to obtain the processing completion time of the whole personnel, extracting the value of the processing completion time and dividing the value by the cleaning interval to obtain a time interval ratio, and counting the number of all the processing completion time of the whole personnel to obtain the total processing times; summing all time interval ratios, taking an average value to obtain a time interval average value, carrying out normalization processing on the time interval average value, the total processing times and the total cancellation times, and taking the numerical values of the time interval average value, the total processing times and the total cancellation times, and marking the numerical values as TK1, TK2 and TK3 respectively; substituting a preset formula
Obtaining a clear position value QC3 of the whole personnel; wherein vs1, vs2 and vs3 are preset weight factors, and yb is a preset time interval threshold.
The bench value of the electronic truck scale is compared through the signaling processing unit, when the bench value is smaller than the preset bench threshold value, a bench instruction is generated, the bench instruction is analyzed and processed through the bench analysis unit to obtain a bench person, the whole personnel corresponding to the weighing platform obtained through intelligent analysis clean the surface of the weighing platform of the electronic automobile scale, so that the cleaning efficiency of the surface of the weighing platform of the electronic automobile scale is improved, meanwhile, the surface of the weighing platform is cleaned timely, dirt and pits are prevented from being formed on the surface of the weighing platform, the friction of the weighing platform is caused, and weighing errors are influenced.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The electronic truck scale calibration method based on big data is characterized by comprising the following steps:
collecting truck scale information of an electronic truck scale;
processing the truck scale information to obtain the weighed times, average interval duration, threshold super total value and bench integral value, substituting the values into a preset model to output a calibration value of the electronic truck scale;
when the calibration value is larger than the set threshold value, generating a calibration signal and sending the calibration signal to a calibration module corresponding to the electronic truck scale;
after the calibration module receives the calibration signaling, the electronic truck scale is calibrated, specifically:
firstly, conveying the weight conveying unit to a position corresponding to the electronic truck scale;
secondly, sending a standard electronic automobile balance calibration weight to a corresponding position of the electronic automobile balance, weighing the calibration weight by a weighing sensor in the electronic automobile balance to obtain a weight weighing value, and sending the weight weighing value to a display instrument; repeating for a plurality of times;
finally, after receiving all weight weighing values, the display instrument removes the maximum value and the minimum value from all weight weighing values, calculates the average value of the rest weight weighing values to obtain an average weighing value, compares the average weighing value with the standard value of the calibration weight, calculates an error, and the display unit performs weighing calibration on the electronic automobile scale through the error.
2. The electronic truck scale calibration method based on big data according to claim 1, wherein the specific process of processing the truck scale information is:
acquiring a calibration time closest to the current time, and if no calibration time exists, acquiring the initial use time of the truck scale; marking the calibration time closest to the current time or the first use time of the truck scale as a first time;
screening weighing data between a first moment and a current moment, and processing the screened weighing data, wherein the weighing data comprises the following specific steps: counting the number of weighing values and marking the number as the weighed times; sequencing weighing moments corresponding to the weighing values according to the sequence, calculating moment differences between two adjacent weighing moments to obtain interval duration, and calculating average values of all interval duration to obtain average interval duration; comparing all weighing values with a set threshold value, and marking the weighing values larger than the set threshold value as first values; setting a plurality of value ranges, wherein each value range corresponds to a threshold value exceeding value, matching the first numerical value with the plurality of value ranges to obtain a corresponding threshold value exceeding value, and summing all matched threshold value exceeding values to obtain a threshold value exceeding total value; and analyzing the weighing table data to obtain a table integer.
3. The electronic truck scale calibration method based on big data according to claim 2, wherein the specific process of analyzing the weighing platform data is as follows:
dividing the surface image of the weighing platform into a plurality of divided pictures, amplifying each divided picture by a plurality of times to form a pixel grid image, carrying out color recognition on each pixel grid in the pixel grid image, comparing the recognized color with a preset color, marking the pixel grid as different Yan Ge when the color is inconsistent with the preset color, and counting the number of different Yan Ge to obtain a pixel grid image which is larger than the total number of different colors; summing the total number of different colors corresponding to all the divided pictures to obtain total number of different colors, and multiplying the total number of different colors by a preset conversion coefficient to convert the total number of different colors to obtain a total value of different colors;
calculating the difference value between the distance value of the same column in the distance measurement data of the weighing platform and a preset distance threshold value to obtain a distance deviation value, summing the distance deviation values to obtain a distance total value of the same column, substituting the value of the extracted distance deviation value into a line diagram according to the corresponding position sequence, marking the point of the distance deviation value in the line diagram as a deviation point, connecting two adjacent deviation points to obtain a deviation line, calculating the slope of the deviation line, taking the absolute value, summing the slopes of all the deviation lines, and taking the value to obtain a deflection total value; respectively summing the deflection total values and the distance total values of all columns, and multiplying the deflection total values and the distance total values by a preset conversion coefficient to obtain a total deflection value and a total difference value;
and drawing circles respectively by taking numerical values corresponding to the different total values, the total deviation values and the total difference values as radiuses, placing the circle centers of the three circles on the same vertical line, sequentially and equidistantly distributing the circle centers, connecting the outer circles of the three circles at intervals, constructing a table body, calculating the volume of the table body, extracting the numerical values of the volume, and marking the numerical values as table integral values.
4. The electronic automobile scale calibration method based on big data according to claim 1, wherein a signaling processing unit and a bench analysis unit are further arranged in the calibration module;
the signaling processing unit is used for processing the calibration signaling, and the specific processing process is as follows: analyzing the calibration signaling to obtain a bench integral value, the number of the electronic automobile scale and a calibration instruction; comparing the bench integral value with a preset bench integral threshold, and generating a bench instruction and sending the bench instruction to a bench integral analysis unit when the bench integral value is smaller than the preset bench integral threshold;
the whole table analysis unit is used for receiving the table position instruction and analyzing and processing to obtain whole table personnel, generating whole table processing labels and sending the whole table processing labels to intelligent terminals of the whole table personnel, and after the whole table personnel receives the whole table processing labels through the intelligent terminals, the whole table personnel reach corresponding positions of the corresponding electronic truck scales and clean the weighing table surfaces of the electronic truck scales.
5. The electronic truck scale calibration method based on big data according to claim 4, wherein the specific process of analyzing the instructions at the stage by the stage analysis unit is as follows:
after receiving the instruction of the platform, acquiring and processing cleaning information of cleaning personnel corresponding to the electronic truck scale;
sending a cleaning request to a cleaning person, receiving a request information result fed back by the cleaning person within a preset time range, and recording the feedback moment of the cleaning person; when the result of the request information is that the processing and positioning of the position are allowed, marking the cleaning personnel as the optimizing personnel;
calculating the time difference between the feedback time of the optimal personnel and the time of sending the cleaning request to obtain the response time of the optimal personnel; calculating the distance between the positioning position and the position of the electronic truck scale to obtain a cleaning distance;
extracting and processing the values of the response time length, the cleaning interval and the cleaning value to obtain a bench sorting value TZ of the optimizing personnel; sorting the priority personnel according to the table sorting value TZ from large to small, marking the priority personnel with the largest sorting as the table sorting personnel, generating a prompt tag and feeding back the prompt tag to a mobile phone terminal of the priority personnel; when the priority personnel feed back the cancel instruction within the preset time range, marking the priority personnel with ordered orders as the whole personnel and generating a prompt label to feed back the prompt label to the mobile phone terminal of the priority personnel, and so on, and simultaneously, the total cancel times qx of the priority personnel feed back the cancel instruction is increased once.
6. The electronic automobile scale calibration method based on big data according to claim 4, wherein a clear analysis unit is further arranged in the calibration module; the cleaning and analyzing unit is used for collecting and analyzing the time when the cleaning of the electronic truck scale is completed by the whole personnel, and the specific analysis process is as follows: calculating the time difference between the cleaning completion time and the time when the whole processing label is received to obtain the processing completion time of the whole personnel, extracting the value of the processing completion time and dividing the value by the cleaning interval to obtain a time interval ratio, and counting the number of all the processing completion time of the whole personnel to obtain the total processing times; and summing all the time-distance ratios, taking an average value to obtain a time-distance average value, and carrying out normalization processing on the time-distance average value, the total processing times and the total cancelling times to obtain a clear value of the whole staff.
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| JPH01282430A (en) * | 1988-05-06 | 1989-11-14 | Kubota Ltd | Method and apparatus for measuring static weight for running vehicle |
| CN103630221A (en) * | 2012-08-27 | 2014-03-12 | 济南纳维信息技术有限公司 | Method for remotely monitoring vehicle weighing cheating by using video analyzing |
| CN111189517A (en) * | 2019-09-29 | 2020-05-22 | 重庆联昶科技有限公司 | Detection and adjustment method for electronic truck scale |
| US20210381881A1 (en) * | 2020-06-09 | 2021-12-09 | Mettler-Toledo (Changzhou) Precision Instruments Ltd | Method and device for diagnosing weighing system |
| CN113223294A (en) * | 2021-05-07 | 2021-08-06 | 中国计量大学 | Expressway automobile balance period checking method based on social vehicle big data |
| CN115790804A (en) * | 2023-02-08 | 2023-03-14 | 福建省智能交通信息工程有限公司 | Dynamic truck scale state monitoring method, medium, equipment and device |
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