CN116258433A - Cold chain food transportation supervision system based on big data verification - Google Patents
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Abstract
The invention belongs to the technical field of food transportation supervision, in particular to a cold chain food transportation supervision system based on big data verification, which comprises a server, a food transportation grade generation module, a vehicle data verification analysis module, a vehicle selection analysis module and a driver data verification analysis module; according to the invention, the transportation grade analysis and the vehicle grade analysis are carried out before the cold chain food of the corresponding batch are loaded, the corresponding vehicle set is selected based on the transportation grade analysis information and the vehicle grade analysis information, so that reasonable vehicle dispatching of the personnel at the vehicle dispatching end is facilitated, the idle personnel are analyzed by the driver data verification analysis module after the transportation vehicle is selected, so that the personnel at the driver dispatching end can be facilitated to be reasonably dispatched, and the transportation management and control analysis is carried out in the transportation process by the cold chain transportation management and control module so as to realize effective monitoring of the cold chain food transportation process, ensure safe transportation of the cold chain food, and facilitate transportation and storage of the cold chain food.
Description
Technical Field
The invention relates to the technical field of food transportation supervision, in particular to a cold chain food transportation supervision system based on big data verification.
Background
The cold chain transportation is an important link of cold chain logistics, and refers to transportation in which the transported goods always keep a certain temperature in the whole transportation process, namely, the links of loading, unloading, transporting, changing transportation modes, changing packaging equipment and the like, and the cold chain transportation modes can be road transportation, waterway transportation, railway transportation and aviation transportation, and can also be comprehensive transportation modes consisting of a plurality of transportation modes;
at present, cold chain food transportation is mainly carried out through vehicles, but at present, idle vehicles cannot be subjected to data verification analysis and vehicle grading and collection sorting before cold chain food loading is carried out, transportation vehicles of corresponding batches of cold chain foods cannot be selected quickly and reasonably, idle drivers cannot be subjected to data verification analysis and transportation drivers of corresponding batches of cold chain foods cannot be selected reasonably after the vehicles are selected, and effective monitoring, control and early warning of the cold chain foods cannot be carried out in the transportation process, so that safe and stable transportation of the cold chain foods is not guaranteed;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a cold chain food transportation supervision system based on big data verification, which solves the problems that in the prior art, a transportation vehicle for cold chain food of a corresponding batch cannot be selected quickly and reasonably before loading the cold chain food, a transportation driver for the cold chain food of the corresponding batch cannot be selected reasonably after the vehicle is selected, and effective monitoring, control and early warning of the cold chain food cannot be performed easily in the transportation process, so that safe and stable transportation of the cold chain food is not guaranteed.
In order to achieve the above purpose, the present invention provides the following technical solutions: a cold chain food transportation supervision system based on big data verification comprises a server, a food transportation grade generation module, a vehicle data verification analysis module, a vehicle selection analysis module and a driver data verification analysis module;
before loading the corresponding batch of cold chain foods, the food transportation grade generation module carries out transportation grade analysis on the corresponding batch of cold chain foods, obtains a heavy value and a long-acting value through the transportation grade analysis, generates a primary transportation signal, a secondary transportation signal or a tertiary transportation signal of the corresponding batch of cold chain foods through the analysis based on the heavy value and the long-acting value, and sends the primary transportation signal, the secondary transportation signal or the tertiary transportation signal to the vehicle selection analysis module through the server;
the vehicle data verification analysis module is used for analyzing the idle vehicles, marking the corresponding idle vehicles as primary vehicles, secondary vehicles or tertiary vehicles through analysis, establishing a primary vehicle set, a secondary vehicle set and a tertiary vehicle set through analysis, sequencing subsets in the sets, and sending the sequenced primary vehicle set, secondary vehicle set and tertiary vehicle set to the vehicle selection analysis module through the server;
the vehicle selection analysis module is used for selecting a corresponding vehicle set based on the transport signals of the cold chain foods in the corresponding batch, wherein the primary transport signals, the secondary transport signals or the tertiary transport signals are in one-to-one correspondence with the primary vehicle set, the secondary vehicle set and the tertiary vehicle set, marking the first subset in the corresponding vehicle set as recommended vehicles, and sending the recommended vehicles and the corresponding vehicle set to the vehicle dispatching end through the server; the driver data verification analysis module is used for acquiring all idle drivers after the transport vehicle is selected, analyzing the idle drivers, acquiring qualified personnel through analysis, sorting the qualified personnel through analysis, establishing a qualified personnel set, marking the idle driver positioned at the first position in the qualified personnel set as a recommended driver, and sending the recommended driver and the qualified personnel set to a driver dispatching end through the server.
Further, the specific operation process of the food transportation grade generation module comprises the following steps:
before loading the corresponding batch of cold chain foods, acquiring the total value and the total weight of the corresponding batch of cold chain foods, acquiring a transportation end point of the corresponding batch of cold chain foods, determining a cold chain food transportation path based on the current position and the transportation end point, acquiring the length of the transportation path and marking the transportation path as the transportation length, acquiring the arrival stop time of the corresponding batch of cold chain foods, calculating the time difference between the arrival stop time and the current time to obtain arrival time limit, and calculating the ratio of the transportation length to the arrival time limit to obtain an operation efficiency value; and carrying out numerical calculation on the total value and the total weight of the cold chain food in the corresponding batch to obtain a heavy value, carrying out numerical calculation on the transportation length and the transportation effect value to obtain a long-acting value, and generating a primary transportation signal if the heavy value exceeds a preset heavy value threshold or the long-acting value exceeds a preset long-acting threshold.
Further, if the heavy value does not exceed the preset heavy value threshold and the long-acting value does not exceed the preset long-acting threshold, subtracting the heavy value from the preset heavy value threshold to obtain a heavy value difference, subtracting the long-acting value from the preset long-acting threshold to obtain a long-acting difference, carrying out numerical calculation on the heavy value difference and the long-acting difference to obtain a grade evaluation coefficient, generating a third-level transportation signal if the grade evaluation coefficient exceeds the maximum value of the preset grade evaluation coefficient range, generating a second-level transportation signal if the grade evaluation coefficient is located in the preset grade evaluation coefficient range, and generating a first-level transportation signal if the grade evaluation coefficient does not exceed the minimum value of the preset grade evaluation coefficient range.
Further, the specific operation process of the vehicle data verification analysis module comprises the following steps:
acquiring idle vehicles, marking the idle vehicles as candidate vehicles i, i= {1,2, …, n }, wherein n represents the number of the idle vehicles and n is a natural number greater than 1; setting a vehicle data acquisition period, obtaining the maintenance times and the maintenance time length of the vehicle i to be selected in the vehicle data acquisition period, summing the maintenance time lengths to obtain the total maintenance time length, carrying out numerical calculation on the maintenance times and the total maintenance time length to obtain a maintenance coefficient, and marking the vehicle i to be selected as a three-level vehicle if the maintenance coefficient does not exceed a preset maintenance coefficient threshold; if the maintenance coefficient exceeds a preset maintenance coefficient threshold value, obtaining a vehicle verification coefficient through vehicle verification analysis and marking the vehicle i to be selected as a primary vehicle, a secondary vehicle or a tertiary vehicle;
establishing a first-level vehicle set for all first-level vehicles, a second-level vehicle set for all second-level vehicles, a third-level vehicle set for all third-level vehicles, sorting subsets in the first-level vehicle set according to the values of the vehicle verification coefficients from small to large, and sorting subsets in the second-level vehicle set according to the values of the vehicle verification coefficients from small to large; the subsets in the three-level vehicle set are firstly ranked according to the values of the vehicle verification coefficients from small to large, and then ranked according to the values of the maintenance coefficients from large to small.
Further, the specific analysis process of the vehicle verification analysis is as follows:
the maintenance coefficient is subjected to difference calculation with a preset maintenance coefficient threshold value to obtain a maintenance difference value, a fault coefficient of the to-be-selected vehicle i is obtained through fault analysis, the input operation date of the to-be-selected vehicle i is obtained, the time difference between the current date and the input operation date is calculated to obtain operation duration, the operation mileage of the to-be-selected vehicle i in the operation duration is calculated, the maintenance difference value, the fault coefficient, the operation duration and the operation mileage are calculated to obtain a vehicle verification coefficient, if the vehicle verification coefficient exceeds the maximum value of a preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a three-level vehicle, if the vehicle verification coefficient is located in the preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a two-level vehicle, and if the vehicle verification coefficient does not exceed the minimum value of the preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a one-level vehicle.
Further, the specific analysis process of the fault analysis is as follows:
obtaining fault conditions of a vehicle i to be selected in a vehicle data acquisition period, wherein the fault conditions comprise vehicle fault frequency, fault maintenance time length and fault maintenance cost of each vehicle fault, carrying out numerical calculation on the fault maintenance time length and the fault maintenance cost of corresponding vehicle faults to obtain fault magnitude values, carrying out numerical comparison on the fault magnitude values with preset fault thresholds, marking the corresponding vehicle faults as high-loss faults if the fault magnitude values exceed the preset fault thresholds, and otherwise marking the corresponding vehicle faults as low-loss faults; and obtaining the frequency of high-loss faults and the frequency of low-loss faults of the vehicle i to be selected in the vehicle data acquisition period, and carrying out weighted summation calculation on the frequency of the high-loss faults and the frequency of the low-loss faults to obtain a fault coefficient.
Further, the specific operation process of the driver data verification analysis module includes:
after the transport vehicle is selected, acquiring an idle driver, marking the idle driver as an analysis target u, u= {1,2, …, k }, wherein k represents the number of the idle driver and k is a natural number greater than 1; setting a personnel data acquisition period, acquiring a qualified target through human-vehicle default degree analysis, acquiring driving age, total cold chain transportation driving mileage and total cold chain transportation task times corresponding to the qualified target, calculating the ratio of times of successfully completing the cold chain transportation tasks according to the period to the total cold chain transportation task times to acquire a task success coefficient, calculating the driving age, the total cold chain transportation mileage, the total cold chain transportation task times and the task success coefficient to obtain a driver verification coefficient, sequencing according to the value of the driver verification coefficient from large to small, establishing a qualified personnel set, and marking the idle drivers positioned at the first position in the qualified personnel set as recommended drivers.
Further, the specific analysis process of the human car moeidoptera analysis is as follows:
the method comprises the steps of obtaining the number of execution tasks and execution task mileage of a selected transport vehicle by an analysis target u in a personnel data acquisition period, carrying out numerical calculation on the number of execution tasks and the execution task mileage to obtain a human-vehicle acquiescence value, carrying out numerical comparison on the human-vehicle acquiescence value and a preset human-vehicle acquiescence threshold value, and marking the analysis target u as a qualified target if the human-vehicle acquiescence value exceeds the preset human-vehicle acquiescence threshold value.
Further, the server is in communication connection with the cold chain transportation management and control module and the transportation supervision terminal, the cold chain transportation management and control module is used for carrying out transportation management and control analysis in the transportation process of cold chain food, a transportation early warning signal or a transportation qualified signal is generated through analysis, the transportation early warning signal or the transportation qualified signal is sent to the transportation supervision terminal in the corresponding transportation vehicle through the server, and the transportation supervision terminal sends early warning after receiving the transportation early warning signal.
Further, the specific analysis process of the transportation management analysis is as follows:
the method comprises the steps of obtaining carriage environment information of a corresponding transport vehicle, wherein the carriage environment information comprises real-time data of all environment detection items, marking the environment detection items, the detection periods of which are not in a corresponding preset proper range, as deviation detection items, marking the number of the deviation detection items of the detection periods as deviation numbers, marking the deviation degree of the numerical values of the deviation detection items and the corresponding preset proper range as detection item deviation values, multiplying the detection item deviation values of the corresponding deviation detection items by the corresponding preset deviation risk coefficients, marking the products of the detection item deviation values and the corresponding preset deviation risk coefficients as detection item risk values, summing the detection item risk values of all the deviation detection items to obtain deviation risk values, carrying out numerical calculation on the deviation risk values and the deviation numbers to obtain transport risk values, and generating transport early warning signals if the transport risk values exceed a preset transport risk threshold value, otherwise generating transport qualification signals.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the food transportation grade generation module analyzes the transportation grade before the loading of the corresponding batch of cold chain foods to generate the corresponding transportation signal of the corresponding batch of cold chain foods, the vehicle data verification analysis module analyzes the idle vehicles to mark the corresponding idle vehicles as primary vehicles, secondary vehicles or tertiary vehicles, and the vehicle selection analysis module selects the corresponding vehicle set based on the transportation signal of the corresponding batch of cold chain foods by analyzing and establishing the primary vehicle set, the secondary vehicle set and the tertiary vehicle set to order subsets in the set, and marks the first subset in the corresponding vehicle set as recommended vehicles, so that the transportation vehicle selection of the corresponding batch of cold chain foods is convenient for vehicle dispatching end personnel, the vehicle dispatching end personnel is facilitated, and the vehicle selection is more reasonable and rapid;
2. according to the invention, the driver data verification analysis module is used for analyzing the idle drivers after the transport vehicle is selected to obtain the qualified target, sorting the qualified drivers and establishing the qualified personnel set through analysis, and marking the idle drivers positioned at the first position in the qualified personnel set as recommended drivers, so that the driver scheduling end personnel can conveniently select the drivers corresponding to the batch of cold chain foods, the driver scheduling end personnel can be helped to schedule the drivers, and the driver selection is more reasonable and rapid; and carry out transportation management and control analysis in order to realize the effective monitoring of cold chain food transportation process in the transportation process of cold chain food through cold chain transportation management and control module, send the early warning after transportation supervision terminal received transportation early warning signal in time and remind the navigating mate that corresponds transport vehicle, guarantee the safe transport of cold chain food, be favorable to the transportation of cold chain food to store.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of a second embodiment of 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.
Embodiment one:
as shown in fig. 1, the cold chain food transportation supervision system based on big data verification provided by the invention comprises a server, a food transportation grade generation module, a vehicle data verification analysis module, a vehicle selection analysis module and a driver data verification analysis module, wherein the server is in communication connection with the food transportation grade generation module, the vehicle data verification analysis module, the vehicle selection analysis module and the driver data verification analysis module;
before loading the corresponding batch of cold chain foods, the food transportation grade generation module carries out transportation grade analysis on the corresponding batch of cold chain foods, obtains a heavy value and a long-acting value through the transportation grade analysis, generates a primary transportation signal, a secondary transportation signal or a tertiary transportation signal of the corresponding batch of cold chain foods through the analysis based on the heavy value and the long-acting value, and sends the primary transportation signal, the secondary transportation signal or the tertiary transportation signal to the vehicle selection analysis module through the server; the specific operation process of the food transport grade generating module is as follows:
before loading the corresponding batch of cold chain foods, acquiring the total value and the total weight of the corresponding batch of cold chain foods, marking the total value as ZJ and ZL, acquiring the transportation end point of the corresponding batch of cold chain foods, determining a cold chain food transportation path based on the current position and the transportation end point, acquiring the length of the transportation path, marking the transportation length as the transportation length YD, acquiring the arrival stop time of the corresponding batch of cold chain foods, calculating the arrival time and the current time to obtain arrival time limit, calculating the ratio of the transportation length to the arrival time limit to obtain an operation efficiency value YX, wherein the larger the operation efficiency value YX is, indicating that the transportation of the corresponding batch of cold chain foods is more urgent;
by the formula
The method comprises the steps of carrying out numerical calculation on the total value ZJ and the total weight ZL of the cold chain food in the corresponding batch to obtain a weight value JQ, wherein a1 and a2 are preset weight coefficients, a1 is more than a2 and more than 0, and the weight value JQ is a data value for comprehensively reflecting the total value and the total weight condition of the cold chain food in the corresponding batch; by the formula->
Carrying out numerical calculation on the transport length YD and the transport effect value YX to obtain a long-acting value CX, wherein b1 and b2 are preset weight coefficients, and b1 is more than 0 and less than b2; it should be noted that, the larger the value of the heavy value JQ or the larger the value of the long-acting value CX, the higher the transportation requirement of the corresponding batch of cold chain food is indicated; respectively comparing the heavy value JQ and the long-acting value CX with a preset heavy value threshold and a preset long-acting threshold in numerical value, and generating a primary transportation signal if the heavy value JQ exceeds the preset heavy value threshold or the long-acting value CX exceeds the preset long-acting threshold;
if the heavy value JQ does not exceed the preset heavy value threshold and the long-acting value CX does not exceed the preset long-acting threshold, subtracting the heavy value JQ from the preset heavy value threshold to obtain a heavy value difference JC, and subtracting the long-acting value CX from the preset long-acting threshold to obtain a long-acting difference XL; by the formula
Carrying out numerical calculation on the heavy price difference JC and the long-acting difference XL to obtain a grade evaluation coefficient JP; wherein tg1 and tg2 are preset proportional coefficients with values larger than zero, and tg1 is smaller than tg2; and the numerical value of the grade evaluation coefficient JP is in a direct proportion relation with the value of the differential value JC and the value of the long-acting differential value XL, and the larger the numerical value of the differential value JC is, the larger the numerical value of the grade evaluation coefficient JP is, so that the lower the transportation requirement of the cold chain food in the corresponding batch is;
and calling a preset grade evaluation coefficient range which is recorded and stored in advance, carrying out numerical comparison on a grade evaluation coefficient JP and the preset grade evaluation coefficient range, generating a three-level transportation signal if the grade evaluation coefficient JP exceeds the maximum value of the preset grade evaluation coefficient range, generating a second-level transportation signal if the grade evaluation coefficient JP is positioned in the preset grade evaluation coefficient range, and generating a first-level transportation signal if the grade evaluation coefficient JP does not exceed the minimum value of the preset grade evaluation coefficient range.
The vehicle data verification analysis module analyzes the idle vehicles, marks the corresponding idle vehicles as primary vehicles, secondary vehicles or tertiary vehicles through analysis, establishes a primary vehicle set, a secondary vehicle set and a tertiary vehicle set through analysis, sorts subsets in the sets, and sends the sorted primary vehicle set, secondary vehicle set and tertiary vehicle set to the vehicle selection analysis module through a server; the specific operation process of the vehicle data verification analysis module is as follows:
acquiring idle vehicles, marking the idle vehicles as candidate vehicles i, i= {1,2, …, n }, wherein n represents the number of the idle vehicles and n is a natural number greater than 1; setting a vehicle data acquisition period, acquiring the maintenance times and the maintenance time length of the vehicle i to be selected in the vehicle data acquisition period, summing the maintenance time lengths to obtain a total maintenance time length BZi, marking the maintenance times as BCi, and determining the maintenance time length by the formula:
wherein tp1 and tp2 are preset proportionality coefficients, and tp1 is more than tp2 is more than 0; the larger the value of the maintenance coefficient Byi is, the better the maintenance effect corresponding to the vehicle i to be selected is, and the better the use state corresponding to the vehicle i to be selected is; comparing the maintenance coefficient BYI with a preset maintenance coefficient threshold value in a numerical value, and marking the vehicle i to be selected as a three-level vehicle if the maintenance coefficient BYI does not exceed the preset maintenance coefficient threshold value, which indicates that the maintenance of the corresponding vehicle i to be selected is poor; if the maintenance coefficient Byi exceeds the preset maintenance coefficient threshold, performing difference calculation on the maintenance coefficient Byi and the preset maintenance coefficient threshold to obtain a maintenance difference KHi;
the fault condition of the vehicle i to be selected in the vehicle data acquisition period is obtained, wherein the fault condition comprises the frequency of vehicle faults, the fault maintenance time length WS and the fault maintenance cost WB of each vehicle fault, and the formula is adopted
Performing numerical calculation on the fault maintenance duration and the fault maintenance cost of the corresponding vehicle faults to obtain fault magnitude GL, wherein c1 and c2 are preset weight coefficients, and the values of c1 and c2 are both larger than zero; the larger the value of the fault maintenance duration and the larger the value of the fault maintenance cost, the larger the value of the fault magnitude GL is, which indicates that the damage caused by the corresponding fault is relatively more serious;
the fault magnitude GL is compared with a preset fault threshold value in a numerical value mode, if the fault magnitude GL exceeds the preset fault threshold value, the corresponding vehicle fault is marked as a high-loss fault, otherwise, the corresponding vehicle fault is marked as a low-loss fault; the method comprises the steps of acquiring the frequency GPi of high-loss faults and the frequency SPi of low-loss faults of a vehicle i to be selected in a vehicle data acquisition period, and respectively endowing the frequency GPi of the high-loss faults and the frequency SPi of the low-loss faults with weight values f1 and f2, wherein f1 is more than f2 is more than 0; multiplying the frequency GPi of the high-loss fault by a weight value f1, multiplying the frequency SPI of the low-loss fault by a weight value f2, and carrying out summation calculation on the two groups of products to obtain a fault coefficient GXi of the vehicle i to be selected, namely carrying out weighting summation calculation to obtain a fault coefficient GXi;
and acquiring the input operation date of the vehicle i to be selected, calculating the time difference between the current date and the input operation date to obtain operation duration YSi, and acquiring the operation mileage YLi in the operation duration of the vehicle i to be selected through the formula:
it should be noted that, the vehicle verification coefficient CHi is a data value indicating the quality of the vehicle quality, and the greater the value of the vehicle verification coefficient CHi, the worse the vehicle condition corresponding to the candidate vehicle i is relatively; the method comprises the steps of carrying out numerical comparison on a vehicle verification coefficient CHi and a preset vehicle verification coefficient range, marking a vehicle i to be selected as a three-level vehicle if the vehicle verification coefficient CHi exceeds the maximum value of the preset vehicle verification coefficient range, marking the vehicle i to be selected as a two-level vehicle if the vehicle verification coefficient CHi is positioned in the preset vehicle verification coefficient range, and marking the vehicle i to be selected as a first-level vehicle if the vehicle verification coefficient CHi does not exceed the minimum value of the preset vehicle verification coefficient range;
establishing a first-level vehicle set for all first-level vehicles, a second-level vehicle set for all second-level vehicles, a third-level vehicle set for all third-level vehicles, sorting subsets in the first-level vehicle set according to the values of the vehicle verification coefficients from small to large, and sorting subsets in the second-level vehicle set according to the values of the vehicle verification coefficients from small to large; the subsets in the three-level vehicle set are firstly sequenced from small to large according to the values of the vehicle verification coefficients, and then sequenced from large to small according to the values of the maintenance coefficients, so that verification analysis of data related to the transportation vehicles is realized, grading and set sequencing of the transportation vehicles are realized, subsequent automatic reasonable vehicle selection is facilitated, and safe and stable transportation and storage of cold chain foods are ensured.
The vehicle selection analysis module selects a corresponding vehicle set based on the transportation signals of the corresponding batches of cold chain foods, wherein the primary transportation signals, the secondary transportation signals or the tertiary transportation signals are in one-to-one correspondence with the primary vehicle set, the secondary vehicle set and the tertiary vehicle set, namely the vehicle set which is matched with the primary transportation signals, preferably, the primary transportation signals are the primary vehicle set; the secondary transportation signal is preferably selected from a primary vehicle set and a secondary vehicle set; the third-level transportation signals are preferably selected from a first-level vehicle set, a second-level vehicle set and a third-level vehicle set, and the vehicle set lower than the corresponding transportation signals is avoided to the greatest extent; the first subset in the corresponding vehicle set is marked as recommended vehicles, the recommended vehicles and the corresponding vehicle set are sent to the vehicle dispatching end through the server, so that transportation vehicle selection of corresponding batches of cold chain foods is facilitated for vehicle dispatching end personnel, vehicle dispatching is facilitated for vehicle dispatching end personnel, and vehicle selection is more reasonable and rapid.
The driver data verification analysis module is used for acquiring all idle drivers after a transport vehicle is selected, analyzing the idle drivers, acquiring qualified personnel through analysis, sorting the qualified personnel through analysis, establishing a qualified personnel set, marking the idle driver positioned at the first position in the qualified personnel set as a recommended driver, and sending the recommended driver and the qualified personnel set to a driver dispatching end through a server; the specific operation process of the driver data verification analysis module is as follows:
after the transport vehicle is selected, acquiring an idle driver, marking the idle driver as an analysis target u, u= {1,2, …, k }, wherein k represents the number of the idle driver and k is a natural number greater than 1; setting a personnel data acquisition period, acquiring the execution task times and execution task mileage of the transport vehicle selected by an analysis target u in the personnel data acquisition period, marking the execution task times and the execution task mileage as ZRu and ZCu, and passing through the formula
Numerical calculation is carried out on the task execution times ZRu of the analysis target u and the task execution mileage ZCu to obtain a human-vehicle mercy value MQu, whereinE1 and e2 are preset weight coefficients, and e1 is more than e2 and more than 0; it should be noted that, the larger the value of the human-vehicle implied value MQu, the higher the proficiency of the corresponding idle driver in driving the selected transport vehicle; comparing the human-vehicle implied value MQu with a preset human-vehicle implied threshold value, and marking the analysis target u as a qualified target if the human-vehicle implied value MQu exceeds the preset human-vehicle implied threshold value;
the driving age JLu, the total cold chain transportation driving mileage QLu and the total cold chain transportation task number QRu of the corresponding qualified targets are obtained, the ratio of the number of times of successfully completing the cold chain transportation task according to the deadline to the total cold chain transportation task number is calculated to obtain a task success coefficient RCu, and a driver verification analysis formula is passed:
wherein fp1, fp2, fp3, fp4 are preset scaling factors, fp4 > fp1 > fp3 > fp2 > 0; it should be noted that, the magnitude of the driver verification coefficient JHu is in a direct proportion relation with the driving age JLu, the total cold chain transportation mileage QLu, the total cold chain transportation task times QRu and the task success coefficient RCu, the larger the magnitude of the driver verification coefficient JHu is, the more abundant the driving experience corresponding to the qualified target is, and the more suitable for carrying out the transportation task of the current cold chain food; sequencing from large to small according to the numerical value of the driver verification coefficient, establishing a qualified personnel set, marking the idle drivers positioned at the first position in the qualified personnel set as recommended drivers, facilitating driver scheduling end personnel to select drivers corresponding to batches of cold chain foods, facilitating driver scheduling end personnel to perform personnel scheduling, and enabling the driver selection to be more reasonable and rapid.
Embodiment two:
as shown in fig. 2, the difference between this embodiment and embodiment 1 is that the server is communicatively connected to the cold chain transportation management and control module and the transportation supervision terminal, and the cold chain transportation management and control module is used for carrying out transportation management and control analysis in the transportation process of the cold chain food, and the specific analysis process of the transportation management and control analysis is as follows:
acquiring carriage environment information of a corresponding transport vehicle, wherein the carriage environment information comprises real-time data (comprising the temperature, the humidity, the oxygen concentration, the vibration frequency, the vibration amplitude and the like in the carriage) of each environment detection item, data acquisition is carried out by setting related sensors, the data are sent to a cold chain transport management and control module through a server, the environment detection items, the detection periods of which are not in a corresponding preset proper range, are marked as deviation detection items, the preset proper range of the corresponding environment detection items is recorded in advance by a worker and stored in the server, and the preset proper range is the optimal data range for carrying out corresponding cold chain food transport and storage; for example, when the temperature in the compartment environment information is determined, if the preset proper range of the temperature is-15 ℃ to 3 ℃ and the temperature in the compartment is 5 ℃, the temperature in the compartment is determined to be not in accordance with the requirement, and the temperature is a deviation detection item;
marking the deviation degree of the numerical value of the corresponding deviation detection item and the corresponding preset suitable range as a detection item deviation value, namely if the numerical value of the corresponding deviation detection item exceeds the maximum value of the corresponding preset suitable range, the corresponding detection item deviation value represents the difference value between the numerical value of the deviation detection item and the maximum value of the corresponding preset suitable range, and if the numerical value of the corresponding deviation detection item does not exceed the minimum value of the corresponding preset suitable range, the corresponding detection item deviation value represents the difference value between the minimum value of the preset suitable range and the numerical value of the corresponding deviation detection item; for example, if the preset proper temperature range is-15 ℃ to 3 ℃, the deviation value of the detection item of the temperature is 2 when the temperature in the carriage is 5 ℃, and the deviation value of the detection item of the temperature is 3 when the temperature in the carriage is-18 ℃;
multiplying the detection item deviation value of the corresponding deviation detection item by a corresponding preset deviation risk coefficient, and marking the product of the detection item deviation value and the corresponding preset deviation risk coefficient as the detection item risk value, wherein the preset deviation risk coefficient of the corresponding deviation detection item is recorded in advance by a worker and stored in a server, the numerical value of the preset deviation risk coefficient is a positive number, and the larger the numerical value of the corresponding preset deviation risk coefficient is, the larger the harm caused by the deviation of the corresponding deviation detection item to the transportation and storage of cold chain food is indicated; summing the risk values of all the detection items deviating from the detection item to obtain a deviating risk value PF, and marking the number of the detection period deviating from the detection item as a deviating number PS;
by the formula
Carrying out numerical calculation on the deviation risk value PF and the deviation quantity PS to obtain a transportation risk value YF, wherein wp1 and wp2 are preset weight coefficients, wp1 is smaller than wp2, and the values of wp1 and wp2 are positive numbers; the value of the transportation risk value YF is in a direct proportion relation with the deviation risk value PF and the deviation quantity PS, and the larger the value of the deviation risk value PF and the larger the value of the deviation quantity PS, the larger the value of the transportation risk value YF indicates that the transportation and storage risks of the cold chain food in the detection period are larger; and carrying out numerical comparison on the transportation risk value YF and a preset transportation risk threshold value, if the transportation risk value YF exceeds the preset transportation risk threshold value, indicating that the cold chain food is abnormal in the current transportation and storage process, generating a transportation early warning signal, and if the transportation risk value YF exceeds the preset transportation risk threshold value, generating a transportation qualified signal.
The cold chain transportation management and control module is used for carrying out transportation management and control analysis in the transportation process of the cold chain food, a transportation early warning signal or a transportation qualified signal is generated through analysis, effective monitoring of the transportation process of the cold chain food is achieved, the transportation early warning signal or the transportation qualified signal is sent to a transportation supervision terminal in a corresponding transportation vehicle through a server, the transportation supervision terminal sends early warning after receiving the transportation early warning signal, a driver corresponding to the transportation vehicle is reminded in time, the transportation environment of the cold chain food is regulated and controlled when the driver corresponding to the transportation vehicle receives the transportation early warning signal, and vehicle driving adjustment is checked and carried out in time as required, so that safe transportation of the cold chain food is guaranteed, and transportation and storage of the cold chain food are facilitated.
When the system is used, before loading the corresponding batch of cold chain foods, the corresponding batch of cold chain foods are subjected to transportation grade analysis through the food transportation grade generation module to generate corresponding transportation signals of the corresponding batch of cold chain foods, the vehicle data verification analysis module analyzes idle vehicles to mark the corresponding idle vehicles as primary vehicles, secondary vehicles or tertiary vehicles, and establishes a primary vehicle set, a secondary vehicle set and a tertiary vehicle set through analysis and sorts subsets in the sets, the vehicle selection analysis module selects the corresponding vehicle set based on the transportation signals of the corresponding batch of cold chain foods, marks the first subset in the corresponding vehicle set as recommended vehicles, and is convenient for vehicle dispatching end personnel to select transportation vehicles of the corresponding batch of cold chain foods, thereby facilitating vehicle dispatching by the vehicle dispatching end personnel, and ensuring that the vehicle selection is more reasonable and rapid; and after the transport vehicle is selected, the idle drivers are analyzed through the driver data verification analysis module to acquire the qualified targets, the qualified drivers are sequenced through the analysis, the qualified personnel set is established, the idle drivers positioned at the first position in the qualified personnel set are marked as recommended drivers, the driver scheduling end personnel can conveniently select drivers corresponding to batches of cold chain foods, the driver scheduling end personnel can be helped to schedule the drivers, and the driver selection is more reasonable and rapid.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. 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 (10)
1. The cold chain food transportation supervision system based on big data verification is characterized by comprising a server, a food transportation grade generation module, a vehicle data verification analysis module, a vehicle selection analysis module and a driver data verification analysis module;
before loading the corresponding batch of cold chain foods, the food transportation grade generation module carries out transportation grade analysis on the corresponding batch of cold chain foods, generates a primary transportation signal, a secondary transportation signal or a tertiary transportation signal of the corresponding batch of cold chain foods through the transportation grade analysis, and sends the primary transportation signal, the secondary transportation signal or the tertiary transportation signal to the vehicle selection analysis module through the server;
the vehicle data verification analysis module is used for analyzing the idle vehicles, marking the corresponding idle vehicles as primary vehicles, secondary vehicles or tertiary vehicles through analysis, establishing a primary vehicle set, a secondary vehicle set and a tertiary vehicle set through analysis, sequencing subsets in the sets, and sending the sequenced primary vehicle set, secondary vehicle set and tertiary vehicle set to the vehicle selection analysis module through the server;
the vehicle selection analysis module is used for selecting a corresponding vehicle set based on the transport signals of the cold chain foods in the corresponding batch, wherein the primary transport signals, the secondary transport signals or the tertiary transport signals are in one-to-one correspondence with the primary vehicle set, the secondary vehicle set and the tertiary vehicle set, marking the first subset in the corresponding vehicle set as recommended vehicles, and sending the recommended vehicles and the corresponding vehicle set to the vehicle dispatching end through the server; the driver data verification analysis module is used for acquiring all idle drivers after the transport vehicle is selected, analyzing the idle drivers, acquiring qualified personnel through analysis, sorting the qualified personnel through analysis, establishing a qualified personnel set, marking the idle driver positioned at the first position in the qualified personnel set as a recommended driver, and sending the recommended driver and the qualified personnel set to a driver dispatching end through the server.
2. The big data verification-based cold chain food transportation regulatory system of claim 1, wherein the specific operation of the food transportation class generation module comprises:
before loading the corresponding batch of cold chain foods, acquiring the total value and the total weight of the corresponding batch of cold chain foods, acquiring a transportation end point of the corresponding batch of cold chain foods, determining a cold chain food transportation path based on the current position and the transportation end point, acquiring the length of the transportation path and marking the transportation path as the transportation length, acquiring the arrival stop time of the corresponding batch of cold chain foods, calculating the time difference between the arrival stop time and the current time to obtain arrival time limit, and calculating the ratio of the transportation length to the arrival time limit to obtain an operation efficiency value; by the formula
The method comprises the steps of carrying out numerical calculation on the total value ZJ and the total weight ZL of the cold chain food in the corresponding batch to obtain a weight value JQ, wherein a1 and a2 are preset weight coefficients, a1 is more than a2 and more than 0, and the weight value JQ is a data value for comprehensively reflecting the total value and the total weight condition of the cold chain food in the corresponding batch; and carrying out numerical calculation on the transportation length and the transportation effect value to obtain a long-acting value, and generating a primary transportation signal if the re-acting value exceeds a preset re-acting threshold or the long-acting value exceeds a preset long-acting threshold.
3. The cold chain food transportation supervision system based on big data verification according to claim 2, wherein if the weight value does not exceed a preset weight value threshold and the long-acting value does not exceed a preset long-acting threshold, the weight value is subtracted from the preset weight value threshold to obtain a weight value difference, the long-acting value is subtracted from the preset long-acting value to obtain a long-acting difference, numerical calculation is performed on the weight value difference and the long-acting difference to obtain a grade evaluation coefficient, if the grade evaluation coefficient exceeds a maximum value of a preset grade evaluation coefficient range, a three-grade transportation signal is generated, if the grade evaluation coefficient is within the preset grade evaluation coefficient range, a two-grade transportation signal is generated, and if the grade evaluation coefficient does not exceed a minimum value of the preset grade evaluation coefficient range, a first-grade transportation signal is generated.
4. The big data verification-based cold chain food transportation supervision system according to claim 1, wherein the specific operation process of the vehicle data verification analysis module comprises:
acquiring idle vehicles, marking the idle vehicles as candidate vehicles i, i= {1,2, …, n }, wherein n represents the number of the idle vehicles and n is a natural number greater than 1; setting a vehicle data acquisition period, obtaining the maintenance times and the maintenance time length of the vehicle i to be selected in the vehicle data acquisition period, summing the maintenance time lengths to obtain the total maintenance time length, carrying out numerical calculation on the maintenance times and the total maintenance time length to obtain a maintenance coefficient, and marking the vehicle i to be selected as a three-level vehicle if the maintenance coefficient does not exceed a preset maintenance coefficient threshold; if the maintenance coefficient exceeds a preset maintenance coefficient threshold value, obtaining a vehicle verification coefficient through vehicle verification analysis and marking the vehicle i to be selected as a primary vehicle, a secondary vehicle or a tertiary vehicle;
establishing a first-level vehicle set for all first-level vehicles, a second-level vehicle set for all second-level vehicles, a third-level vehicle set for all third-level vehicles, sorting subsets in the first-level vehicle set according to the values of the vehicle verification coefficients from small to large, and sorting subsets in the second-level vehicle set according to the values of the vehicle verification coefficients from small to large; the subsets in the three-level vehicle set are firstly ranked according to the values of the vehicle verification coefficients from small to large, and then ranked according to the values of the maintenance coefficients from large to small.
5. The big data verification-based cold chain food transportation regulatory system of claim 4, wherein the specific analysis process of the vehicle verification analysis is as follows:
the maintenance coefficient is subjected to difference calculation with a preset maintenance coefficient threshold value to obtain a maintenance difference value, a fault coefficient of the to-be-selected vehicle i is obtained through fault analysis, the input operation date of the to-be-selected vehicle i is obtained, the time difference between the current date and the input operation date is calculated to obtain operation duration, the operation mileage of the to-be-selected vehicle i in the operation duration is calculated, the maintenance difference value, the fault coefficient, the operation duration and the operation mileage are calculated to obtain a vehicle verification coefficient, if the vehicle verification coefficient exceeds the maximum value of a preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a three-level vehicle, if the vehicle verification coefficient is located in the preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a two-level vehicle, and if the vehicle verification coefficient does not exceed the minimum value of the preset vehicle verification coefficient range, the to-be-selected vehicle i is marked as a one-level vehicle.
6. The cold chain food transportation supervision system based on big data verification according to claim 5, wherein the specific analysis process of the failure analysis is as follows:
obtaining fault conditions of a vehicle i to be selected in a vehicle data acquisition period, wherein the fault conditions comprise vehicle fault frequency, fault maintenance time length and fault maintenance cost of each vehicle fault, carrying out numerical calculation on the fault maintenance time length and the fault maintenance cost of corresponding vehicle faults to obtain fault magnitude values, carrying out numerical comparison on the fault magnitude values with preset fault thresholds, marking the corresponding vehicle faults as high-loss faults if the fault magnitude values exceed the preset fault thresholds, and otherwise marking the corresponding vehicle faults as low-loss faults; and obtaining the frequency of high-loss faults and the frequency of low-loss faults of the vehicle i to be selected in the vehicle data acquisition period, and carrying out weighted summation calculation on the frequency of the high-loss faults and the frequency of the low-loss faults to obtain a fault coefficient.
7. The big data verification-based cold chain food transportation regulatory system of claim 1, wherein the specific operation of the driver data verification analysis module comprises:
after the transport vehicle is selected, acquiring an idle driver, marking the idle driver as an analysis target u, u= {1,2, …, k }, wherein k represents the number of the idle driver and k is a natural number greater than 1; setting a personnel data acquisition period, acquiring a qualified target through human-vehicle default degree analysis, acquiring driving age, total cold chain transportation driving mileage and total cold chain transportation task times corresponding to the qualified target, calculating the ratio of times of successfully completing the cold chain transportation tasks according to the period to the total cold chain transportation task times to acquire a task success coefficient, calculating the driving age, the total cold chain transportation mileage, the total cold chain transportation task times and the task success coefficient to obtain a driver verification coefficient, sequencing according to the value of the driver verification coefficient from large to small, establishing a qualified personnel set, and marking the idle drivers positioned at the first position in the qualified personnel set as recommended drivers.
8. The cold chain food transportation supervision system based on big data verification according to claim 7, wherein the specific analysis process of the human car merger degree analysis is as follows:
the method comprises the steps of obtaining the number of execution tasks and execution task mileage of a selected transport vehicle by an analysis target u in a personnel data acquisition period, carrying out numerical calculation on the number of execution tasks and the execution task mileage to obtain a human-vehicle acquiescence value, carrying out numerical comparison on the human-vehicle acquiescence value and a preset human-vehicle acquiescence threshold value, and marking the analysis target u as a qualified target if the human-vehicle acquiescence value exceeds the preset human-vehicle acquiescence threshold value.
9. The cold chain food transportation supervision system based on big data verification according to claim 1, wherein the server is in communication connection with a cold chain transportation control module and a transportation supervision terminal, the cold chain transportation control module is used for carrying out transportation control analysis in the transportation process of cold chain food, generating a transportation early warning signal or a transportation qualified signal through analysis, sending the transportation early warning signal or the transportation qualified signal to the transportation supervision terminal in a corresponding transportation vehicle through the server, and sending an early warning after the transportation supervision terminal receives the transportation early warning signal.
10. The big data verification-based cold chain food transportation regulatory system of claim 9, wherein the specific analysis process of the transportation management analysis is as follows:
acquiring carriage environment information of a corresponding transport vehicle, wherein the carriage environment information comprises real-time data of various environment detection items, the detection periods of which are not in a corresponding preset proper range, are marked as deviation detection items, and the number of the deviation detection items of the detection periods is marked as the deviation number; marking the deviation degree of the numerical value of the deviation detection item and the corresponding preset suitable range as a detection item deviation value, multiplying the detection item deviation value of the corresponding deviation detection item by the corresponding preset deviation risk coefficient, marking the product of the detection item deviation value and the detection item risk coefficient as a detection item risk value, summing the detection item risk values of all the deviation detection items to obtain a deviation risk value, carrying out numerical calculation on the deviation risk value and the deviation quantity to obtain a transportation risk value, generating a transportation early warning signal if the transportation risk value exceeds a preset transportation risk threshold, and otherwise, generating a transportation qualified signal.
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