CN118037151A - Internet-based automobile enterprise logistics supply chain monitoring management system - Google Patents
Internet-based automobile enterprise logistics supply chain monitoring management system Download PDFInfo
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Abstract
The invention relates to the technical field of automobile enterprise logistics supply chains, in particular to an internet-based automobile enterprise logistics supply chain monitoring and management system; the invention is used for solving the technical problems that the situation before and during the transportation of the parts cannot be integrated and analyzed, and the optimal transportation of the parts is difficult to realize; according to the invention, the pre-selection analysis operation of the distribution vehicle and the analysis operation of the component loading gauge are used for analyzing and processing the situation before component transportation, the operation monitoring module is used for analyzing and processing the situation during component transportation, and the cloud management platform is used for enabling the processes to be related to each other, so that the situation before component transportation and the situation during component transportation can be integrated and analyzed comprehensively, and the optimal transportation of the components is realized.
Description
Technical Field
The invention relates to the technical field of automobile enterprise logistics supply chains, in particular to an internet-based automobile enterprise logistics supply chain monitoring and management system.
Background
The automobile manufacturing industry has very many links on the upstream and downstream, so that the supply chain of the automobile manufacturing industry is very representative, taking the supply chain of the automobile manufacturing industry as an example, the main components can be divided into transportation supply flows of parts of suppliers, storage and transportation flows in the production process, storage and transportation flows of whole vehicles and spare parts, industrial waste recycling treatment flows and the like, and the supply chain management requires the automobile industry to integrate the whole supply chain flow, and the automobile supply chain is comprehensively integrated through the functional integration, the process integration and the resource integration of the automobile flow;
The automobile logistics is a collaborative competition system or market competition community which takes automobile manufacturers as the center, namely takes the production and the marketing of parts as a main line, takes related information flows to assist suppliers and clients to act, reflects the capability of automobile enterprises in connection with customers and suppliers, enables the parts to rapidly move in an effective supply chain by utilizing logistics management, enables the supply chain link point enterprises to benefit, and enables core manufacturing enterprises to realize various resource plans and control from raw material acquisition to the completion of the whole process of the parts by establishing strategic partnerships with logistics companies, suppliers and distributors;
At present, in the whole automobile manufacturing process, parts required in the whole automobile manufacturing process are usually transported from each part production factory to the whole automobile factory to be assembled to form a whole automobile, then the whole automobile is transported to an automobile seller through logistics, the transportation process of the parts cannot be integrated and analyzed by combining the situations before and during transportation, optimal transportation of the parts is difficult to realize, and reasonable planning and reasonable preselection of the part transportation vehicles before transportation cannot be carried out;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an internet-based automobile enterprise logistics supply chain monitoring and management system so as to solve the technical defects of the background technology.
The aim of the invention can be achieved by the following technical scheme: the internet-based automobile enterprise logistics supply chain monitoring management system comprises a data acquisition module, a cloud management platform, an execution control module and an operation monitoring module;
the data acquisition module is used for acquiring the demand data of parts in the whole automobile manufacturing process, the quantity data of the idle delivery vehicles and the carriage temperature and humidity of the idle delivery vehicles, and transmitting the data to the cloud management platform together;
The cloud management platform immediately makes a vehicle preselection analysis operation according to the quantity data of the idle delivery vehicles after receiving the quantity data of the idle delivery vehicles, generates a vehicle maintenance signal and sends vehicle maintenance text content to a receiving terminal of a manager, and meanwhile generates a normal running signal for further processing, and obtains the delivery preselection vehicles and travel elimination vehicles, and makes a loading planning analysis operation for carriage loading parts of the delivery preselection vehicles, generates a loading abnormal signal and a loading normal signal, and sends the loading normal signal to an execution control module;
The execution control module is also used for collecting environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process and sending the environment data to the operation monitoring module;
The operation monitoring module immediately performs environment monitoring analysis operation according to the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process after receiving the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process, generates a carriage environment adjusting signal and sends the carriage environment adjusting signal to the cloud management platform;
and the cloud management platform immediately controls the alarm to give an alarm after receiving the carriage environment adjusting signal and prompts a driver by voice.
Preferably, the demand data comprises the types of parts required in the whole automobile manufacturing process and the number of parts required by each type, and the environment data comprises the values of the temperature and the humidity of the interior and the exterior of the compartment of the delivery preselected automobile corresponding to the loading normal signals in the part transportation process.
Preferably, the cloud management platform stores the voltage value Ve of the engine, the qualified storage temperature range and the qualified storage humidity range of the parts in the historical driving process of the idle delivery vehicle.
Preferably, the specific process of the vehicle pre-selection analysis operation is as follows:
Step SS1: acquiring the quantity data of idle delivery vehicles used for transporting the parts at the parts warehouse, marking the quantity data as detection vehicles, and setting the number k, k=1, 2, …, n and n as positive integers;
Step SS2: acquiring voltage values Ve, e=1, 2, …, m and m of an engine in a historical driving process of a detected vehicle through a cloud management platform, constructing a running voltage value set { V1, V2, …, vm } according to the acquired voltage values Ve, wherein the historical driving process is represented as a driving process closest to the current time, V1 is represented as a voltage value of the engine at the starting moment of the vehicle, and Vm is represented as a voltage value of the engine at the arrival moment of the vehicle;
Step SS3: the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is obtained through a formula, and compared with a preset threshold value: if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is more than or equal to a preset stored threshold value, judging that the detected vehicle is normal in operation and marking the detected vehicle as an operation normal signal, and entering step SS4; if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is less than a preset threshold value which is stored in a set mode, judging that the detected vehicle runs abnormally and marking the detected vehicle as a vehicle maintenance signal;
Step SS4: performing environment detection processing on a carriage of a detection vehicle corresponding to a normal running signal, setting environment monitoring time length, and dividing the environment monitoring time length into o subtime periods at intervals of every minute, wherein o=1, 2, …, p and p are positive integers;
Step SS5: acquiring a qualified storage temperature range and a qualified storage humidity range of the part through a cloud management platform, marking the highest temperature in the qualified storage temperature range as an upper temperature critical value WDmax, marking the lowest temperature in the qualified storage temperature range as a lower temperature critical value WDmin, marking the highest humidity in the qualified storage humidity range as an upper humidity critical value SDmax, marking the lowest humidity in the qualified storage humidity range as a lower humidity critical value SDmin, and carrying out environment detection processing on each sub-time period:
Acquiring the compartment temperature and humidity of the detected vehicle corresponding to the operation normal signals of each sub-time period in the environment monitoring time period, marking the compartment temperature and humidity as WDt and SDt respectively, acquiring the compartment environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signals of each sub-time period through a formula, and comparing the compartment environment monitoring coefficient HJX with a preset stored threshold range;
Step SS6: when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is within the preset stored threshold range, marking the sub-time period as a qualified sub-time period, and when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is outside the preset stored threshold range, marking the sub-time period as a lattice-free time period;
Step SS7: and when the number of the qualified sub-time periods is greater than the number of the unqualified time periods and the number of the unqualified sub-time periods is less than 2, marking the detected vehicle corresponding to the normal running signal as a delivery preselected vehicle, and when the number of the qualified sub-time periods is less than the number of the unqualified time periods or the number of the unqualified time periods is greater than or equal to 2, marking the detected vehicle corresponding to the normal running signal as a travel elimination vehicle.
Preferably, the specific process of the loading plan analysis operation is as follows:
Dividing the compartment of the pre-distribution vehicle into a plurality of square areas, setting the last area of the division as an additional area if the compartment of the pre-distribution vehicle cannot be divided into a plurality of square areas in equal area, comparing the volume of the plurality of square areas with the packaging volume of a single part according to the division, marking the pre-distribution vehicle as a loading abnormal signal when the theoretical loading quantity of all square areas is not consistent with the actual loading quantity, and marking the pre-distribution vehicle as a loading normal signal when the theoretical loading quantity of all square areas is consistent with the actual loading quantity and the sum of the volumes formed by all square areas and the additional area is larger than the packaging volume of a part.
Preferably, the specific process of the environmental monitoring analysis operation is as follows:
Step Q1: acquiring environment data of a distribution preselected vehicle corresponding to a loading normal signal in the part transportation process, marking a difference value between an external environment temperature value of a carriage and an internal environment temperature value of the carriage as WDC, and marking a difference value between an external environment humidity value of the carriage and an internal environment humidity value of the carriage as SDC;
Step Q2: acquiring an external environment influence coefficient YX of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process through a formula;
Step Q3: comparing an external environment influence coefficient YX of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process with a preset stored threshold value: when the external environment influence coefficient YX of the pre-selected vehicle is larger than or equal to a preset stored threshold value, a carriage environment adjusting signal is generated to indicate that the external environment of the pre-selected vehicle has influence, and when the external environment influence coefficient YX of the pre-selected vehicle is smaller than the preset stored threshold value, no signal is generated to indicate that the external environment of the pre-selected vehicle has no influence.
The beneficial effects of the invention are as follows:
(1) According to the invention, the pre-selection analysis operation of the distribution vehicle and the analysis operation of the component loading gauge are used for analyzing and processing the situation before component transportation, the operation monitoring module is used for analyzing and processing the situation during component transportation, and the cloud management platform is used for enabling the processes to be related to each other, so that the situation before component transportation and the situation during component transportation can be integrated and analyzed comprehensively, and the optimal transportation of the components is realized;
(2) The invention also carries out detection selection on the idle delivery vehicles through the delivery vehicle preselection analysis operation, prevents slow or interruption of the transportation process caused by improper vehicle allocation, carries out voltage detection on the idle vehicles, judges whether the idle vehicle historically operated engine has a problem, avoids the reduction of transportation efficiency caused by the lack of maintenance of the vehicles, prevents the quality problem of parts caused by the delivery vehicle problem in the transportation process, is used for carrying out loading planning on the delivery preselection vehicles through the part loading rule analysis operation, reasonably plans the placement of the parts in a carriage, furthest improves the loading quantity of the parts on the premise of safe transportation, and improves the transportation efficiency; space waste caused by uneven placement is avoided, and transportation cost is increased;
(3) The invention also carries out environment detection on the transport vehicle in the process of transporting the parts through the operation monitoring module, and when the interior of the carriage is influenced by the external environment, the environment is correspondingly regulated, so that the influence of the external environment on the environment in the carriage is reduced, and the influence on the operation of the vehicle and the storage of the parts is avoided.
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The invention is further described below with reference to the accompanying drawings;
fig. 1 is a system block diagram 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 one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
Embodiment one:
Referring to fig. 1, the present embodiment is an internet-based monitoring and management system for a logistics supply chain of an automobile enterprise, including a data acquisition module, a cloud management platform, an execution control module and an operation monitoring module;
The data acquisition module is used for acquiring the demand data of the parts in the whole automobile manufacturing process, the quantity data of the idle delivery vehicles, and the carriage temperature and humidity of the idle delivery vehicles, and sending the idle delivery vehicles to the cloud management platform together, wherein the demand data comprises the types of the parts required in the whole automobile manufacturing process and the quantity of the parts required by each type, and the environment data comprises the carriage internal and external temperature and humidity values of the delivery preselected vehicles corresponding to the normal loading signals in the part transportation process;
The cloud management platform stores the voltage value Ve of the engine, the qualified storage temperature range and the qualified storage humidity range of parts in the historical driving process of the idle delivery vehicle;
The cloud management platform immediately makes vehicle preselection analysis operation according to the quantity data of the idle delivery vehicles after receiving the quantity data of the idle delivery vehicles, and the specific process is as follows:
Step SS1: acquiring the quantity data of idle delivery vehicles used for transporting the parts at the parts warehouse, marking the quantity data as detection vehicles, and setting the number k, k=1, 2, …, n and n as positive integers;
Step SS2: acquiring voltage values Ve, e=1, 2, …, m and m of an engine in a historical driving process of a detected vehicle through a cloud management platform, constructing a running voltage value set { V1, V2, …, vm } according to the acquired voltage values Ve, wherein the historical driving process is represented as a driving process closest to the current time, V1 is represented as a voltage value of the engine at the starting moment of the vehicle, and Vm is represented as a voltage value of the engine at the arrival moment of the vehicle;
Step SS3: by the formula The average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is obtained and compared with a preset threshold value: if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is more than or equal to a preset stored threshold value, judging that the detected vehicle is normal in operation and marking the detected vehicle as an operation normal signal, and entering step SS4; if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is less than a preset threshold value which is stored in a set mode, judging that the detected vehicle runs abnormally and marking the detected vehicle as a vehicle maintenance signal;
Step SS4: performing environment detection processing on a carriage of a detection vehicle corresponding to a normal running signal, setting environment monitoring time length, and dividing the environment monitoring time length into o subtime periods at intervals of every minute, wherein o=1, 2, …, p and p are positive integers;
Step SS5: acquiring a qualified storage temperature range and a qualified storage humidity range of the part through a cloud management platform, marking the highest temperature in the qualified storage temperature range as an upper temperature critical value WDmax, marking the lowest temperature in the qualified storage temperature range as a lower temperature critical value WDmin, marking the highest humidity in the qualified storage humidity range as an upper humidity critical value SDmax, marking the lowest humidity in the qualified storage humidity range as a lower humidity critical value SDmin, and carrying out environment detection processing on each sub-time period:
Acquiring the temperature and humidity of the carriage of the detected vehicle corresponding to the normal running signals of each sub-time period in the environment monitoring time period, marking the detected carriage as WDt and SDt respectively, and determining the detected carriage temperature and humidity according to the formula Acquiring a carriage environment monitoring coefficient HJX of a detected vehicle corresponding to a normal operation signal of each sub-time period, and comparing the carriage environment monitoring coefficient HJX with a preset stored threshold range;
Step SS6: when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is within the preset stored threshold range, marking the sub-time period as a qualified sub-time period, and when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is outside the preset stored threshold range, marking the sub-time period as a lattice-free time period;
Step SS7: marking the detected vehicle corresponding to the normal running signal as a delivery preselected vehicle when the number of qualified sub-time periods is greater than the number of non-fit grid time periods and the number of non-fit grid time periods is less than 2, and marking the detected vehicle corresponding to the normal running signal as a travel elimination vehicle when the number of qualified sub-time periods is less than the number of non-fit grid time periods or the number of non-fit grid time periods is greater than or equal to 2;
Wherein, alpha and beta are correction coefficients, alpha is 0.984, beta is 1.04, the correction coefficients in the formula are obtained by sampling analysis by a person skilled in the art, such as temperature correction coefficients, the person skilled in the art randomly extracts five time periods and monitors the five time periods to obtain real-time temperature values in carriages of the five time periods, namely 35 ℃, 29 ℃, 24 ℃, 31 ℃ and 30 ℃, obtain a proper temperature interval for storing components, 22-36 ℃, and mark the median value in the proper temperature interval as the optimal temperature, namely 29 ℃, when the real-time temperature value is in the proper temperature interval, the real-time temperature value can be adjusted to the optimal temperature through the temperature correction coefficients in the analysis and calculation process, namely the temperature correction coefficients corresponding to the five time periods are 0.82,1,1.20,0.94 and 0.96, and the average value is 0.984;
Generating a vehicle maintenance signal, sending the vehicle maintenance text content to a receiving terminal of a manager, generating a normal running signal for next processing, obtaining a delivery preselected vehicle and a travel elimination vehicle, and carrying out loading planning analysis operation on carriage loading parts of the delivery preselected vehicle, wherein the specific process is as follows:
Dividing the carriage of the pre-distribution vehicle into a plurality of square areas, setting the last area of the division as an additional area if the carriage of the pre-distribution vehicle cannot be divided into a plurality of square areas in equal area, comparing the volume of the plurality of square areas with the packaging volume of a single part according to the division, marking the pre-distribution vehicle as a loading abnormal signal when the theoretical loading quantity of all square areas is not consistent with the actual loading quantity, and marking the pre-distribution vehicle as a loading normal signal when the theoretical loading quantity of all square areas is consistent with the actual loading quantity and the sum of the volumes formed by all square areas and the additional area is greater than the packaging volume of a part;
the method has the advantages that the loading quantity of parts is improved to the greatest extent on the premise of safe transportation, the transportation efficiency is improved, idle delivery vehicles are detected and selected through the delivery vehicle preselection analysis operation, slow or interruption of transportation process caused by improper vehicle allocation is prevented, voltage detection is carried out on the idle vehicles, whether problems exist in an idle vehicle historical operation engine or not is judged, the reduction of transportation efficiency caused by the fact that the vehicles are not maintained is avoided, the quality problems of the parts in the transportation process caused by the delivery vehicle problem are prevented, the parts are used for carrying out loading planning on the delivery preselection vehicles through the part loading rule analysis operation, the placement of the parts in a carriage is reasonably planned, the loading quantity of the parts is improved to the greatest extent on the premise of safe transportation, and the transportation efficiency is improved; space waste caused by uneven placement is avoided, and transportation cost is increased;
Generating a loading abnormal signal and a loading normal signal, and sending the loading normal signal to an execution control module;
The execution control module is also used for collecting environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process and sending the environment data to the operation monitoring module;
The operation monitoring module immediately performs environment monitoring analysis operation according to the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process after receiving the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process, and the specific process is as follows:
Step Q1: acquiring environment data of a distribution preselected vehicle corresponding to a loading normal signal in the part transportation process, marking a difference value between an external environment temperature value of a carriage and an internal environment temperature value of the carriage as WDC, marking a difference value between an external environment humidity value of the carriage and an internal environment humidity value of the carriage as SDC, and acquiring the temperature and the humidity through detection equipment such as a sensor, wherein the external environment temperature is possibly smaller than the internal temperature of the carriage, so that the temperature difference value and the humidity difference value are calculated according to absolute values;
Step Q2: by the formula Acquiring an external environment influence coefficient YX of a distribution preselected vehicle corresponding to a loading normal signal in the part transportation process, wherein x1 and x2 are proportionality coefficients, x1 is more than x2 is more than 0, e is a natural constant, and HJX is a carriage environment monitoring coefficient of a detection vehicle corresponding to the running normal signal of each sub-time period;
Step Q3: comparing an external environment influence coefficient YX of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process with a preset stored threshold value: when the external environment influence coefficient YX of the pre-selected delivery vehicle is more than or equal to a preset stored threshold value, generating a carriage environment adjusting signal to indicate that the external environment of the pre-selected delivery vehicle has influence, and when the external environment influence coefficient YX of the pre-selected delivery vehicle is less than the preset stored threshold value, generating no signal to indicate that the external environment of the pre-selected delivery vehicle has no influence;
the operation monitoring module is used for detecting the environment of the transport vehicle in the part transport process, and when the interior of the carriage is influenced by the external environment, the environment is correspondingly regulated, so that the influence of the external environment on the interior of the carriage is reduced, and the influence on the operation of the vehicle and the storage of the parts is avoided;
generating a carriage environment adjusting signal and sending the carriage environment adjusting signal to a cloud management platform;
The cloud management platform immediately controls the alarm to give an alarm after receiving the carriage environment adjusting signal, and prompts a driver by voice;
The conditions before the transportation of the parts are analyzed and processed through the pre-selection analysis operation of the distribution vehicle and the analysis operation of the part loading gauge, the conditions during the transportation of the parts are analyzed and processed through the operation monitoring module, and the processes are mutually related through the cloud management platform, so that the integration analysis can be performed on the transportation process of the parts by integrating the conditions before the transportation and the conditions during the transportation of the parts, and the optimal transportation of the parts is realized.
Embodiment two:
referring to fig. 1, the internet-based method for monitoring and managing a logistics supply chain of an automobile enterprise comprises the following steps:
Step one: the method comprises the steps of collecting demand data of parts in the whole automobile manufacturing process, quantity data of idle delivery vehicles and carriage temperature and humidity of the idle delivery vehicles through a data collecting module, and sending the demand data and the quantity data of the idle delivery vehicles to a cloud management platform;
Step two: the cloud management platform performs vehicle preselection analysis operation according to the received data to obtain a delivery preselection vehicle and a travel elimination vehicle, performs loading planning analysis operation on carriage loading parts of the delivery preselection vehicle to obtain loading normal signals and sends the loading normal signals to the execution control module;
Step three: the method comprises the steps that environmental data of a delivery preselected vehicle corresponding to a loading normal signal in the transportation process of parts are collected through an execution control module and are sent to an operation monitoring module, the operation monitoring module carries out environmental monitoring analysis operation according to the received data, a carriage environmental regulation signal is generated and is sent to a cloud management platform, and after the carriage environmental regulation signal is received by the cloud management platform, an alarm is immediately controlled to give an alarm, and a driver is prompted through voice.
The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (6)
1. The internet-based automobile enterprise logistics supply chain monitoring management system is characterized by comprising a data acquisition module, a cloud management platform, an execution control module and an operation monitoring module;
the data acquisition module is used for acquiring the demand data of parts in the whole automobile manufacturing process, the quantity data of the idle delivery vehicles and the carriage temperature and humidity of the idle delivery vehicles, and transmitting the data to the cloud management platform together;
The cloud management platform immediately makes a vehicle preselection analysis operation according to the quantity data of the idle delivery vehicles after receiving the quantity data of the idle delivery vehicles, generates a vehicle maintenance signal and sends vehicle maintenance text content to a receiving terminal of a manager, and meanwhile generates a normal running signal for further processing, and obtains the delivery preselection vehicles and travel elimination vehicles, and makes a loading planning analysis operation for carriage loading parts of the delivery preselection vehicles, generates a loading abnormal signal and a loading normal signal, and sends the loading normal signal to an execution control module;
The execution control module is also used for collecting environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process and sending the environment data to the operation monitoring module;
The operation monitoring module immediately performs environment monitoring analysis operation according to the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process after receiving the environment data of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process, generates a carriage environment adjusting signal and sends the carriage environment adjusting signal to the cloud management platform;
and the cloud management platform immediately controls the alarm to give an alarm after receiving the carriage environment adjusting signal and prompts a driver by voice.
2. The internet-based logistics supply chain monitoring and management system of an automobile enterprise according to claim 1, wherein the demand data comprises types of parts required in the whole automobile manufacturing process and the number of parts required in each type, and the environment data comprises values of the temperature and the humidity of the interior and the exterior of a compartment of the delivery preselected vehicle corresponding to the normal loading signals in the part transportation process.
3. The internet-based logistics supply chain monitoring and management system of an automobile enterprise of claim 1, wherein the voltage value Ve of the engine, the acceptable storage temperature range of the parts and the acceptable storage humidity range during the historical driving of the idle delivery vehicle are stored in the cloud management platform.
4. The internet-based automotive enterprise logistics supply chain monitoring and management system of claim 1, wherein the vehicle pre-selection analysis operation comprises the following steps:
Step SS1: acquiring the quantity data of idle delivery vehicles used for transporting the parts at the parts warehouse, marking the quantity data as detection vehicles, and setting the number k, k=1, 2, …, n and n as positive integers;
Step SS2: acquiring voltage values Ve, e=1, 2, …, m and m of an engine in a historical driving process of a detected vehicle through a cloud management platform, constructing a running voltage value set { V1, V2, …, vm } according to the acquired voltage values Ve, wherein the historical driving process is represented as a driving process closest to the current time, V1 is represented as a voltage value of the engine at the starting moment of the vehicle, and Vm is represented as a voltage value of the engine at the arrival moment of the vehicle;
Step SS3: the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is obtained through a formula, and compared with a preset threshold value: if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is more than or equal to a preset stored threshold value, judging that the detected vehicle is normal in operation and marking the detected vehicle as an operation normal signal, and entering step SS4; if the average value AVG of the voltage values corresponding to the engine in the historical driving process of the detected vehicle is less than a preset threshold value which is stored in a set mode, judging that the detected vehicle runs abnormally and marking the detected vehicle as a vehicle maintenance signal;
Step SS4: performing environment detection processing on a carriage of a detection vehicle corresponding to a normal running signal, setting environment monitoring time length, and dividing the environment monitoring time length into o subtime periods at intervals of every minute, wherein o=1, 2, …, p and p are positive integers;
Step SS5: acquiring a qualified storage temperature range and a qualified storage humidity range of the part through a cloud management platform, marking the highest temperature in the qualified storage temperature range as an upper temperature critical value WDmax, marking the lowest temperature in the qualified storage temperature range as a lower temperature critical value WDmin, marking the highest humidity in the qualified storage humidity range as an upper humidity critical value SDmax, marking the lowest humidity in the qualified storage humidity range as a lower humidity critical value SDmin, and carrying out environment detection processing on each sub-time period:
Acquiring the compartment temperature and humidity of the detected vehicle corresponding to the operation normal signals of each sub-time period in the environment monitoring time period, marking the compartment temperature and humidity as WDt and SDt respectively, acquiring the compartment environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signals of each sub-time period through a formula, and comparing the compartment environment monitoring coefficient HJX with a preset stored threshold range;
Step SS6: when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is within the preset stored threshold range, marking the sub-time period as a qualified sub-time period, and when the carriage environment monitoring coefficient HJX of the detected vehicle corresponding to the operation normal signal of each sub-time period is outside the preset stored threshold range, marking the sub-time period as a lattice-free time period;
Step SS7: and when the number of the qualified sub-time periods is greater than the number of the unqualified time periods and the number of the unqualified sub-time periods is less than 2, marking the detected vehicle corresponding to the normal running signal as a delivery preselected vehicle, and when the number of the qualified sub-time periods is less than the number of the unqualified time periods or the number of the unqualified time periods is greater than or equal to 2, marking the detected vehicle corresponding to the normal running signal as a travel elimination vehicle.
5. The internet-based automotive enterprise logistics supply chain monitoring and management system of claim 1, wherein the loading planning analysis operation comprises the following steps:
Dividing the compartment of the pre-distribution vehicle into a plurality of square areas, setting the last area of the division as an additional area if the compartment of the pre-distribution vehicle cannot be divided into a plurality of square areas in equal area, comparing the volume of the plurality of square areas with the packaging volume of a single part according to the division, marking the pre-distribution vehicle as a loading abnormal signal when the theoretical loading quantity of all square areas is not consistent with the actual loading quantity, and marking the pre-distribution vehicle as a loading normal signal when the theoretical loading quantity of all square areas is consistent with the actual loading quantity and the sum of the volumes formed by all square areas and the additional area is larger than the packaging volume of a part.
6. The internet-based automotive enterprise logistics supply chain monitoring and management system of claim 1, wherein the environment monitoring and analysis operations comprise the following steps:
Step Q1: acquiring environment data of a distribution preselected vehicle corresponding to a loading normal signal in the part transportation process, marking a difference value between an external environment temperature value of a carriage and an internal environment temperature value of the carriage as WDC, and marking a difference value between an external environment humidity value of the carriage and an internal environment humidity value of the carriage as SDC;
Step Q2: acquiring an external environment influence coefficient YX of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process through a formula;
Step Q3: comparing an external environment influence coefficient YX of the delivery preselected vehicle corresponding to the loading normal signal in the part transportation process with a preset stored threshold value: when the external environment influence coefficient YX of the pre-selected vehicle is larger than or equal to a preset stored threshold value, a carriage environment adjusting signal is generated to indicate that the external environment of the pre-selected vehicle has influence, and when the external environment influence coefficient YX of the pre-selected vehicle is smaller than the preset stored threshold value, no signal is generated to indicate that the external environment of the pre-selected vehicle has no influence.
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