Multi-source data evidence storage method for pork cold chain compliance detection

Abstract

The invention discloses a multi-source data evidence storage method for pork cold chain compliance detection, and relates to the technical field of data evidence storage, and the method comprises the steps: obtaining distributed sensing points and core sensing points based on cold chain carriage data; an abnormal sensing array is constructed in the compartment, and abnormal sensing features are obtained; obtaining a transportation sensing evolution graph based on the abnormal sensing features, and recording the transportation sensing evolution graph as an evidence storage standard; the multi-source data evidence storage method is used for solving the problems that in an existing multi-source data evidence storage method for pork cold chain compliance detection, targeted data collection cannot be carried out in the cold chain transportation process on the basis of goods characteristics of cold chain transportation, collected data are lack of linkage, evidence storage data are not comprehensive, parallel data are mutually independent, and the data storage efficiency is low. Therefore, the problem that compliance detection cannot be effectively carried out on evidence storage data is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data storage, specifically a multi-source data storage method for pork cold chain compliance detection. BACKGROUND

[0002] Pork cold chain compliance detection refers to a series of quality and safety verification processes for pork products transported and stored in the cold chain in accordance with relevant national regulations and standards. Multi-source data storage for pork cold chain compliance detection refers to the process of real-time collection, encrypted storage, and ensuring the unalterability of key data such as temperature, humidity, and transaction certificates generated during the transportation, storage, and sales of pork in the cold chain through technologies such as the Internet of Things and blockchain.

[0003] Existing methods for multi-source data storage for pork cold chain compliance detection usually rely on temperature collection data during cold chain transportation, such as obtaining temperature data sequences for multiple collection periods to obtain temperature anomaly reference values, which are used to identify collection periods with temperature anomalies and then filter out abnormal sensors to ensure the accuracy of data collection during cold chain transportation. Although this improved method can improve data collection accuracy through anomaly identification and sensor calibration, it is still not comprehensive enough to collect data based on the characteristics of goods transported in the cold chain. The collected data lacks linkage, resulting in incomplete storage data and independent parallel data that cannot establish logical relationships and collaborative verification relationships, making it difficult to effectively detect compliance. For example, in patent application CN116643951A, a cold chain logistics transportation big data monitoring and collection method is disclosed, which screens out sensors with abnormal data during sensor data collection. Other methods for multi-source data storage for pork cold chain compliance detection usually solve the problems of data verification difficulty and signature liability during data sharing and transmission between different cold chain logistics entities, but still cannot collect data based on the characteristics of goods transported in the cold chain, and the collected data lacks linkage, resulting in incomplete storage data and independent parallel data that cannot establish logical relationships and collaborative verification relationships, making it difficult to effectively detect compliance. Therefore, it is necessary to improve the existing method for multi-source data storage for pork cold chain compliance detection. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art by proposing a multi-source data archiving method for pork cold chain compliance detection, which is used to solve the problem that in the existing multi-source data archiving method for pork cold chain compliance detection, targeted data collection cannot be carried out based on the characteristics of the goods transported in the cold chain during cold chain transportation, and the collected data lacks linkage, resulting in that the archived data is not comprehensive, and the parallel data are independent of each other, cannot construct logical correlation and collaborative verification relationship, and the archived data cannot be effectively detected for compliance.

[0005] To achieve the above-mentioned purpose, the present application provides a multi-source data archiving method for pork cold chain compliance detection, comprising the following steps:

[0006] Obtain the size data of all carriages used in pork cold chain transportation, denoted as cold chain carriage data; based on the cold chain carriage data and the position of pork placed in each carriage, obtain the corresponding distributed sensing points and core sensing points of each carriage;

[0007] Based on the distributed sensing nodes and core sensing nodes of each carriage, use the volatile salt-based nitrogen rapid sensing module and the microbial metabolic gas sensor to construct an abnormal sensing array in the carriage, and obtain the abnormal sensing features of each carriage;

[0008] Based on the abnormal sensing features of all carriages, obtain a transportation sensing evolution map, denoted as an archiving standard.

[0009] Further, based on the cold chain carriage data and the position of pork placed in each carriage, obtaining the corresponding distributed sensing points and core sensing points of each carriage comprises:

[0010] Obtain all carriages used for pork cold chain transportation, based on the time sequence of the use of the carriages, sequentially record the size data of all carriages as cold chain carriage data LC1 to cold chain carriage data LC t , wherein the cold chain carriage data includes length size data, width size data and height size data corresponding to the space inside the carriage for placing pork;

[0011] For the cold chain carriage data of any one carriage α: establish a space rectangular coordinate system with the unit of the coordinate axis being m, and based on the size data corresponding to the cold chain carriage data, construct a three-dimensional model corresponding to the cold chain carriage data in the space rectangular coordinate system, denoted as a placement space model;

[0012] Record the space occupied by pork placed in the carriage α in the pork cold chain transportation as pork occupied space, and mark the pork occupied space in the placement space model.

[0013] Furthermore, based on the data from the cold chain freight cars and the location of the pork within each car, obtaining the distributed sensing points and core sensing points corresponding to each car also includes:

[0014] Within the Cartesian coordinate system: the area within the placement space model excluding the space occupied by pork is designated as the node placement area; the smallest circumscribed sphere of the pork-occupied space is obtained and designated as the occupied wrapping sphere; when the center of the occupied wrapping sphere is within the pork-occupied space, the center of the occupied wrapping sphere is designated as the core sensing point; when the center of the occupied wrapping sphere is outside the pork-occupied space, the point on the outer surface of the pork-occupied space that is closest to the center of the occupied wrapping sphere is designated as the core sensing point of the carriage α.

[0015] Obtain the points with the smallest and largest distances from the core sensing point in the node placement area, and record them as the shortest linkage point and the maximum linkage point; obtain the distances between the shortest linkage point and the maximum linkage point and the core sensing point, and record them as the shortest linkage distance and the maximum linkage distance, respectively; record the interval [shortest linkage distance, maximum linkage distance] as the sensing linkage interval of carriage α.

[0016] Furthermore, based on the data from the cold chain freight cars and the location of the pork within each car, obtaining the distributed sensing points and core sensing points corresponding to each car also includes:

[0017] Based on the data of all cold chain carriages, the core sensing points and sensing linkage intervals of all carriages are obtained; the regions corresponding to the sensing linkage intervals of all carriages are marked on the number axis, and the closed interval formed by the maximum and minimum values ​​in the region with the most overlap is recorded as the sensing determination interval.

[0018] The carriages within the sensor-defined interval of the sensing linkage zone are designated as interlocking carriages; based on the order in which carriages are used during pork cold chain transportation, all interlocking carriages are sequentially designated as interlocking carriages KL1 to KL. r , where r is a positive integer less than or equal to t and greater than or equal to 1.

[0019] Furthermore, based on the data from the cold chain freight cars and the location of the pork within each car, obtaining the distributed sensing points and core sensing points corresponding to each car also includes:

[0020] Obtain the volume of the node placement area corresponding to all the interconnectable carriages, and record the interconnectable carriage with the smallest volume as the minimum analysis carriage; based on the conventional detection radius of the gas sensor, use AI to obtain the minimum number of gas sensors to place when performing gas detection on the node placement area in the minimum analysis carriage, and record it as n.

[0021] For any movable carriage β: In the spatial rectangular coordinate system corresponding to the movable carriage β, mark the area within the node placement area that is at a distance of u1 to u2 from the core sensing point, and denot it as the sensing determination area, where u1 and u2 are the minimum and maximum values ​​in the sensing determination interval, respectively.

[0022] Within the defined sensing range, n points are uniformly acquired and denoted as distributed sensing points;

[0023] Acquire all distributed sensor points of the interconnected carriages.

[0024] Furthermore, based on the distributed sensing nodes and core sensing nodes of each carriage, an anomaly sensing array is constructed within the carriage using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, and the anomaly sensing characteristics of each carriage are obtained, including:

[0025] Inside the interconnected carriage β, cold chain sensing units are placed at all locations of distributed sensing points and in the outer packaging box of pork closest to the core sensing point. The array formed by all cold chain sensing units is called the abnormal sensing array. The cold chain sensing unit includes a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor. The volatile basic nitrogen rapid sensing module is used to detect the content of TVB-N, and the microbial metabolic gas sensor is used to detect the content of characteristic detection gas.

[0026] The gases that are easily produced when pork spoils are recorded as characteristic detection gases; the detection indicators of TVB-N and all characteristic detection gases during the cold chain transportation of pork are obtained and recorded as standard indicators of TVB-N and all characteristic detection gases, respectively, and the value corresponding to each standard indicator is recorded as the standard judgment value of the standard indicator.

[0027] Furthermore, acquiring the abnormal sensing characteristics of each carriage also includes:

[0028] Keeping the compartments used for cold chain transportation of pork and the position of the pork space inside the compartments unchanged, conduct k simulated transportations; for any interlocking compartment β: when the pork is placed in the interlocking compartment β and the door is closed, acquire the detection results of the sensors in all cold chain sensing units every tmin.

[0029] For any cold chain sensing unit: when the TVB-N content in the detection result is outside the standard index of TVB-N, [(p1-p2) / p2]×1000 is recorded as the core difference value, where p1 is the TVB-N content in the detection result, p2 is the standard judgment value of the standard index of TVB-N, and the core difference value only retains the integer part.

[0030] Furthermore, acquiring the abnormal sensing characteristics of each carriage also includes:

[0031] When the content of the characteristic gas in the test result is outside the standard index corresponding to the characteristic gas, [(q1-q2) / q2] is recorded as the co-discrepancy value, where q1 is the content of the characteristic gas in the test result and q2 is the standard judgment value of the standard index of the characteristic gas.

[0032] The sum of the core difference value and all collaborative difference values ​​corresponding to the cold chain sensing unit in all test results is recorded as the difference parameter of the cold chain sensing unit.

[0033] Let M be the differential parameter of the cold chain sensing unit corresponding to the core sensing point, and let M' be the maximum and minimum value of the differential parameters of the cold chain sensing units corresponding to all distributed sensing points. min and M max ; the interval [MM max MM min [This is denoted as a single sensing characteristic of the linked carriage β;]

[0034] Obtain all single-sensor features corresponding to the linked carriage β in k simulated transportations, and denote the union of all single-sensor features as the abnormal sensor feature of the linked carriage β.

[0035] Furthermore, based on the abnormal sensing characteristics of all carriages, a transportation sensing evolution map is obtained, and the transportation sensing evolution map is recorded as evidence, including:

[0036] Establish a Cartesian coordinate system, denoted as the sensing analysis coordinate system. The Y-axis of the sensing analysis coordinate system is a constant axis, and the coordinate points along the X-axis from the origin to the right are named sequentially from the movable carriage KL1 to the movable carriage KL. r ;

[0037] For any movable carriage β, in the straight line X = movable carriage β, the area where the abnormal sensing feature of movable carriage β is located with the vertical coordinate is denoted as the sensing feature area of ​​movable carriage β.

[0038] Furthermore, based on the abnormal sensing characteristics of all carriages, a transportation sensing evolution map is obtained, and recording the transportation sensing evolution map as evidence also includes:

[0039] Mark the sensing feature regions of all interlocking carriages in the sensing analysis coordinate system, and denote the curves obtained by fitting the highest point of all sensing feature regions and the curves obtained by fitting the lowest point of all sensing feature regions as evolution limit line A1 and evolution limit line A2, respectively.

[0040] The evolution limit line A1 and the area between evolution limit lines A1 are denoted as the transportation sensing evolution diagram, and the transportation sensing evolution diagram is denoted as the evidence standard.

[0041] The beneficial effects of this invention are as follows: First, this application obtains the size data of all the compartments used in the cold chain transportation of pork, denoted as cold chain compartment data. Based on the cold chain compartment data and the position of the pork placed in each compartment, the distributed sensing points and core sensing points corresponding to each compartment are obtained. The advantage of this is that by obtaining the distributed sensing points and core sensing points in the compartment based on the cold chain compartment data, it can be ensured that after the subsequent construction of the abnormal sensing array, the positional relationship between the core sensing points and distributed sensing points in each interlocking compartment is within a controllable range. This ensures that after obtaining the sensing data through the abnormal sensing array, effective linkage analysis can be performed on the parallel data obtained from multiple interlocking compartments to achieve logical association and collaborative verification between the parallel data. In turn, the obtained transportation sensing evolution diagram can be used as a standard for evidence storage and effective compliance testing.

[0042] This application also constructs an anomaly sensing array within each carriage using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, based on distributed sensing nodes and core sensing nodes in each carriage, and acquires the anomaly sensing characteristics of each carriage. Finally, based on the anomaly sensing characteristics of all carriages, a transportation sensing evolution diagram is obtained, and this diagram is recorded as an evidence standard. The advantage of this approach is that by constructing an anomaly sensing array, it is possible to ensure that the sensing data obtained from the array can be specifically collected within the carriage based on the characteristics of pork transported in the cold chain. At the same time, the data collection between the anomaly sensing arrays in multiple carriages can be linked, meaning that the transportation sensing evolution diagram is obtained from the anomaly sensing characteristics of all carriages. This enables a comprehensive analysis of pork cold chain transportation based on the characteristics of pork cold chain transportation, thus allowing the evidence data to effectively detect compliance issues in pork cold chain transportation. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0044] Figure 2 This is a schematic diagram of the spatial rectangular coordinate system of the present invention;

[0045] Figure 3 This is a schematic diagram showing the space occupied by the pork in this invention;

[0046] Figure 4 This is a schematic diagram of the sensing analysis coordinate system of the present invention;

[0047] Figure 5 This is a schematic diagram illustrating the acquisition of the transport sensing evolution diagram of the present invention;

[0048] Figure 6 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1, please refer to Figure 1 As shown, this application provides a multi-source data storage method for pork cold chain compliance testing, including the following steps:

[0051] Step S1: Obtain the size data of all the truck compartments used in the cold chain transportation of pork, and record them as cold chain truck compartment data; based on the cold chain truck compartment data and the location where the pork is placed in each compartment, obtain the distributed sensing points and core sensing points corresponding to each compartment.

[0052] Step S1 includes: Step S101, obtaining all the wagons used for cold chain transportation of pork, and recording the size data of all wagons sequentially as cold chain wagon data LC1 to cold chain wagon data LC based on the order in which the wagons were used. t The data for the cold chain truck compartment includes the length, width, and height dimensions of the space inside the compartment used to store pork.

[0053] In the specific implementation process, for example, during a data analysis, the number of wagons used for cold chain transportation of pork was 8, and the length, width and height data corresponding to all cold chain wagon data are shown in Table 1.

[0054] Table 1

[0055]

[0056] In this embodiment, the length, width, and height data corresponding to the cold chain truck data used for analysis are the data corresponding to the cold chain truck data LC3, that is, the length, width, and height are 4m, 2.1m, and 2.1m, respectively;

[0057] Step S102: For any cold chain car data of a car α: establish a spatial rectangular coordinate system with the coordinate axes in m, and based on the size data corresponding to the cold chain car data, construct a three-dimensional model corresponding to the cold chain car data in the spatial rectangular coordinate system, and denot it as the placement space model.

[0058] Step S103: The space occupied by pork when it is placed in the compartment α during cold chain transportation is recorded as the pork-occupied space, and the pork-occupied space is marked in the placement space model.

[0059] Step S104, in the spatial rectangular coordinate system: the area within the placement space model excluding the space occupied by pork is recorded as the node placement area; the smallest circumscribed sphere of the space occupied by pork is obtained and recorded as the occupied wrapping sphere; when the center of the occupied wrapping sphere is within the space occupied by pork, the center of the occupied wrapping sphere is recorded as the core sensing point; when the center of the occupied wrapping sphere is outside the space occupied by pork, the point on the outer surface of the space occupied by pork that is closest to the center of the occupied wrapping sphere is recorded as the core sensing point of the carriage α;

[0060] In the analysis of this embodiment, the established spatial rectangular coordinate system is as follows: Figure 2 As shown, the rectangular body FK is the placement space model corresponding to the cold chain truck data LC3; by obtaining the space occupied by pork corresponding to the cold chain truck data LC3, the space occupied by pork when placed in the cold chain truck data LC3 during pork cold chain transportation is obtained, as shown in the figure. Figure 3 As shown in the cubic assembly LZ, each small cube XL in the cubic assembly LZ is a model corresponding to the cardboard box used to package pork. In this embodiment, the diameter of the occupying sphere is obtained by taking the longest diagonal LT inside the cubic assembly LZ as the diameter of the occupying sphere. In specific implementation, the occupying sphere can be obtained by obtaining the longest diagonal inside the model corresponding to the space occupied by pork, or the parameters of the space occupied by pork can be directly sent to AI, and AI can obtain the smallest circumscribed sphere that can wrap the space occupied by pork. The purpose of obtaining the occupying sphere is to obtain the center position of the space occupied by pork, that is, the center of the occupying sphere, so as to obtain the point of detection of the interior of the area where pork is located, that is, the core sensing point. Then, when constructing the abnormal sensing array in the future, based on the characteristics of cold chain pork, the sensing data of the internal area and the external area where pork is located in the compartment can be collected in a targeted manner.

[0061] Step S105: Obtain the points with the smallest and largest distances from the core sensing point in the node placement area, and record them as the shortest linkage point and the maximum linkage point; obtain the distances between the shortest linkage point and the maximum linkage point and the core sensing point, and record them as the shortest linkage distance and the maximum linkage distance respectively; record the interval [shortest linkage distance, maximum linkage distance] as the sensing linkage interval of the carriage α.

[0062] In the analysis of this embodiment, based on the occupied sphere ZB obtained from the above analysis, the core sensing point in the rectangular body KF is obtained as follows: Figure 2The location of the midpoint HC is determined by analyzing the node placement area. In the node placement area of ​​the rectangular body KF, the points with the smallest distance from the core sensing point and the largest distance are CX and CD, respectively. The straight line lengths between HC and CX and between HC and CD can be recorded as the shortest linkage distance and the maximum linkage distance. The straight line lengths between HC and CX and between HC and CD are 0.7m and 3.1m, respectively. The sensing linkage interval can be recorded as [0.7m, 3.1m].

[0063] Step S106: Based on the cold chain car data of all cars, obtain the core sensing points and sensing linkage intervals corresponding to all cars; mark the areas corresponding to the sensing linkage intervals of all cars on the number axis, and record the closed interval formed by the maximum and minimum values ​​in the area with the most overlap as the sensing determination interval.

[0064] In the analysis of this embodiment, by analyzing the data of the above 8 cold chain carriages, all the sensor linkage intervals obtained are [0.5m, 3.1m], [0m, 2m], [1.7m, 3.1m], [0.5m, 2.5m], [1.7m, 2m], [0.7m, 1.1m], [0.6m, 1.7m], and [0.3m, 2.1m]. Analysis by establishing a number axis shows that the region with the most overlap within the number axis is the interval [0.7m, 1.1m]. Therefore, the interval [0.7m, 1.1m] can be recorded as the sensor determination interval. By obtaining the sensor determination interval... Furthermore, in subsequent analysis, the interconnected carriages obtained from specific sensing intervals are analyzed to ensure that the positional relationship between the core sensing point and the distributed sensing point in each interconnected carriage is within a controllable range. That is, the distance between the distributed sensing point and the core sensing point in the same carriage is between [0.7m, 1.1m]. This ensures that after obtaining sensing data through the abnormal sensing array, effective linkage analysis can be performed on the parallel data obtained from multiple interconnected carriages to achieve logical association and collaborative verification between parallel data. In turn, the obtained transportation sensing evolution diagram can be used as a standard for evidence storage and effective compliance testing.

[0065] In the specific implementation process, if the distance between distributed sensing points and core sensing points is not limited, the distance between distributed sensing points and core sensing points in a certain carriage may be too close or too far. This will result in a large deviation in the data when performing linkage analysis, which will affect the accuracy of the logical association and collaborative verification between parallel data, and cause a large error when using the evidence storage standard for compliance testing.

[0066] Step S107: The carriages within the sensing linkage range that contain the sensing-determined range are designated as interlocking carriages; based on the order in which the carriages are used during pork cold chain transportation, all interlocking carriages are sequentially designated as interlocking carriages KL1 to KL. r , where r is a positive integer less than or equal to t and greater than or equal to 1.

[0067] Step S108: Obtain the volume of the node placement area corresponding to all the interconnectable carriages, and record the interconnectable carriage with the smallest volume as the minimum analysis carriage; Based on the conventional detection radius of the gas sensor, use AI to obtain the minimum number of gas sensors to place when performing gas detection on the node placement area in the minimum analysis carriage, and record it as n.

[0068] In the analysis of this embodiment, the conventional detection radius of the gas sensor used for analysis is 5m. Therefore, AI can be used to obtain the minimum number of gas sensors corresponding to a detection radius of 5m when performing gas detection on the node placement area in the smallest analysis compartment. Through data analysis, the compartment corresponding to the smallest analysis compartment is the compartment corresponding to the cold chain compartment data LC3, and the value of n is obtained as 3. In the specific implementation process, the value of n can be adaptively adjusted according to the actual collection radius of the gas sensors that can be used.

[0069] Step S109: For any movable carriage β: In the spatial rectangular coordinate system corresponding to the movable carriage β, mark the area within the node placement area that is 1 to 2 distances from the core sensing point, and record it as the sensing determination area, where u1 and u2 are the minimum and maximum values ​​in the sensing determination interval, respectively; In the analysis of this embodiment, through the above analysis, it can be obtained that u1 and u2 are 0.7m and 1.1m, respectively;

[0070] Step S110: Within the sensing defined range, acquire n points evenly and record them as distributed sensing points;

[0071] Step S111: Obtain the distributed sensor points of all interconnectable carriages.

[0072] Step S2: Based on the distributed sensing nodes and core sensing nodes of each carriage, an abnormal sensing array is constructed in the carriage using a volatile basic nitrogen fast sensing module and a microbial metabolic gas sensor, and the abnormal sensing characteristics of each carriage are obtained.

[0073] Step S2 includes: Step S201, placing cold chain sensing units at the locations of all distributed sensing points and in the outer packaging box of pork closest to the core sensing point within the interlocking carriage β, and recording the array formed by all cold chain sensing units as an abnormal sensing array. The cold chain sensing unit includes a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor. The volatile basic nitrogen rapid sensing module is used to detect the content of TVB-N, and the microbial metabolic gas sensor is used to detect the content of characteristic detection gas.

[0074] Step S202: The gases that are easily generated when pork spoils are recorded as characteristic detection gases; the detection indicators of TVB-N and all characteristic detection gases during the cold chain transportation of pork are obtained and recorded as standard indicators of TVB-N and all characteristic detection gases, respectively, and the value corresponding to each standard indicator is recorded as the standard judgment value of the standard indicator.

[0075] In the analysis of this embodiment, the characteristic detection gases obtained are carbon dioxide, trimethylamine, and hydrogen sulfide. In the actual cold chain transportation of pork, the content of TVB-N can indicate the degree of decomposition of pork protein. If the content of TVB-N exceeds the standard, it indicates that the pork protein has been severely decomposed. The concentration of carbon dioxide can reflect the active state of microbial reproduction. If the concentration of carbon dioxide exceeds the standard, it indicates that microbial reproduction is active, which will accelerate the spoilage of meat. Trimethylamine is a marker gas of pork spoilage. When it exceeds the standard, it is accompanied by a distinct odor, which will significantly reduce the freshness of pork. Hydrogen sulfide is produced by protein decomposition. Even a trace amount can make pork have a toxic smell. When it exceeds the standard, the pork is no longer edible and there is a food safety risk. In actual analysis, the above-mentioned carbon dioxide, trimethylamine, and hydrogen sulfide can be added, deleted, or modified according to the gases used to detect pork spoilage during actual cold chain transportation.

[0076] Step S203: Keep the compartment used for cold chain transportation of pork and the position of the space occupied by the pork in the compartment unchanged, and perform k simulated transportations; For any interlocking compartment β: When the pork is placed in the interlocking compartment β and the door is closed, obtain the detection results of the sensors in all cold chain sensing units every tmin.

[0077] In the specific implementation process, the values ​​of k and t can be determined according to the actual ability to simulate transportation. The higher the value of k and the smaller the value of t, the more closely the abnormal sensing characteristics of the linked carriage can match the actual characteristics of the linked carriage. Thus, when using the evidence preservation standard to conduct compliance testing on the pork cold chain, the test results are more accurate. In the analysis of this embodiment, the values ​​of k and t are set to 5 and 30, respectively.

[0078] Step S204: For any cold chain sensing unit: when the content of TVB-N in the detection result is outside the standard index of TVB-N, record [(p1-p2) / p2]×1000 as the core difference value, where p1 is the content of TVB-N in the detection result, p2 is the standard judgment value of the standard index of TVB-N, and the core difference value only retains the integer part.

[0079] Step S205: When the content of the characteristic gas in the detection result is outside the standard index corresponding to the characteristic gas, [(q1-q2) / q2] is recorded as the collaborative difference value, where q1 is the content of the characteristic gas in the detection result and q2 is the standard judgment value of the standard index of the characteristic gas.

[0080] In the analysis of this embodiment, the detection indices for TVB-N and carbon dioxide are ≤9mg / 100g and ≤5%, respectively. Therefore, the standard judgment values ​​for TVB-N and carbon dioxide are 9 and 0.05, respectively. During a simulated transport of the compartment corresponding to LC3 in the cold chain compartment, the TVB-N content detected by a cold chain sensing unit in the compartment corresponding to LC3 was 10mg / 100g, and the carbon dioxide concentration was 6%. Therefore, the core difference value and the synergistic difference value for carbon dioxide are calculated to be 111 and 0.2, respectively. In addition, the detection indices for trimethylamine and hydrogen sulfide are ≤0.5mg / m³ and ≤0.1mg / m³, respectively. The synergistic difference values ​​for trimethylamine and hydrogen sulfide are calculated to be 0.02 and 0.3, respectively. Therefore, the difference parameter of the cold chain sensing unit is calculated to be 111.52.

[0081] Step S206: The sum of the core difference value and all collaborative difference values ​​corresponding to the cold chain sensing unit in all the detection results is recorded as the difference parameter of the cold chain sensing unit.

[0082] Step S207: Denote the difference parameter of the cold chain sensing unit corresponding to the core sensing point as M, and denote the maximum and minimum values ​​of the difference parameters of the cold chain sensing units corresponding to all distributed sensing points as M. min and M max ; the interval [MM max MM min [This is denoted as a single sensing characteristic of the linked carriage β;]

[0083] In the analysis of this example, the difference parameter of the cold chain sensing unit corresponding to the core sensing point of the cold chain compartment data LC3 is 111.52, and the difference parameters of the cold chain sensing units corresponding to the three distributed sensing points are 120.45, 110.08 and 154.33, respectively. Therefore, it can be calculated that the interval [-42.81, 1.44] is the single sensing feature of the compartment corresponding to the cold chain compartment data LC3.

[0084] Step S208: Obtain all single-sensor features corresponding to the linked carriage β in k simulated transportations, and denote the union of all single-sensor features as the abnormal sensor feature of the linked carriage β.

[0085] Step S3: Based on the abnormal sensor characteristics of all carriages, obtain the transportation sensor evolution map, record the transportation sensor evolution map as the evidence standard, and conduct compliance testing based on the evidence standard during the cold chain transportation of pork.

[0086] Step S3 includes: Step S301, establishing a plane rectangular coordinate system, denoted as the sensing analysis coordinate system, wherein the Y-axis of the sensing analysis coordinate system is a constant axis, and the coordinate points on the X-axis from the origin to the right are named sequentially as the movable carriage KL1 to the movable carriage KL. r ;

[0087] Step S302: For any movable carriage β, in the straight line X = movable carriage β, the area where the abnormal sensing feature of movable carriage β is located with the vertical coordinate is recorded as the sensing feature area of ​​movable carriage β.

[0088] Step S303: Mark the sensing feature areas of all the interlocking carriages in the sensing analysis coordinate system, and record the curves obtained by fitting the highest point of all the sensing feature areas and the curves obtained by fitting the lowest point of all the sensing feature areas as evolution limit line A1 and evolution limit line A2, respectively.

[0089] In this example analysis, the number of interlocking carriages obtained from the above 8 cold chain carriage data is 4, and the interlocking carriage corresponding to cold chain carriage data LC3 is interlocking carriage KL2. The sensing analysis coordinate system established from the analyzed data is as follows: Figure 4 As shown, the dashed line X represents the sensing feature region of the movable carriage KL2. The evolution limit lines A1 and A2, obtained from the analysis of all sensing feature regions, are respectively... Figure 5 The curves YB1 and YB2 are in the middle, so the region YC between curves YB1 and YB2 is the evolution diagram of transportation sensing.

[0090] Step S304: Record the evolution limit line A1 and the area between the evolution limit line A1 as the transportation sensor evolution diagram and record the transportation sensor evolution diagram as the evidence standard.

[0091] In this embodiment, when using the evidence storage standard to conduct compliance testing of pork cold chain after obtaining the evidence storage standard, the following scheme can be used: When transporting pork in the cold chain, based on the sensing data of the sensors in the abnormal sensing array in all the linked compartments, the single sensing feature of each linked compartment is obtained in real time, and after the single sensing feature of any linked compartment is updated, the sensing feature area corresponding to all linked compartments is updated in real time.

[0092] The transportation sensor evolution map obtained from the latest sensor feature areas corresponding to all the interlocking carriages is recorded as the real-time evolution map; when any real-time evolution map is not within the evidence storage standard, the interlocking carriage corresponding to the sensor feature area outside the evidence storage standard in the real-time evolution map is recorded as the non-compliant carriage.

[0093] In the specific implementation process, if the real-time evolution map is not within the evidence storage standard, it indicates that there is at least one interlocking carriage where the data detected by the core sensing point and the distributed sensing point differs too much. Therefore, the interlocking carriages corresponding to the sensing feature areas outside the evidence storage standard can be marked as non-compliant carriages. In the analysis of this embodiment, when the core difference value is acquired, the corresponding parameter is multiplied by 1000 and only the integer part is retained. The collaborative difference value is not adjusted when it is acquired. Therefore, when the violation length is greater than 100, it can be said that the abnormality is caused by TVB-N corresponding to the core difference value. When there is a decimal in the violation length, it can be said that the abnormality is caused by the feature detection gas corresponding to the collaborative difference value. For example, if the violation length is 110.12, the result of the compliance test by the evidence storage standard can be recorded as: abnormal TVB-N content and abnormal feature detection gas content.

[0094] The length of the area outside the evidence storage standard but within the sensing feature area in the straight line X = the illegal carriage is recorded as the illegal length; when the illegal length is greater than 100, the illegal indicator is marked as TVB-N; when the illegal length contains a decimal, the illegal indicator is marked as feature detection gas.

[0095] Example 2, please refer to Figure 6 As shown, Figure 6A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps of a multi-source data evidence storage method for pork cold chain compliance detection are performed to achieve the following functions: First, the size data of all the compartments used in pork cold chain transportation are acquired and recorded as cold chain compartment data; based on the cold chain compartment data and the position of the pork in each compartment, the distributed sensing points and core sensing points corresponding to each compartment are acquired; then, based on the distributed sensing nodes and core sensing nodes of each compartment, an abnormal sensing array is constructed in the compartment using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, and the abnormal sensing characteristics of each compartment are acquired; finally, based on the abnormal sensing characteristics of all compartments, a transportation sensing evolution diagram is acquired, and the transportation sensing evolution diagram is recorded as the evidence storage standard.

[0096] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a multi-source data storage method for pork cold chain compliance detection provided by the above methods. The method includes: first, acquiring the size data of all the compartments used in pork cold chain transportation, and recording them as cold chain compartment data; based on the cold chain compartment data and the position of the pork in each compartment, acquiring the distributed sensing points and core sensing points corresponding to each compartment; then, based on the distributed sensing nodes and core sensing nodes of each compartment, constructing an abnormal sensing array in the compartment using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, and acquiring the abnormal sensing characteristics of each compartment; finally, based on the abnormal sensing characteristics of all compartments, acquiring a transportation sensing evolution diagram, and recording the transportation sensing evolution diagram as the storage standard.

[0098] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-mentioned multi-source data evidence storage method for pork cold chain compliance detection to achieve the following functions: First, it acquires the size data of all the compartments used in pork cold chain transportation, which is recorded as cold chain compartment data; based on the cold chain compartment data and the position of the pork in each compartment, it acquires the distributed sensing points and core sensing points corresponding to each compartment; then, based on the distributed sensing nodes and core sensing nodes of each compartment, it constructs an abnormal sensing array in the compartment using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, and acquires the abnormal sensing characteristics of each compartment; finally, based on the abnormal sensing characteristics of all compartments, it acquires a transportation sensing evolution diagram, which is recorded as the evidence storage standard.

[0099] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0100] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for storing multi-source data on compliance testing of pork cold chain, characterized in that, Includes the following steps: Acquire the size data of all the truck compartments used in the cold chain transportation of pork, and record them as cold chain truck compartment data; based on the cold chain truck compartment data and the location of the pork in each compartment, obtain the distributed sensor points and core sensor points corresponding to each compartment; Based on the distributed sensing nodes and core sensing nodes of each carriage, an abnormal sensing array is constructed in the carriage using a volatile basic nitrogen fast sensing module and a microbial metabolic gas sensor, and the abnormal sensing characteristics of each carriage are obtained. Based on the abnormal sensing characteristics of all carriages, a transportation sensing evolution map is obtained, and the transportation sensing evolution map is recorded as the evidence standard.

2. The multi-source data storage method for pork cold chain compliance testing according to claim 1, characterized in that, Based on data from cold chain freight cars and the location of pork within each car, the distributed sensor points and core sensor points for each car are obtained, including: Obtain all wagons used for cold chain transportation of pork. Based on the order in which the wagons were used, record the size data of all wagons sequentially as cold chain wagon data LC1 to cold chain wagon data LC. t The data for the cold chain truck compartment includes the length, width, and height dimensions of the space inside the compartment used to store pork. For any cold chain car data of car α: establish a spatial rectangular coordinate system with the coordinate axis in m, and based on the size data corresponding to the cold chain car data, construct a three-dimensional model corresponding to the cold chain car data in the spatial rectangular coordinate system, and denot it as the placement space model; The space occupied by pork when it is placed in compartment α during cold chain transportation is denoted as the pork-occupied space, and the pork-occupied space is marked in the placement space model.

3. The multi-source data storage method for pork cold chain compliance testing according to claim 2, characterized in that, Based on data from cold chain freight cars and the location of pork within each car, the distributed and core sensor points for each car also include: Within the Cartesian coordinate system: the area within the placement space model excluding the space occupied by pork is designated as the node placement area; the smallest circumscribed sphere of the pork-occupied space is obtained and designated as the occupied wrapping sphere; when the center of the occupied wrapping sphere is within the pork-occupied space, the center of the occupied wrapping sphere is designated as the core sensing point; when the center of the occupied wrapping sphere is outside the pork-occupied space, the point on the outer surface of the pork-occupied space that is closest to the center of the occupied wrapping sphere is designated as the core sensing point of the carriage α. Obtain the points with the smallest and largest distances from the core sensing point in the node placement area, and record them as the shortest linkage point and the maximum linkage point; obtain the distances between the shortest linkage point and the maximum linkage point and the core sensing point, and record them as the shortest linkage distance and the maximum linkage distance, respectively; record the interval [shortest linkage distance, maximum linkage distance] as the sensing linkage interval of carriage α.

4. The multi-source data storage method for pork cold chain compliance testing according to claim 3, characterized in that, Based on data from cold chain freight cars and the location of pork within each car, the distributed and core sensor points for each car also include: Based on the data of all cold chain carriages, the core sensing points and sensing linkage intervals of all carriages are obtained; the regions corresponding to the sensing linkage intervals of all carriages are marked on the number axis, and the closed interval formed by the maximum and minimum values ​​in the region with the most overlap is recorded as the sensing determination interval. The carriages within the sensor-defined interval of the sensing linkage zone are designated as interlocking carriages; based on the order in which carriages are used during pork cold chain transportation, all interlocking carriages are sequentially designated as interlocking carriages KL1 to KL. r , where r is a positive integer less than or equal to t and greater than or equal to 1.

5. The multi-source data storage method for pork cold chain compliance testing according to claim 4, characterized in that, Based on data from cold chain freight cars and the location of pork within each car, the distributed and core sensor points for each car also include: Obtain the volume of the node placement area corresponding to all the interconnectable carriages, and record the interconnectable carriage with the smallest volume as the minimum analysis carriage; based on the conventional detection radius of the gas sensor, use AI to obtain the minimum number of gas sensors to place when performing gas detection on the node placement area in the minimum analysis carriage, and record it as n. For any movable carriage β: In the spatial rectangular coordinate system corresponding to the movable carriage β, mark the area within the node placement area that is at a distance of u1 to u2 from the core sensing point, and denot it as the sensing determination area, where u1 and u2 are the minimum and maximum values ​​in the sensing determination interval, respectively. Within the defined sensing range, n points are uniformly acquired and denoted as distributed sensing points; Acquire all distributed sensor points of the interconnected carriages.

6. The multi-source data storage method for pork cold chain compliance testing according to claim 5, characterized in that, Based on distributed sensing nodes and core sensing nodes in each carriage, an anomaly sensing array is constructed within the carriage using a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor, and the anomaly sensing characteristics of each carriage are obtained, including: Inside the interconnected carriage β, cold chain sensing units are placed at all locations of distributed sensing points and in the outer packaging box of pork closest to the core sensing point. The array formed by all cold chain sensing units is called the abnormal sensing array. The cold chain sensing unit includes a volatile basic nitrogen rapid sensing module and a microbial metabolic gas sensor. The volatile basic nitrogen rapid sensing module is used to detect the content of TVB-N, and the microbial metabolic gas sensor is used to detect the content of characteristic detection gas. The gases that are easily produced when pork spoils are recorded as characteristic detection gases; the detection indicators of TVB-N and all characteristic detection gases during the cold chain transportation of pork are obtained and recorded as standard indicators of TVB-N and all characteristic detection gases, respectively, and the value corresponding to each standard indicator is recorded as the standard judgment value of the standard indicator.

7. A multi-source data storage method for pork cold chain compliance testing according to claim 6, characterized in that, Obtaining the abnormal sensing features of each carriage also includes: Keeping the compartments used for cold chain transportation of pork and the position of the pork space inside the compartments unchanged, conduct k simulated transportations; for any interlocking compartment β: when the pork is placed in the interlocking compartment β and the door is closed, acquire the detection results of the sensors in all cold chain sensing units every tmin. For any cold chain sensing unit: when the TVB-N content in the detection result is outside the standard index of TVB-N, [(p1-p2) / p2]×1000 is recorded as the core difference value, where p1 is the TVB-N content in the detection result, p2 is the standard judgment value of the standard index of TVB-N, and the core difference value only retains the integer part.

8. A multi-source data storage method for pork cold chain compliance testing according to claim 7, characterized in that, Obtaining the abnormal sensing features of each carriage also includes: When the content of the characteristic gas in the test result is outside the standard index corresponding to the characteristic gas, [(q1-q2) / q2] is recorded as the co-discrepancy value, where q1 is the content of the characteristic gas in the test result and q2 is the standard judgment value of the standard index of the characteristic gas. The sum of the core difference value and all collaborative difference values ​​corresponding to the cold chain sensing unit in all test results is recorded as the difference parameter of the cold chain sensing unit. Let M be the differential parameter of the cold chain sensing unit corresponding to the core sensing point, and let M' be the maximum and minimum value of the differential parameters of the cold chain sensing units corresponding to all distributed sensing points. min and M max ; the interval [MM max MM min [This is denoted as a single sensing characteristic of the linked carriage β;] Obtain all single-sensor features corresponding to the linked carriage β in k simulated transportations, and denote the union of all single-sensor features as the abnormal sensor feature of the linked carriage β.

9. A multi-source data storage method for pork cold chain compliance testing according to claim 8, characterized in that, Based on the abnormal sensing characteristics of all carriages, a transportation sensing evolution map is obtained, and the transportation sensing evolution map is recorded as evidence. The standards include: Establish a Cartesian coordinate system, denoted as the sensing analysis coordinate system. The Y-axis of the sensing analysis coordinate system is a constant axis, and the coordinate points along the X-axis from the origin to the right are named sequentially from the movable carriage KL1 to the movable carriage KL. r ; For any movable carriage β, in the straight line X = movable carriage β, the area where the abnormal sensing feature of movable carriage β is located with the vertical coordinate is denoted as the sensing feature area of ​​movable carriage β.

10. A multi-source data storage method for pork cold chain compliance testing according to claim 9, characterized in that, Based on the abnormal sensing characteristics of all carriages, a transportation sensing evolution map is obtained. Recording this transportation sensing evolution map as evidence also includes: Mark the sensing feature regions of all interlocking carriages in the sensing analysis coordinate system, and denote the curves obtained by fitting the highest point of all sensing feature regions and the curves obtained by fitting the lowest point of all sensing feature regions as evolution limit line A1 and evolution limit line A2, respectively. The evolution limit line A1 and the area between evolution limit lines A1 are denoted as the transportation sensing evolution diagram, and the transportation sensing evolution diagram is denoted as the evidence standard.

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