Risk monitoring system and method based on pork logistics distribution

Abstract

The invention relates to a risk monitoring system and method based on pork logistics distribution, and relates to the technical field of logistics distribution. And based on the data before the pork logistics distribution, the data in the pork logistics distribution process and the data after the pork logistics distribution is finished, carrying out risk monitoring on the pork logistics distribution. Wherein the data before pork logistics distribution and the data after pork logistics distribution are used for determining a risk monitoring result, the data in the pork logistics distribution process is used for determining another risk monitoring result, and the two risk monitoring results are combined to determine a final risk monitoring result representing whether a risk exists in the pork logistics distribution process or not. According to the technical scheme, by integrating the risk monitoring results of different dimensions, a more accurate risk monitoring result conforming to the actual situation can be obtained, so that the accuracy of risk monitoring of pork logistics distribution is improved, and the reliability and stability of pork logistics distribution are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of logistics and distribution, and in particular, to a risk monitoring system and method based on pork logistics and distribution. Background Art

[0002] With the development of logistics and distribution technology, cold chain logistics and distribution have also developed. Cold chain logistics and distribution can be widely used in the distribution of various fresh goods, such as pork. In the pork logistics and distribution scenario, it is necessary to ensure that pork will not be affected by logistics and distribution as much as possible. Therefore, in the pork logistics and distribution scenario, risk monitoring of pork logistics and distribution is usually carried out to ensure the stability and reliability of pork logistics and distribution. Summary of the invention

[0003] The purpose of the present invention is to provide a risk monitoring system and method based on pork logistics distribution. The risk monitoring system and method based on pork logistics distribution can improve the accuracy of risk monitoring of pork logistics distribution, thereby improving the reliability and stability of pork logistics distribution.

[0004] In order to achieve the above-mentioned objectives, in a first aspect, the present disclosure provides a risk monitoring method based on pork logistics distribution, comprising: obtaining pork logistics distribution data, the pork logistics distribution data comprising: first data before pork logistics distribution, second data during pork logistics distribution and third data after the end of pork logistics distribution, the data type of the first data is the same as the data type of the third data, and the data type of the second data is different from the data type of the first data; determining a first risk monitoring result based on the first data and the third data; determining a second risk monitoring result based on the second data; determining a target risk monitoring result based on the first risk monitoring result and the second risk monitoring result, the target risk monitoring result being used to characterize whether there is a risk in the pork logistics distribution process.

[0005] Optionally, the first data includes: first pork inspection data, first pork quantity and first pork weight, the first pork inspection data is used to characterize the pork quality before pork logistics distribution, and the third data includes: second pork inspection data, second pork quantity and second pork weight, the second pork inspection data is used to characterize the pork quality after the pork logistics distribution is completed; the risk monitoring method also includes: before the pork logistics distribution, performing a quantity inspection on multiple portions of pork to be distributed to obtain the first pork quantity, and performing a weight inspection on multiple portions of pork to be distributed to obtain the first pork weight, and performing a sampling inspection on multiple portions of pork to be distributed based on the first pork quantity and the first pork weight to obtain the first pork inspection data; after the pork logistics distribution is completed, performing a quantity inspection on multiple portions of pork that have been distributed to obtain the second pork quantity, and performing a weight inspection on multiple portions of pork that have been distributed to obtain the second pork weight, and performing a sampling inspection on multiple portions of pork that have been distributed based on the second pork quantity and the second pork weight to obtain the second pork inspection data.

[0006] Optionally, the sampling quantity of the multiple servings of pork to be delivered is determined based on the first quantity of pork, and the sampling weight of the multiple servings of pork to be delivered is determined based on the first weight of pork; the sampling quantity of the multiple servings of pork that have been delivered is determined based on the second quantity of pork, and the sampling weight of the multiple servings of pork that have been delivered is determined based on the second weight of pork; wherein the sampling quantity of the multiple servings of pork to be delivered is less than the sampling quantity of the multiple servings of pork that have been delivered, and the number of sampling inspection items for the multiple servings of pork to be delivered is less than the number of sampling inspection items for the multiple servings of pork that have been delivered.

[0007] Optionally, determining a first risk monitoring result based on the first data and the third data includes: determining a predicted deterioration rate of pork during the pork logistics and distribution process based on the first pork inspection data, the first pork quantity and the first pork weight; determining an actual deterioration rate of pork during the pork logistics and distribution process based on the first pork inspection data and the second pork inspection data; determining a first deterioration impact value based on the first pork quantity and the second pork quantity; determining a second deterioration impact value based on the first pork weight and the second pork weight; determining the first risk monitoring result based on the predicted deterioration rate, the actual deterioration rate, the first deterioration impact value and the second deterioration impact value, and the first risk monitoring result is used to characterize whether there is a deterioration risk in the pork logistics and distribution.

[0008] Optionally, determining the first risk monitoring result based on the predicted spoilage rate, the actual spoilage rate, the first spoilage impact value and the second spoilage impact value includes: determining a reference spoilage rate based on the first spoilage impact value and the second spoilage impact value; if the actual spoilage rate is the maximum spoilage rate among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, determining that the first risk monitoring result indicates that there is a spoilage risk in pork logistics and distribution; if the predicted spoilage rate is the maximum spoilage rate among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, determining the difference between the predicted spoilage rate and the reference spoilage rate, and determining the difference between the actual spoilage rate and the reference spoilage rate; if the difference between the predicted spoilage rate and the reference spoilage rate is smaller than the difference between the actual spoilage rate and the reference spoilage rate, determining that the first risk monitoring result indicates that there is a spoilage risk in pork logistics and distribution.

[0009] Optionally, the second data includes: pork logistics distribution temperature, pork logistics distribution humidity, pork logistics distribution route information and pork logistics distribution time; determining the second risk monitoring result based on the second data includes: determining the first logistics distribution impact value of pork based on the pork logistics distribution temperature and the pork logistics distribution humidity; determining the second logistics distribution impact value of pork based on the pork logistics distribution route information; determining the third logistics distribution impact value of pork based on the pork logistics distribution time; determining the second risk monitoring result based on the first logistics distribution impact value, the second logistics distribution impact value, the third logistics distribution impact value and the preset logistics distribution impact value, and the second risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution environment.

[0010] Optionally, the pork logistics distribution temperature includes the pork storage environment temperature and the external environment temperature, and the pork logistics distribution humidity includes the pork storage environment humidity and the external environment humidity, and determining the first logistics distribution impact value of pork based on the pork logistics distribution temperature and the pork logistics distribution humidity includes: comparing the pork storage environment temperature with the reference storage environment temperature, and comparing the external environment temperature with the reference external environment temperature to obtain a first comparison result; comparing the pork storage environment humidity with the reference storage environment humidity, and comparing the external environment humidity with the reference external environment humidity to obtain a second comparison result; determining the temperature impact value based on the first comparison result and the reference temperature impact value; determining the humidity impact value based on the second comparison result and the reference humidity impact value; determining the first logistics distribution impact value based on the temperature impact value and the humidity impact value.

[0011] Optionally, the first risk monitoring result is used to characterize whether there is a risk of deterioration in the pork logistics distribution, and the second risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution environment. Determining the target risk monitoring result based on the first risk monitoring result and the second risk monitoring result includes: if the first risk monitoring result characterizes that there is a risk of deterioration in the pork logistics distribution, and / or the second risk monitoring result characterizes that there is a risk in the pork logistics distribution environment, determining that the target risk monitoring result characterizes that there is a risk in the pork logistics distribution process; if the first risk monitoring result characterizes that there is no risk of deterioration in the pork logistics distribution, and the second risk monitoring result characterizes that there is no risk in the pork logistics distribution environment, determining that the target risk monitoring result characterizes that there is no risk in the pork logistics distribution process.

[0012] Optionally, the risk monitoring method also includes: obtaining a distribution strategy corresponding to the pork logistics distribution data; if the target risk monitoring result indicates that there is a risk in the pork logistics distribution process, optimizing the distribution strategy according to the first risk monitoring result and the second risk monitoring result to obtain an optimized distribution strategy, and feeding back the optimized distribution strategy and risk warning information; if the target risk monitoring result indicates that there is no risk in the pork logistics distribution process, storing the distribution strategy in a preset distribution strategy library, and the distribution strategies in the preset distribution strategy library are used for logistics distribution of meat.

[0013] In a second aspect, the present disclosure provides a risk monitoring system based on pork logistics distribution, comprising: a data acquisition device, which is configured to collect pork logistics distribution data; a risk monitoring device, which is communicatively connected to the data acquisition device and is configured to execute the risk monitoring method based on pork logistics distribution as described in the first aspect.

[0014] Through the above technical solution, risk monitoring of pork logistics distribution is carried out based on the data before pork logistics distribution, the data during pork logistics distribution, and the data after pork logistics distribution. Among them, the data before pork logistics distribution and the data after pork logistics distribution are used to determine a risk monitoring result, and the data during pork logistics distribution are used to determine another risk monitoring result. Then, by combining the two risk monitoring results, the final risk monitoring result that characterizes whether there is a risk in the pork logistics distribution process can be determined. This risk monitoring method determines the risk monitoring results of different dimensions based on different data. By integrating the risk monitoring results of different dimensions, a more accurate and actual risk monitoring result can be obtained, thereby improving the accuracy of risk monitoring of pork logistics distribution, and then improving the reliability and stability of pork logistics distribution.

[0015] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0017] Figure 1 It is a structural block diagram of a risk monitoring system based on pork logistics distribution according to an exemplary embodiment.

[0018] Figure 2 It is a schematic diagram showing a data collection process according to an exemplary embodiment.

[0019] Figure 3 It is a flow chart of a risk monitoring method based on pork logistics distribution according to an exemplary embodiment.

[0020] Figure 4 The present invention is a risk monitoring flow chart shown according to an exemplary embodiment.

[0021] Figure 5 It is a structural block diagram of a risk monitoring device based on pork logistics distribution according to an exemplary embodiment.

[0022] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0024] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0025] With the development of logistics and distribution technology, cold chain logistics and distribution have also developed. Cold chain logistics and distribution can be widely used in the distribution of various fresh goods, such as pork. In the pork logistics and distribution scenario, it is necessary to ensure that pork will not be affected by logistics and distribution as much as possible. Therefore, in the pork logistics and distribution scenario, risk monitoring of pork logistics and distribution is usually carried out to ensure the stability and reliability of pork logistics and distribution.

[0026] In the related art, the risk monitoring of pork logistics and distribution usually adopts real-time monitoring, that is, real-time risk monitoring is carried out during the pork logistics and distribution process. Although this risk monitoring method can play a real-time monitoring role, the effect of real-time risk processing based on real-time risk monitoring results is not obvious, that is, the significance of real-time risk monitoring is not great. In addition, due to the single data dimension of real-time monitoring, the accuracy of the risk monitoring results obtained is also poor.

[0027] Based on this, the embodiment of the present disclosure provides a risk monitoring solution, which covers the data before pork logistics distribution, during pork logistics distribution, and after pork logistics distribution, analyzes the risk monitoring results of different dimensions, and then integrates the risk monitoring results of different dimensions to obtain high-precision and actual risk monitoring results. Based on the risk monitoring results, a more reasonable pork logistics distribution plan can be formulated, thereby improving the reliability and stability of pork logistics distribution.

[0028] Figure 1 is a structural block diagram of a risk monitoring system based on pork logistics distribution according to an exemplary embodiment. Figure 1 As shown, the risk monitoring system includes: a data acquisition device and a risk monitoring device, wherein the data acquisition device is communicatively connected with the risk monitoring device.

[0029] The data collection device is configured to collect pork logistics and distribution data. Since the pork logistics and distribution data disclosed in the present invention involves data from three stages: before pork logistics and distribution, after pork logistics and distribution, and during pork logistics and distribution, the data collection device can be regarded as a data back-end processing platform, which can pre-process the data at different stages into data that meets certain specifications and requirements, and then use it as pork logistics and distribution data for risk monitoring equipment to implement risk monitoring.

[0030] Figure 2 is a schematic diagram of a data collection process according to an exemplary embodiment. Figure 2 As shown, the corresponding data are collected before pork logistics distribution, after pork logistics distribution, and during pork logistics distribution. The collection of these corresponding data can be achieved through corresponding data collection methods. For specific implementation methods, please refer to the introduction of subsequent embodiments. Then, the data of the three stages are transmitted to the data collection device, and the data collection device summarizes and standardizes these data. When the risk monitoring device needs these data, it can be directly obtained from the data collection device.

[0031] Regarding the risk monitoring equipment, it can be regarded as a data processing device, which can analyze the pork logistics distribution data to obtain the risk monitoring results. In addition, the risk monitoring equipment also has a visual display function, which can display the risk monitoring results, etc., which is not limited here.

[0032] Figure 3 is a flow chart of a risk monitoring method based on pork logistics distribution according to an exemplary embodiment. Figure 3 As shown, the risk monitoring method can be applied to the aforementioned risk monitoring device, which includes the following steps:

[0033] Step S31, obtaining pork logistics distribution data, the pork logistics distribution data includes: first data before pork logistics distribution, second data during pork logistics distribution and third data after pork logistics distribution. The data type of the first data is the same as the data type of the third data, and the data type of the second data is different from the data type of the first data.

[0034] Step S32: determining a first risk monitoring result according to the first data and the third data.

[0035] Step S33: determining a second risk monitoring result according to the second data.

[0036] Step S34, determining a target risk monitoring result based on the first risk monitoring result and the second risk monitoring result, wherein the target risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution process.

[0037] In step S31, the first data before pork logistics distribution, the second data during pork logistics distribution, and the third data after pork logistics distribution are involved, and the data type of the first data is the same as the data type of the third data, and the data type of the second data is different from the data type of the first data. That is, before pork logistics distribution and after pork logistics distribution, the same type of data is monitored, while during pork logistics distribution, data different from the data types before pork logistics distribution and after pork logistics distribution are monitored.

[0038] As an optional implementation, the first data includes: first pork inspection data, first pork quantity and first pork weight, the first pork inspection data is used to characterize the quality of pork before pork logistics distribution, and the third data includes: second pork inspection data, second pork quantity and second pork weight.

[0039] Correspondingly, the acquisition of the first data and the third data may include: before the logistics distribution of pork, conducting a quantity inspection on the multiple portions of pork to be distributed to obtain a first pork quantity, and conducting a weight inspection on the multiple portions of pork to be distributed to obtain a first pork weight, and performing a sampling inspection on the multiple portions of pork to be distributed based on the first pork quantity and the first pork weight to obtain first pork inspection data; after the logistics distribution of pork is completed, conducting a quantity inspection on the multiple portions of pork that have been distributed to obtain a second pork quantity, and conducting a weight inspection on the multiple portions of pork that have been distributed to obtain a second pork weight, and performing a sampling inspection on the multiple portions of pork that have been distributed based on the second pork quantity and the second pork weight to obtain second pork inspection data.

[0040] In this embodiment, before the logistics distribution of pork, the quantity of the multiple portions of pork to be distributed can be detected to obtain the first quantity of pork. For example, the multiple portions of pork to be distributed are transmitted to the logistics distribution device (for example, the cargo box of a cold chain vehicle) by a transmission device (for example, a conveyor belt), and a counting device can be installed on the transmission device, and the counting device automatically counts. For example, the quantity of the multiple portions of pork to be distributed is information known to the distribution personnel, and the distribution personnel can directly upload the quantity to the data collection device.

[0041] Also, before the logistics distribution of pork, the weight of the multiple portions of pork to be distributed can be detected to obtain the first pork weight. For example, the multiple portions of pork to be distributed are transmitted to the logistics distribution device (for example, the cargo box of a cold chain vehicle) by a transmission device (for example, a conveyor belt), and a weight sensor can be installed on the transmission device to detect the weight. For example, the weight of the multiple portions of pork to be distributed is information known to the distribution personnel, and the distribution personnel can directly upload the weight to the data acquisition device.

[0042] In addition, regarding the above-mentioned first pork quantity and first pork weight, they may be the total quantity and the total amount, respectively.

[0043] In some embodiments, before the logistics distribution of pork, the quality of the multiple portions of pork to be distributed can also be tested to obtain the first pork test data. Regarding the first pork test data, it can include one or more information characterizing the quality, such as: one or more of meat quality, meat parts and meat ingredients. For example, the multiple portions of pork to be distributed are transmitted to the logistics distribution device (such as the cargo box of a cold chain vehicle) by a transmission device (such as a conveyor belt), and a pork quality analysis device can be installed on the transmission device, and the pork quality analysis device performs quality inspection. For example, the quality of the multiple portions of pork to be distributed is information known to the distribution personnel, and the distribution personnel can directly upload the corresponding quality data to the data acquisition device to obtain the test data.

[0044] Pork quality analysis equipment may include one or more of a meat quality detector, a meat component analyzer, and a meat part analyzer. For specific implementations of these devices, reference may be made to mature meat quality analysis technologies in the art, which will not be introduced in detail here.

[0045] In some embodiments, the first pork test data is data obtained based on sampling tests, that is, quality tests are not performed on all pork. Therefore, if an automated test method is used, quality tests can be performed on some pork at random according to the number of sampling tests. If manual data upload is used, the delivery personnel only need to upload the test data corresponding to the number of sampling tests.

[0046] In some embodiments, the number of sampling tests can be determined based on the first pork quantity and the first pork weight. For example, the ratio of the first pork weight to the first pork quantity is determined, and the ratio represents the average weight of each portion of pork. The larger the weight, the smaller the number of sampling tests can be, and the smaller the weight, the larger the number of sampling tests can be. That is, the number of sampling tests is inversely proportional to the average weight of each portion of pork.

[0047] In some embodiments, the sampling quantity of the multiple servings of pork to be delivered is determined based on a first quantity of pork, and the sampling weight of the multiple servings of pork to be delivered is determined based on the first weight of pork; the sampling quantity of the multiple servings of pork that have been delivered is determined based on a second quantity of pork, and the sampling weight of the multiple servings of pork that have been delivered is determined based on the second weight of pork; wherein the sampling quantity of the multiple servings of pork to be delivered is less than the sampling quantity of the multiple servings of pork that have been delivered, and the number of sampling inspection items for the multiple servings of pork to be delivered is less than the number of sampling inspection items for the multiple servings of pork that have been delivered.

[0048] In this embodiment, in addition to the sampling quantity, the sampling weight is also considered. Therefore, the sampling weight of the multiple portions of pork to be delivered can be directly proportional to the first pork quantity, and the sampling weight of the multiple portions of pork to be delivered can be directly proportional to the first pork weight. Also, the sampling quantity of the multiple portions of pork delivered is directly proportional to the second pork quantity, and the sampling weight of the multiple portions of pork delivered is directly proportional to the second pork weight.

[0049] Furthermore, the number of samples of the multiple portions of pork to be delivered is less than the number of samples of the multiple portions of pork that have been delivered, and the number of sampled test items of the multiple portions of pork to be delivered is less than the number of sampled test items of the multiple portions of pork that have been delivered. That is, the pork that has been delivered needs to be tested for more portions of pork and more quality items. For example, the moisture content of the pork before delivery is not tested, while the moisture content of the pork that has been delivered needs to be tested, etc.

[0050] In some embodiments, the method for obtaining the second pork quantity, the second pork weight, and the second pork detection data can refer to the method for obtaining the first pork quantity, the first pork weight, and the first pork detection data. The difference is that if automated detection is used, the automated detection process can occur in the process of transferring the multiple portions of pork that have been delivered from the logistics distribution device (such as the cold chain truck box) to the corresponding storage device through a conveying device (such as a conveyor belt). And, if manual uploading is used, manual detection is required before uploading.

[0051] It can be understood that the first data before the logistics distribution of pork is regarded as the initial data of pork. If the logistics distribution process has a significant impact on pork, the third data after the logistics distribution of pork will be significantly different from the first data. Therefore, through the analysis of the first data and the third data, a risk monitoring result can be determined.

[0052] Further, in step S32, a first risk monitoring result is determined according to the first data and the third data.

[0053] As an optional implementation, step S32 includes: determining the predicted spoilage rate of pork during the pork logistics and distribution process based on the first pork detection data, the first pork quantity and the first pork weight; determining the actual spoilage rate of pork during the pork logistics and distribution process based on the first pork detection data and the second pork detection data; determining a first spoilage impact value based on the first pork quantity and the second pork quantity; determining a second spoilage impact value based on the first pork weight and the second pork weight; determining a first risk monitoring result based on the predicted spoilage rate, the actual spoilage rate, the first spoilage impact value and the second spoilage impact value, and the first risk monitoring result is used to characterize whether there is a spoilage risk in the pork logistics and distribution.

[0054] In some embodiments, the first pork detection data, the first pork quantity and the first pork weight are input into a pre-trained model to obtain a predicted spoilage rate output by the pre-trained model.

[0055] In some embodiments, the training process of the pre-trained model includes: obtaining a training data set, the training data set including a plurality of initial pork data and spoilage rate labels corresponding to the plurality of initial pork data, each type of initial pork data including pork detection data, pork quantity and pork weight; based on the training data set, training the model to be trained to obtain a pre-trained model.

[0056] In some embodiments, the pre-trained model can be a neural network model, a large model, etc.

[0057] In some embodiments, the deterioration rate label may be a manually marked label or a label obtained through actual measurement.

[0058] It can be understood that the spoilage rate prediction can be a relatively objective spoilage rate predicted by analyzing the initial pork data. The objective spoilage rate is a spoilage rate allowed by pork logistics and distribution.

[0059] In some embodiments, the real deterioration rate can be determined by analyzing the first pork test data and the second pork test data. For example, the difference between the specific values ​​of the same quality test item in the first pork test data and the second pork test data is compared, and a deterioration rate is determined based on the difference, for example: the greater the difference, the higher the deterioration rate. And according to the value of the newly added quality test item in the second pork test data, a deterioration rate is determined, for example: a threshold value of the test item is preset, and the deterioration rate is determined according to the difference between the actual test value and the threshold value. Finally, the two deterioration rates are integrated to obtain the final real deterioration rate. For example, the integrated weight of the deterioration rate determined by comparing the difference is higher than the integrated weight of the deterioration rate determined by comparing the threshold value.

[0060] In some embodiments, the greater the difference between the first pork quantity and the second pork quantity, the greater the first spoilage impact value. Therefore, the first spoilage impact value is directly proportional to the difference between the first pork quantity and the second pork quantity. The greater the difference between the first pork weight and the second pork weight, the greater the second spoilage impact value. Therefore, the second spoilage impact value is directly proportional to the difference between the first pork weight and the second pork weight.

[0061] In the embodiments of the present disclosure, the determination of the direct proportional relationship and the inverse proportional relationship can be determined through a large amount of measured data. For details, reference can be made to mature data analysis techniques in the art, which will not be introduced in detail here.

[0062] In some embodiments, determining a first risk monitoring result based on the predicted spoilage rate, the actual spoilage rate, the first spoilage impact value and the second spoilage impact value may include: determining a reference spoilage rate based on the first spoilage impact value and the second spoilage impact value; if the actual spoilage rate is the maximum spoilage rate among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, determining that the first risk monitoring result indicates that there is a spoilage risk in pork logistics and distribution; if the predicted spoilage rate is the maximum spoilage rate among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, determining the difference between the predicted spoilage rate and the reference spoilage rate, and determining the difference between the actual spoilage rate and the reference spoilage rate; if the difference between the predicted spoilage rate and the reference spoilage rate is smaller than the difference between the actual spoilage rate and the reference spoilage rate, determining that the first risk monitoring result indicates that there is a spoilage risk in pork logistics and distribution.

[0063] In this embodiment, the first deterioration impact value and the second deterioration impact value are first integrated into a final deterioration impact value, for example, weighted average integration. Then, based on the deterioration impact value, a reference deterioration rate is determined. For example, the higher the deterioration impact value, the higher the reference deterioration rate, and the reference deterioration rate is within a preset range, which is a smaller deterioration rate range, for example, 0% to 10%.

[0064] Therefore, the reference spoilage rate is the minimum spoilage rate among the three spoilage rates, and is the highest spoilage rate allowed without considering the impact of the logistics distribution process on pork.

[0065] Then, the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate are sorted in order. If the actual spoilage rate is the maximum spoilage rate, it can be directly determined that there is a spoilage risk in pork logistics distribution. If the predicted spoilage rate is the maximum spoilage rate, the difference between the predicted spoilage rate and the reference spoilage rate is further determined, as well as the difference between the actual spoilage rate and the reference spoilage rate.

[0066] If the difference between the predicted spoilage rate and the reference spoilage rate is smaller than the difference between the actual spoilage rate and the reference spoilage rate, it means that the predicted spoilage rate is closer to the reference spoilage rate, and it can be determined that there is a risk of spoilage in pork logistics distribution.

[0067] It is understandable that in other cases, it can be determined that there is no risk of spoilage in the logistics distribution of pork.

[0068] In step S33, a second risk monitoring result is determined according to the second data.

[0069] In some embodiments, the second data includes: pork logistics distribution temperature, pork logistics distribution humidity, pork logistics distribution route information and pork logistics distribution time.

[0070] The temperature of pork logistics distribution can be detected by temperature sensors. The humidity of pork logistics distribution can be detected by humidity sensors. The route information of pork logistics distribution can be detected by the navigation system of the cold chain logistics distribution vehicle, or by a third-party navigation application. The duration of pork logistics distribution can be determined by the end time and the start time of distribution, which can be recorded by the logistics distribution system.

[0071] For example, pork logistics distribution route information may include road conditions, such as road type, road complexity, etc.

[0072] As an optional implementation, step S33 includes: determining a first logistics distribution impact value of pork based on the pork logistics distribution temperature and the pork logistics distribution humidity; determining a second logistics distribution impact value of pork based on the pork logistics distribution route information; determining a third logistics distribution impact value of pork based on the pork logistics distribution time; determining a second risk monitoring result based on the first logistics distribution impact value, the second logistics distribution impact value, the third logistics distribution impact value and the preset logistics distribution impact value, and the second risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution environment.

[0073] In some embodiments, the pork logistics distribution temperature includes the pork storage environment temperature and the external environment temperature, and the pork logistics distribution humidity includes the pork storage environment humidity and the external environment humidity. The storage environment may be a cold chain logistics distribution vehicle cargo box, and the external environment may be the environment where the cold chain logistics distribution vehicle is located.

[0074] Therefore, determining the first logistics distribution impact value of pork based on the pork logistics distribution temperature and the pork logistics distribution humidity can include: comparing the pork storage environment temperature with the reference storage environment temperature, and comparing the external environment temperature with the reference external environment temperature to obtain a first comparison result; comparing the pork storage environment humidity with the reference storage environment humidity, and comparing the external environment humidity with the reference external environment humidity to obtain a second comparison result; determining the temperature impact value based on the first comparison result and the reference temperature impact value; determining the humidity impact value based on the second comparison result and the reference humidity impact value; determining the first logistics distribution impact value based on the temperature impact value and the humidity impact value.

[0075] In some embodiments, the first comparison result may be a difference between the ambient temperature and a reference ambient temperature, and the second comparison result may be a difference between the ambient humidity and the reference ambient humidity.

[0076] In some embodiments, the reference temperature impact value may be a preset impact value caused by temperature, and may be increased or decreased based on the reference temperature impact value according to the first comparison result to determine the temperature impact value. For example, the greater the difference between the ambient temperature and the reference ambient temperature, the greater the increase in the reference temperature impact value.

[0077] In some embodiments, the reference humidity impact value may be a preset impact value caused by humidity, and may be increased or decreased based on the reference humidity impact value according to the second comparison result to determine the humidity impact value. For example, the greater the difference between the external environment humidity and the reference external environment humidity, the greater the increase in the reference humidity impact value.

[0078] Furthermore, the first logistics distribution impact value may be determined by weighted summing the temperature impact value and the humidity impact value, wherein the weights corresponding to the temperature impact value and the humidity impact value may be equal.

[0079] In some embodiments, different logistics distribution impact values ​​corresponding to different pork logistics distribution route information may be pre-configured, and based on the pre-configured correspondence and the current pork logistics distribution route information, the second logistics distribution impact value of pork may be determined. For example, the worse the road conditions and the more complex the road conditions, the higher the second logistics distribution impact value. For example, the logistics distribution impact value of a highway is lower than that of an ordinary road, and the logistics distribution impact value of a railway is lower than that of a highway.

[0080] In some embodiments, the logistics distribution time of pork is directly proportional to the third logistics distribution impact value of pork, that is, the longer the logistics distribution time of pork, the higher the third logistics distribution impact value.

[0081] In some embodiments, the first logistics distribution impact value, the second logistics distribution impact value, and the third logistics distribution impact value are weighted and summed to obtain a final logistics distribution impact value, wherein the weights corresponding to the first logistics distribution impact value, the second logistics distribution impact value, and the third logistics distribution impact value can be from high to low, and their sum is 1.

[0082] In some embodiments, the preset logistics and distribution impact value may be an impact value preset according to the logistics and distribution conditions of pork, which may represent the highest allowable logistics and distribution impact value.

[0083] In some embodiments, the final logistics distribution impact value is compared with the preset logistics distribution impact value. If the final logistics distribution impact value is less than or equal to the preset logistics distribution impact value, it is determined that there is no risk in the pork logistics distribution environment; otherwise, it is determined that there is a risk in the pork logistics distribution environment.

[0084] Therefore, the first risk monitoring result is used to characterize whether there is a risk of deterioration in the pork logistics distribution, and the second risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution environment. In step S34, the target risk monitoring result is determined based on the first risk monitoring result and the second risk monitoring result.

[0085] As an optional implementation, step S34 includes: if the first risk monitoring result indicates that there is a risk of deterioration in the pork logistics distribution, and / or the second risk monitoring result indicates that there is a risk in the pork logistics distribution environment, determining that the target risk monitoring result indicates that there is a risk in the pork logistics distribution process; if the first risk monitoring result indicates that there is no risk of deterioration in the pork logistics distribution, and the second risk monitoring result indicates that there is no risk in the pork logistics distribution environment, determining that the target risk monitoring result indicates that there is no risk in the pork logistics distribution process.

[0086] In this embodiment, when both risk monitoring results indicate that there is no risk, it is determined that there is no risk in the pork logistics and distribution process. Otherwise, it is determined that there is a risk in the pork logistics and distribution process.

[0087] In some embodiments, risk processing may be performed after determining the target risk monitoring result.

[0088] Therefore, as an optional implementation, the method also includes: obtaining a distribution strategy corresponding to pork logistics distribution data; if the target risk monitoring result indicates that there is a risk in the pork logistics distribution process, optimizing the distribution strategy according to the first risk monitoring result and the second risk monitoring result to obtain an optimized distribution strategy, and feeding back the optimized distribution strategy and risk warning information; if the target risk monitoring result indicates that there is no risk in the pork logistics distribution process, storing the distribution strategy in a preset distribution strategy library, and the distribution strategy in the preset distribution strategy library is used for logistics distribution of meat.

[0089] In some embodiments, the distribution strategy corresponding to the pork logistics distribution data may include: estimated distribution time, estimated distribution route, estimated distribution device, estimated distribution personnel and other information. Thus, when it is determined that there is a risk, adaptive distribution strategy optimization can be performed based on the first risk monitoring result and the second risk monitoring result. For example, if the first risk monitoring result indicates that there is no risk, and the second risk monitoring result indicates that there is a risk, the relevant equipment involved in the distribution process can be optimized, such as optimizing refrigeration equipment, optimizing distribution routes, optimizing distribution time, etc. If the first risk monitoring result indicates that there is a risk, and the second risk monitoring result indicates that there is no risk, external influencing factors can be optimized, such as optimizing distribution personnel, increasing supervisors, etc. If both the first risk monitoring result and the second risk monitoring result indicate that there is a risk, the above two optimization methods can be integrated.

[0090] In some embodiments, the optimized distribution strategy and risk warning information can be fed back to the managers of pork logistics distribution.

[0091] In some embodiments, the preset delivery strategy library stores multiple delivery strategies, which can be used as reference delivery strategies. If there is no risk, the current delivery strategy is considered to be more reasonable. Therefore, it is stored in the preset delivery strategy library and can be used for selection in other delivery situations.

[0092] Figure 4 is a risk monitoring flow chart according to an exemplary embodiment. Figure 4As shown in the figure, in this risk monitoring process, after the pork logistics distribution process is executed, three stages of data are generated respectively. Based on the three stages of data, two risk monitoring results are obtained. The two risk monitoring results are integrated to obtain the final risk monitoring result.

[0093] Furthermore, the final risk monitoring result and the two risk monitoring results are used to optimize the distribution strategy or update the preset distribution strategy library.

[0094] Therefore, the technical solution of the embodiment of the present disclosure can improve the accuracy of risk monitoring of pork logistics and distribution, and thus ensure the reliability and stability of pork logistics and distribution.

[0095] Figure 5 is a structural block diagram of a risk monitoring device based on pork logistics distribution according to an exemplary embodiment. Figure 5 As shown, the device comprises:

[0096] Acquisition module 501 is used to acquire pork logistics distribution data, and the pork logistics distribution data includes: first data before pork logistics distribution, second data during pork logistics distribution, and third data after the end of pork logistics distribution. The data type of the first data is the same as the data type of the third data, and the data type of the second data is different from the data type of the first data.

[0097] The risk monitoring module 502 is used to determine a first risk monitoring result based on the first data and the third data; determine a second risk monitoring result based on the second data; and determine a target risk monitoring result based on the first risk monitoring result and the second risk monitoring result, wherein the target risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution process.

[0098] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0099] Figure 6 FIG. 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. Figure 6 As shown, the electronic device 600 may include: a processor 601 and a memory 602. The electronic device 600 may also include one or more of a multimedia component 603, an input / output (I / O) interface 604, and a communication component 605.

[0100] The processor 601 is used to control the overall operation of the electronic device 600 to complete all or part of the steps in the risk monitoring method based on pork logistics distribution. The memory 602 is used to store various types of data to support the operation of the electronic device 600, which may include instructions for any application or method used to operate on the electronic device 600, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 603 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 602 or sent through the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 604 provides an interface between the processor 601 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 605 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0101] In an exemplary embodiment, the electronic device 600 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned risk monitoring method based on pork logistics distribution.

[0102] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the risk monitoring method based on pork logistics distribution are implemented. For example, the computer-readable storage medium can be the memory 602 including the program instructions, and the program instructions can be executed by the processor 601 of the electronic device 600 to complete the risk monitoring method based on pork logistics distribution.

[0103] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0104] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0105] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A risk monitoring method based on pork logistics distribution, characterized in that: include: Obtain pork logistics distribution data, the pork logistics distribution data including: first data before pork logistics distribution, second data during pork logistics distribution, and third data after pork logistics distribution, the data type of the first data is the same as the data type of the third data, and the data type of the second data is different from the data type of the first data; Determining a first risk monitoring result according to the first data and the third data; Determining a second risk monitoring result according to the second data; A target risk monitoring result is determined based on the first risk monitoring result and the second risk monitoring result, and the target risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution process.

2. The risk monitoring method according to claim 1, characterized in that: The first data includes: first pork detection data, first pork quantity and first pork weight, the first pork detection data is used to characterize the pork quality before pork logistics distribution, the third data includes: second pork detection data, second pork quantity and second pork weight, the second pork detection data is used to characterize the pork quality after pork logistics distribution; the risk monitoring method also includes: Before the logistics distribution of pork, the quantity of the multiple portions of pork to be distributed is detected to obtain the first quantity of pork, and the weight of the multiple portions of pork to be distributed is detected to obtain the first weight of pork, and according to the first quantity of pork and the first weight of pork, the multiple portions of pork to be distributed are sampled and tested to obtain the first pork test data; After the logistics distribution of pork is completed, the quantity of the multiple delivered pork is detected to obtain the second pork quantity, and the weight of the multiple delivered pork is detected to obtain the second pork weight. In addition, based on the second pork quantity and the second pork weight, the multiple delivered pork is sampled and tested to obtain the second pork detection data.

3. The risk monitoring method according to claim 2, characterized in that: The sampling quantity of the multiple portions of pork to be delivered is determined based on the first quantity of pork, and the sampling weight of the multiple portions of pork to be delivered is determined based on the first weight of pork; the sampling quantity of the multiple portions of pork that have been delivered is determined based on the second quantity of pork, and the sampling weight of the multiple portions of pork that have been delivered is determined based on the second weight of pork; wherein, the sampling quantity of the multiple portions of pork to be delivered is less than the sampling quantity of the multiple portions of pork that have been delivered, and the number of sampling inspection items for the multiple portions of pork to be delivered is less than the number of sampling inspection items for the multiple portions of pork that have been delivered.

4. The risk monitoring method according to claim 2 or 3, characterized in that: The determining a first risk monitoring result according to the first data and the third data includes: Determining a predicted deterioration rate of pork during pork logistics distribution according to the first pork detection data, the first pork quantity and the first pork weight; Determine the actual deterioration rate of pork during the pork logistics distribution process according to the first pork test data and the second pork test data; determining a first deterioration impact value according to the first pork quantity and the second pork quantity; determining a second deterioration impact value according to the first pork weight and the second pork weight; The first risk monitoring result is determined based on the predicted spoilage rate, the actual spoilage rate, the first spoilage impact value and the second spoilage impact value, and the first risk monitoring result is used to characterize whether there is a spoilage risk in pork logistics distribution.

5. The risk monitoring method according to claim 4, characterized in that: The determining the first risk monitoring result according to the predicted deterioration rate, the actual deterioration rate, the first deterioration impact value and the second deterioration impact value includes: determining a reference deterioration rate according to the first deterioration impact value and the second deterioration impact value; If among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, the actual spoilage rate is the maximum spoilage rate, it is determined that the first risk monitoring result indicates that there is a spoilage risk in pork logistics distribution; If among the predicted spoilage rate, the actual spoilage rate and the reference spoilage rate, the predicted spoilage rate is the maximum spoilage rate, the difference between the predicted spoilage rate and the reference spoilage rate is determined, and the difference between the actual spoilage rate and the reference spoilage rate is determined; if the difference between the predicted spoilage rate and the reference spoilage rate is smaller than the difference between the actual spoilage rate and the reference spoilage rate, it is determined that the first risk monitoring result indicates that there is a spoilage risk in pork logistics distribution.

6. The risk monitoring method according to claim 1, characterized in that: The second data includes: pork logistics distribution temperature, pork logistics distribution humidity, pork logistics distribution route information and pork logistics distribution time; the second risk monitoring result is determined based on the second data, including: Determining a first logistics distribution impact value of the pork according to the pork logistics distribution temperature and the pork logistics distribution humidity; Determining a second logistics distribution impact value of pork according to the pork logistics distribution route information; Determine the third logistics distribution impact value of pork according to the logistics distribution time of pork; A second risk monitoring result is determined based on the first logistics distribution impact value, the second logistics distribution impact value, the third logistics distribution impact value and the preset logistics distribution impact value, and the second risk monitoring result is used to characterize whether there is a risk in the pork logistics distribution environment.

7. The risk monitoring method according to claim 6, characterized in that: The pork logistics distribution temperature includes the pork storage environment temperature and the external environment temperature, the pork logistics distribution humidity includes the pork storage environment humidity and the external environment humidity, and determining the first logistics distribution impact value of pork according to the pork logistics distribution temperature and the pork logistics distribution humidity includes: Comparing the pork storage environment temperature with a reference storage environment temperature, and comparing the external environment temperature with a reference external environment temperature, to obtain a first comparison result; Comparing the pork storage environment humidity with a reference storage environment humidity, and comparing the external environment humidity with a reference external environment humidity, to obtain a second comparison result; Determining a temperature influence value according to the first comparison result and a reference temperature influence value; Determining a humidity impact value according to the second comparison result and a reference humidity impact value; The first logistics distribution impact value is determined according to the temperature impact value and the humidity impact value.

8. The risk monitoring method according to claim 1, characterized in that: The first risk monitoring result is used to characterize whether there is a risk of deterioration in the logistics and distribution of pork, and the second risk monitoring result is used to characterize whether there is a risk in the logistics and distribution environment of pork. The target risk monitoring result is determined according to the first risk monitoring result and the second risk monitoring result, including: If the first risk monitoring result indicates that there is a risk of spoilage in the pork logistics distribution, and / or the second risk monitoring result indicates that there is a risk in the pork logistics distribution environment, it is determined that the target risk monitoring result indicates that there is a risk in the pork logistics distribution process; If the first risk monitoring result indicates that there is no risk of spoilage in the pork logistics distribution, and the second risk monitoring result indicates that there is no risk in the pork logistics distribution environment, it is determined that the target risk monitoring result indicates that there is no risk in the pork logistics distribution process.

9. The risk monitoring method according to claim 1, characterized in that: The risk monitoring method further comprises: Obtaining the distribution strategy corresponding to the pork logistics distribution data; If the target risk monitoring result indicates that there is a risk in the pork logistics distribution process, the distribution strategy is optimized according to the first risk monitoring result and the second risk monitoring result to obtain an optimized distribution strategy, and the optimized distribution strategy and risk warning information are fed back; If the target risk monitoring result indicates that there is no risk in the pork logistics distribution process, the distribution strategy is stored in a preset distribution strategy library, and the distribution strategies in the preset distribution strategy library are used for logistics distribution of meat.

10. A risk monitoring system based on pork logistics distribution, characterized in that: include: A data collection device, wherein the data collection device is configured to collect pork logistics and distribution data; The risk monitoring device is communicatively connected to the data acquisition device and is configured to execute the risk monitoring method based on pork logistics distribution as described in any one of claims 1 to 9.

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