A Location Data Perception Method, Device and Medium Based on Identification Resolution

Abstract

The present invention relates to the technical field of identification signal compensation, and specifically relates to a positioning data perception method, device and medium based on identification resolution, including: obtaining identification data and signal strength of each agricultural product when passing through each region, and humidity at each sampling moment in each region; obtaining an environmental constraint coefficient of the current region; counting the number of missing identification data at the same sampling moment, and obtaining an environmental positioning error in combination with the environmental constraint coefficient; counting the missing situations of identification data at different sampling moments to obtain the frequency of identification loss; obtaining the signal coverage completeness according to the signal strength; obtaining the positioning coverage attenuation degree according to the consistency of the covariant relationship between the frequency of identification loss and the signal coverage completeness; generating compensation parameters for the current region; and performing sampling compensation by using the compensation parameters. The present invention aims to solve the problem of missing identification data caused by the influence of the environment and signal strength on the identification data, and improve the perception accuracy of positioning data.

Description

Technical Field

[0001] The present invention relates to the technical field of identification signal compensation, and particularly relates to a positioning data perception method, device and medium based on identification resolution. Background Art

[0002] With the rapid development of information technology and the Internet of Things, devices and application scenarios based on the Internet of Things are becoming increasingly rich, and positioning technology is playing an increasingly important role in fields such as intelligent manufacturing, logistics management, smart cities, and vehicle networking. In order to meet diverse business needs, efficiently and accurately perceiving and obtaining positioning data has become one of the core support technologies for Internet of Things applications. Among them, the identification resolution system, as a key infrastructure of the Internet of Things, provides important support for device positioning, resource tracking, and information management by assigning unique identifiers to physical objects and associating their relevant attributes and service resources.

[0003] However, in a complex and changing environment, the signal propagation process in existing identification resolution methods is affected by factors such as multipath effects, obstacle occlusion, and environmental medium changes, resulting in non-linear and uncertain signal attenuation rates. Traditional static signal propagation models such as path loss models cannot accurately reflect the actual signal attenuation characteristics under environmental influence, thus causing errors in the perception of positioning data. Summary of the Invention

[0004] The present invention provides a positioning data perception method, device and medium based on identification resolution to solve the existing problems.

[0005] The positioning data perception method, device and medium based on identification resolution of the present invention adopt the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a positioning data perception method based on identification resolution, the method comprising the following steps:

[0007] Collect the identifiers of all agricultural products in the same batch, and obtain the identifier data, signal strength at different sampling times when each agricultural product passes through each region, and the humidity at each sampling time in each region.

[0008] When the agricultural products in the same batch pass through the current region, obtain the environmental constraint coefficient of the current region, where the environmental constraint coefficient is positively correlated with the fluctuation of the humidity of the agricultural products in the current region; count the number of missing identifier data of all agricultural products at the same sampling time, and obtain the environmental positioning error of the current region in combination with the environmental constraint coefficient; count the missing situation of the identifier data of all agricultural products at different sampling times to obtain the frequency of identifier loss in the current region; obtain the signal coverage completeness of the current region according to the signal strength of the agricultural products in the same batch at different sampling times.

[0009] Obtain the frequency of identification loss and the signal coverage completeness for each region; obtain the positioning coverage attenuation degree of the current region according to the consistency of the covariation relationship between the frequency of identification loss and the signal coverage completeness of the current region and the regions passed through before the current region;

[0010] Generate a compensation parameter for the current region by using the positioning coverage attenuation degree and the environmental positioning error; use the compensation parameter to perform sampling compensation on the identification data of the next batch of agricultural products passing through the current region.

[0011] Preferably, the specific steps for obtaining the environmental constraint coefficient include:

[0012] Construct a humidity curve for the current region according to the humidity at each sampling moment in the current region, where the horizontal axis of the humidity curve is the sampling moment and the vertical axis is the humidity value;

[0013] Take all the maximum and minimum values in the humidity curve of the current region; record the range between each maximum value and the previous minimum value as the positive increment of the maximum value; calculate the average value of all the positive increments in the humidity curve of the current region;

[0014] Record the product of the average value of all the positive increments in the humidity curve of the current region and the variance of all the humidities in the humidity curve of the current region as the environmental constraint coefficient of the current region.

[0015] Preferably, the specific steps for obtaining the environmental positioning error include:

[0016] Record the number of identification data marked as missing among all agricultural products at the same sampling moment in the current region as the amount of missing identification data at this sampling moment in the current region;

[0017] Record the ratio of the average value of the amount of missing identification data at all sampling moments in the current region to the number of agricultural products as the missing rate of the current region;

[0018] Record the product of the missing rate and the environmental constraint coefficient of the current region as the environmental positioning error of the current region.

[0019] Preferably, the specific steps for obtaining the frequency of identification loss include:

[0020] When the same batch of agricultural products pass through the current region, record each piece of identification data marked as missing for each agricultural product at all sampling moments as a missing sampling point of the agricultural product;

[0021] Obtain the identification perception failure rate of each agricultural product in the current region according to the continuous missing situation of all the missing sampling points of each agricultural product;

[0022] Divide all the agricultural products in the current region according to the identification perception failure rate to obtain the agricultural products prone to missing in the current region;

[0023] Obtain the frequent loss rate of identification in the current area based on the proportion of perishable agricultural products in the current area and the failure rate of identification perception thereof.

[0024] Denote the product of the ratio of the number of perishable agricultural products in the same batch to the total number of agricultural products in the same batch and the failure rate of identification perception of all perishable agricultural products in the same batch as the frequent loss rate of identification in the current area.

[0025] Preferably, the specific steps for obtaining the failure rate of identification perception include:

[0026] For the i-th agricultural product in the current area, merge the consecutive missing sampling points of the i-th agricultural product to obtain several continuous missing segments of the i-th agricultural product.

[0027] When the i-th agricultural product passes through the current area, the failure rate of identification perception Yi i is calculated as follows:

[0028]

[0029] where G i is the total number of identification data when the i-th agricultural product passes through the current area, and g i is the total number of missing sampling points when the i-th agricultural product passes through the current area;

[0030] m i is the total number of continuous missing segments of the i-th agricultural product; Δt is the time length of the preset sampling interval, and Δtk k is the time length of the k-th continuous missing segment of the i-th agricultural product;

[0031] ω is the prior fault tolerance multiple.

[0032] Preferably, the specific steps for obtaining the perishable agricultural products include:

[0033] Obtain the failure rate of identification perception of all agricultural products when passing through the current area;

[0034] Arrange the failure rates of identification perception of all agricultural products when passing through the current area in descending order to obtain a descending order sequence of the failure rates of identification perception of agricultural products when passing through the current area. Calculate the difference between adjacent two sequence values in the descending order sequence of the failure rates of identification perception. Among the differences between the adjacent two sequence values, denote the one with the smaller serial number in the two sequence values corresponding to the maximum difference as the separation serial number. Denote the agricultural products corresponding to all sequence values from the first serial number to the separation serial number and including the first serial number and the separation serial number in the descending order sequence of the failure rates of identification perception as the perishable agricultural products in the current area.

[0035] Preferably, the specific steps for obtaining the signal coverage completeness include:

[0036] When agricultural products of the same batch pass through the current area, calculate the average value of the signal intensities of the identification data of all agricultural products at the same sampling moment, and denote it as the average signal intensity of agricultural products of the same batch at each sampling moment;

[0037] Map the average signal intensities of agricultural products of the same batch at all sampling moments into a two-dimensional space, where the horizontal axis is the sampling moment and the vertical axis is the average signal intensity, to obtain the signal intensity fluctuation curve of agricultural products of the same batch passing through the current area;

[0038] Denote the ratio of the minimum value of the average signal intensity to the average value of the average signal intensities in all sampling moments of the signal intensity fluctuation curve of agricultural products of the same batch passing through the current area as the dip amplitude of the signal intensity fluctuation curve of agricultural products of the same batch passing through the current area;

[0039] Integrate the dip amplitude of the signal intensity fluctuation curve of agricultural products of the same batch passing through the current area and the average value of the amplitudes of all negative increments to obtain the signal coverage completeness of the current area, where the dip amplitude is in direct proportion to the signal coverage completeness, and the average value of the amplitudes of the negative increments is in inverse proportion to the signal coverage completeness.

[0040] Preferably, the specific steps for obtaining the positioning coverage attenuation degree include:

[0041] Construct the identification loss frequency sequence and the signal coverage completeness sequence of this batch of agricultural products according to the identification loss frequency and the signal coverage completeness of agricultural products of the same batch passing through each area;

[0042] Obtain the mean square error of the identification loss frequency sequence and the signal coverage completeness sequence of agricultural products of the same batch, and denote it as the subjective deviation degree of the labels of agricultural products of the same batch;

[0043] Denote a batch of agricultural products that most recently passed through the current area as the current batch of agricultural products;

[0044] Integrate the identification loss frequency and the signal coverage completeness of the current area, and combine with the subjective deviation degree of the labels of the current batch of agricultural products to obtain the positioning coverage attenuation degree of the current area; the identification loss frequency and the subjective deviation degree of the labels are in direct proportion to the positioning coverage attenuation degree, and the signal coverage completeness is in inverse proportion to the positioning coverage attenuation degree.

[0045] In a second aspect, the present invention further provides a positioning data perception device based on identification resolution, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The memory is used to store the computer program, and the processor runs the computer program to implement the steps of the foregoing positioning data perception method based on identification resolution.

[0046] In a third aspect, the present invention further provides a storage medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the steps of the foregoing method are implemented.

[0047] The beneficial effects of the technical solution of the present invention are as follows: obtaining the identification data and signal strength at different sampling times when each agricultural product of the same batch passes through each region, as well as the humidity at each sampling time in each region; when the agricultural products of the same batch pass through the current region, obtaining the environmental constraint coefficient of the current region; counting the number of missing identification data of all agricultural products at the same sampling time, and combining the environmental constraint coefficient to obtain the environmental positioning error of the current region; quantifying the influence of humidity on signal strength, and combining the missing situation of agricultural product identification data to quantify the error caused by environmental interference in the current region; counting the missing situation of identification data of all agricultural products at different sampling times to obtain the frequentness of identification loss in the current region; quantifying the loss situation of agricultural products of the same batch when passing through the current region to obtain the frequentness of loss of agricultural products in the current region; obtaining the signal coverage completeness of the current region according to the signal strength of agricultural products of the same batch at different sampling times, and using the signal strength of different identification data to reflect the signal coverage situation of the current region; obtaining the frequentness of identification loss and signal coverage completeness of each region; obtaining the positioning coverage attenuation degree of the current region according to the consistency of the covariation relationship of the frequentness of identification loss and signal coverage completeness between the current region and the regions before passing through the current region; by analyzing the covariation relationship of the frequentness of identification loss and signal coverage completeness when the current batch of agricultural products passes through all regions, reflecting the relationship between the missing of the current batch of agricultural products and the signal strength, and further using it as the weight for obtaining compensation parameters to obtain the positioning coverage attenuation degree of the current region; generating compensation parameters for the current region by using the positioning coverage attenuation degree and the environmental positioning error; using the compensation parameters to sample and compensate the identification data of the next batch of agricultural products passing through the current region; by analyzing the relationship between the signal strength and the missing of identification data when a batch of agricultural products passes through all regions, as well as the interference of the environment on the identification data, the present invention obtains compensation parameters for sampling and compensating the identification data of the next batch of agricultural products passing through the current region, improving the accuracy and precision of sampling. Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the steps of a method for perceiving positioning data based on identification resolution according to the present invention. Specific embodiments

[0050] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features and effects of a method for perceiving positioning data based on identification resolution proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0052] The following specifically describes the specific solutions of a method, device and medium for perceiving positioning data based on identification resolution provided by the present invention in conjunction with the accompanying drawings.

[0053] In a first aspect, please refer to Figure 1 , which shows a flowchart of the steps of a method for perceiving positioning data based on identification resolution provided by an embodiment of the present invention. The method includes the following steps:

[0054] Step S001: Collect the identifiers of all agricultural products in the same batch, and obtain the identifier data, signal strength at different sampling times when each agricultural product passes through each region, and the humidity at each sampling time in each region.

[0055] There are strict requirements for environmental factors such as temperature, humidity, and obstacle occlusion during the logistics transportation of agricultural products. When the requirements are not met, it may lead to the deterioration of agricultural products, the decline in quality, etc., affecting the efficiency and economic benefits of the circulation link of agricultural products. Therefore, it is necessary to grasp the location information and environmental data of agricultural products in real time; during the logistics transportation of agricultural products, a unique identifier is assigned to each batch of agricultural products. When reaching each region, the identifier data of the agricultural product status is transmitted to the logistics transportation system through the identifier. However, during the transmission, due to the need to keep the agricultural products in a humid environment and the stacking of agricultural products, etc., the transmission of the identifier data is interfered, resulting in signal attenuation or even loss. Therefore, in this embodiment, by analyzing the identifier data of agricultural products passing through each region, the signal is compensated and then collected when the subsequent agricultural products pass through this region, thereby improving the effectiveness of the identifier data.

[0056] First of all, when transporting agricultural products, usually multiple types of agricultural products are transported in one batch. It is necessary to collect the identifier data, humidity, and signal strength at different sampling times when the agricultural products of the same batch pass through each region; the identifier data contains information about this type of agricultural product and serves as data support for links such as agricultural product traceability, identification, and resource tracking; the signal strength is the wireless signal strength received by the receiving and sensing device of the logistics transmission system when collecting this identifier data.

[0057] Specifically, a unique label is assigned to all types of agricultural products in the same batch to carry the identifier data of each agricultural product. In this embodiment, the collection of the identifier data of agricultural products is realized through radio frequency identification (RFID) technology. The label is installed on the packaging of different types of agricultural products by pasting or other means. The logistics transportation systems in each region transmit radio frequency to the labels of all agricultural products through the radio frequency devices installed in the field, and then read the identifier information in the label. After that, the receiving device receives the identifier data, achieving the purpose of the logistics transportation system in each region to identify the agricultural product target and data exchange; the receiving device is described by taking the receiver as an example.

[0058] It should be noted that the region described in this embodiment is the same environment through which the agricultural products pass. As an example, the starting station through which the agricultural products pass is the first region. After the agricultural products are sent out from the starting station, the journey to the first transfer station is the second region, and the arrival at the first transfer station is the third region.

[0059] As a preferred example, the specific steps for obtaining the identifier data, humidity, and signal strength of each agricultural product at different sampling times when passing through each region include:

[0060] Install RF devices and receiving readers in each region, set a fixed sampling interval, and take each sampling interval as a sampling moment. The RF devices continuously transmit radio frequency signals to the region. After the tags of the agricultural products in the same batch enter the range of the radio frequency signals emitted by the RF devices, the tags send out the product information stored in the tags by virtue of the energy obtained from the induced current, which is received by the receiving readers of the logistics transportation system, obtaining the identification data of each agricultural product in the same batch at different sampling moments when passing through each region, and recording the signal strength of the identification data;

[0061] While collecting the identification data and signal strength, the logistics transportation system installs humidity sensors in each region, and obtains the humidity at each sampling moment through the humidity sensors, which is recorded as the humidity of each region at each sampling moment.

[0062] It should be noted that the identification data described in this embodiment includes the basic information of agricultural products, specifically such as product identification number, product name, production batch number, specification, storage temperature, shelf life, raw material batch number, place of origin, foreign object detection result, etc. The content of the basic information is not limited in this embodiment, and other embodiments can modify the content of the basic information;

[0063] At the same time, during the sampling process of the identification data, there are situations where the signal is interfered or weakened, resulting in the inability to recognize or sample the acquisition result. At this time, missing marks are made for the identification data of the agricultural products that cannot be recognized or sampled, and the identification data is filled with null values; it should be especially noted that as long as the tags of agricultural products receive the radio frequency signals emitted by the receiving readers, they can passively send the product information stored in the tags, and the product information is recognized and read after being received by the receiving readers. However, the signals marked as missing cannot be completely recognized and read, but the receiving readers can still receive the signal strength. Therefore, the signal strength of the identification data marked as missing is not all zero.

[0064] It should be noted that the agricultural products analyzed in this embodiment are agricultural products of the same batch. In this embodiment, the division of different types of agricultural products within the same batch is based on different identification data. Agricultural products with the same identification data are regarded as the same type of agricultural product, and agricultural products with different identification data are regarded as different types of agricultural products. The types of agricultural products described in this embodiment are not the same as the agricultural species.

[0065] Among them, radio frequency identification (RFID) technology and its specific emission and reception of radio frequency are well-known prior arts, and will not be elaborated in this embodiment.

[0066] Step S002: When the agricultural products of the same batch pass through the current region, obtain the environmental constraint coefficient of the current region, count the number of missing identification data of all agricultural products at the same sampling moment, and obtain the environmental positioning error of the current region in combination with the environmental constraint coefficient.

[0067] Moisture in the air can interfere with the transmission of wireless signals. Fluctuating humidity has a greater impact on the transmission of a constant wireless radio frequency signal compared to stable humidity. An increase in humidity can cause scattering of wireless signals, and the absorption effect of high humidity on wireless signals increases, resulting in a significant weakening of the signal intensity. Therefore, the higher the humidity and the greater the fluctuations, the greater the demand for signal compensation. In this embodiment, the environmental constraint coefficient of the current area is obtained based on the humidity fluctuations.

[0068] The last area passed by the same batch of agricultural products in chronological order is recorded as the current area. In the current area, there are identification data, humidity, and signal intensity at several sampling times.

[0069] Preferably, the specific steps for obtaining the environmental constraint coefficient of the current area include:

[0070] Construct a humidity curve of the current area based on the humidity at each sampling time in the current area.

[0071] After obtaining all the positive increments in the humidity curve of the current area, calculate the mean value of the positive increments.

[0072] Analyze the fluctuation of the humidity curve of the current area and combine it with the mean value of the positive increments to obtain the environmental constraint coefficient of the current area.

[0073] Specifically, construct a humidity curve of the current area based on the humidity at each sampling time in the current area, where the horizontal axis of the humidity curve is the sampling time and the vertical axis is the humidity value.

[0074] Furthermore, obtain all the maximum and minimum values in the humidity curve of the current area, and the maximum and minimum values appear alternately. Denote the range between each maximum value and the previous minimum value as the positive increment of the maximum value. Calculate the mean value of all the positive increments in the humidity curve of the current area.

[0075] Furthermore, the method for obtaining the environmental constraint coefficient of the current area is: Denote the product of the mean value of all the positive increments in the humidity curve of the current area and the variance of all the humidities in the humidity curve of the current area as the environmental constraint coefficient of the current area. Among them, the variance of all the humidities in the humidity curve of the current area is used to represent the fluctuation of the humidity curve of the current area.

[0076] It should be noted that after obtaining the interference of humidity on the propagation of wireless signals, the same batch of agricultural products need to be transported and moved in the same area. Therefore, the positions of the agricultural products at different sampling times are different, and factors such as the occlusion of obstacles in the area and the distance between the label and the receiving reader result in different numbers of agricultural product identification data that can be collected at different sampling times. Therefore, in this embodiment, the environmental positioning error is obtained by counting the number of identification data of all agricultural products at the same sampling time and combining it with the environmental constraint coefficient.

[0077] Preferably, the specific steps for obtaining the environmental positioning error of the current region by counting the number of missing identification data of all agricultural products at the same sampling time and combining the environmental constraint coefficient are as follows:

[0078] The number of identification data marked as missing among all agricultural products at the same sampling time in the current region is recorded as the amount of missing identification data at this sampling time in the current region;

[0079] The ratio of the mean value of the amount of missing identification data at all sampling times in the current region to the number of agricultural products is recorded as the missing rate of the current region; the product of the missing rate and the environmental constraint coefficient of the current region is recorded as the environmental positioning error of the current region.

[0080] Step S003: When the agricultural products of the same batch pass through the current region, count the missing conditions of the identification data of all agricultural products at different sampling times to obtain the frequency of label loss in the current region; obtain the signal coverage completeness of the current region according to the signal strength of the agricultural products of the same batch at different sampling times.

[0081] For the same agricultural product of the same batch when passing through the current region, when the label of the agricultural product fails, is blocked by other agricultural products and is in a signal blind area, or the amount of information contained in the label of the agricultural product exceeds the processing bottleneck of the logistics transportation system, it will cause a large amount of the identification data collected when the agricultural product passes through the current region to be marked as missing, and there is a continuous long-term missing situation. At this time, it indicates that the identification data of this agricultural product is more likely to be missing. Therefore, in this embodiment, the frequency of label loss in the current region is obtained by counting the missing conditions of the identification data of all agricultural products at different sampling times.

[0082] Preferably, the specific steps for obtaining the frequency of label loss in the current region by counting the missing conditions of the identification data of all agricultural products at different sampling times include:

[0083] When the agricultural products of the same batch pass through the current region, mark each piece of identification data of each agricultural product as missing at all sampling times as a missing sampling point of the agricultural product;

[0084] According to the continuous missing duration of all missing sampling points of each agricultural product, obtain the identification perception failure rate of each agricultural product in the current region;

[0085] Divide all agricultural products in the current region according to the identification perception failure rate to obtain the agricultural products that are prone to missing in the current region;

[0086] Obtain the frequency of label loss in the current region according to the proportion of the agricultural products that are prone to missing in the current region and their identification perception failure rate.

[0087] Specifically, according to the continuous duration of all missing sampling points of each agricultural product, the specific method for obtaining the identification perception failure rate of each agricultural product in the current area is as follows:

[0088] For the i-th agricultural product in the current area, merge the consecutive missing sampling points of the i-th agricultural product to obtain several continuous missing segments of the i-th agricultural product;

[0089] It should be noted that the above-mentioned continuity means that the interval between the sampling times corresponding to two adjacent missing sampling points in time series is equal to one sampling interval. A missing sampling point segment composed of several consecutive missing sampling points with only one sampling interval between them is recorded as a continuous missing segment; when the missing sampling point is separated from other adjacent missing sampling points by more than one sampling interval, a single missing sampling point also constitutes a continuous missing segment.

[0090] It should be especially noted that in this embodiment, the continuity is described by taking the interval equal to one sampling interval as an example. Other embodiments can use other sampling interval lengths as the condition for judging continuity, and this embodiment does not specifically limit the condition for continuity.

[0091] Furthermore, when the i-th agricultural product passes through the current area, the identification perception failure rate Y of the i-th agricultural product i is calculated as follows:

[0092]

[0093] where G i is the total number of identification data when the i-th agricultural product passes through the current area, and g i is the total number of missing sampling points when the i-th agricultural product passes through the current area;

[0094] m i is the total number of continuous missing segments of the i-th agricultural product; Δt is the time length of the preset sampling interval, and Δt k is the time length of the k-th continuous missing segment of the i-th agricultural product;

[0095] ω is the prior fault tolerance multiple, and the fault tolerance multiple is greater than or equal to 1, which is used to avoid misjudgment as continuous missing when the time length of the continuous missing segment is equal to the time length of the sampling interval. In this embodiment, ω = 1.5 is taken as an example for description, and the fault tolerance multiple is not specifically limited.

[0096] where represents the proportion of the missing sampling points of the i-th agricultural product in all identification data. The larger the value, the more likely the identification data of the i-th agricultural product is missing in the current area, that is, the label of the i-th agricultural product may be in a blind area or blocked by other agricultural products, resulting in signal obstacles; at the same time, The larger the value of , the more continuous the missing sampling points of the i-th agricultural product are, rather than sporadic signal loss.

[0097] Furthermore, obtain the identification perception failure rate when all agricultural products pass through the current area.

[0098] Specifically, as an example, the specific method for dividing all agricultural products in the current area according to the identification perception failure rate to obtain the agricultural products prone to missing in the current area is as follows:

[0099] Sort the identification perception failure rates of all agricultural products passing through the current area in descending order to obtain the descending order sequence of the identification perception failure rates of agricultural products passing through the current area. Calculate the difference between two adjacent sequence values in the descending order sequence of the identification perception failure rates. Among the differences between the two adjacent sequence values, record the one with the smaller serial number among the two sequence values corresponding to the largest difference as the separation serial number. Record the agricultural products corresponding to all sequence values from the first serial number to the separation serial number and including the first serial number and the separation serial number in the descending order sequence of the identification perception failure rates as the agricultural products prone to missing in the current area.

[0100] It should be noted that the above division method is based on a large difference, so that the identification perception failure rate of the agricultural products prone to missing is much greater than that of other agricultural products, and the agricultural products prone to missing are used to represent the agricultural products in the current area where signal identification loss is likely to occur.

[0101] As another example, the specific method for dividing all agricultural products in the current area according to the identification perception failure rate to obtain the agricultural products prone to missing in the current area is as follows:

[0102] Sort the identification perception failure rates of all agricultural products passing through the current area in descending order to obtain the descending order sequence of the identification perception failure rates of agricultural products passing through the current area. After bisecting the descending order sequence of the identification perception failure rates, record the agricultural products corresponding to the serial number value with the larger identification perception failure rate as the agricultural products prone to missing.

[0103] Specifically, the specific method for obtaining the frequency of identification loss in the current area according to the proportion of the agricultural products prone to missing in the current area and their identification perception failure rates is as follows:

[0104] When agricultural products of the same batch pass through the current area, record the product of the ratio of the number of agricultural products prone to missing in the same batch to the total number of agricultural products in the same batch and the identification perception failure rates of all agricultural products prone to missing in the same batch as the frequency of identification loss in the current area.

[0105] Among them, the larger the ratio of the number of agricultural products prone to missing in the same batch to the total number of agricultural products in the same batch, the more likely it is that identification data loss occurs due to reasons such as labels or perception devices when the agricultural products of this batch pass through the current area.

[0106] It should be noted that while obtaining the frequency of label loss in the current area, to prove that the frequency of label loss is caused by reasons such as the label being blocked by other agricultural products or the signal strength being greatly weakened due to the receiving end in this area being far from the label, it is necessary to compensate the signal strength received by the receiving end so that the radio frequency signal received by the receiving end after the passive reflection of the label has sufficient signal strength to be recognized. Therefore, in this embodiment, the signal coverage completeness of the current area is obtained according to the signal strengths of the same batch of agricultural products at different sampling times.

[0107] Preferably, the specific steps for obtaining the signal coverage completeness of the current area according to the signal strengths of the same batch of agricultural products at different sampling times are as follows:

[0108] When the same batch of agricultural products passes through the current area, construct a signal strength fluctuation curve of the same batch of agricultural products passing through the current area according to the signal strengths of all agricultural products at each moment;

[0109] Quantify the downward dip amplitude of the signal strength fluctuation curve and the amplitudes of all negative increments to obtain the signal coverage completeness of the current area.

[0110] Specifically, the specific method for constructing the signal strength fluctuation curve of the same batch of agricultural products passing through the current area is as follows:

[0111] When the same batch of agricultural products passes through the current area, calculate the mean value of the signal strengths of the identification data of all agricultural products at the same sampling moment, which is denoted as the mean signal strength of the same batch of agricultural products at each sampling moment;

[0112] Map the mean signal strengths of the same batch of agricultural products at all sampling moments into a two-dimensional space, where the horizontal axis is the sampling moment and the vertical axis is the mean signal strength, to obtain the signal strength fluctuation curve of the same batch of agricultural products passing through the current area.

[0113] Furthermore, quantifying the downward dip amplitude of the signal strength fluctuation curve and the amplitudes of all negative increments to obtain the signal coverage completeness of the current area specifically includes:

[0114] Among all sampling moments of the signal strength fluctuation curve of the same batch of agricultural products passing through the current area, record the ratio of the minimum value of the mean signal strength to the mean value of the mean signal strengths as the downward dip amplitude of the signal strength fluctuation curve of the same batch of agricultural products passing through the current area;

[0115] By synthesizing the downward amplitude of the signal intensity fluctuation curve when the agricultural products of the same batch pass through the current area and the average value of the amplitudes of all negative increments, the signal coverage completeness of the current area is obtained, where the downward amplitude is in a direct proportional relationship with the signal coverage completeness, and the average value of the amplitudes of the negative increments is in an inverse proportional relationship with the signal coverage completeness;

[0116] As an example, the calculation method of the signal coverage completeness D of the current area is:

[0117]

[0118] where B is the downward amplitude of the signal intensity fluctuation curve when the agricultural products of the same batch pass through the current area, and K is the average value of the amplitudes of all negative increments in the signal intensity fluctuation curve when the agricultural products of the same batch pass through the current area.

[0119] It should be noted that in the signal intensity fluctuation curve when the agricultural products of the same batch pass through the current area, the difference between the signal mean intensity at the later sampling moment and the previous moment is used as the increment. When the increment is less than 0, it is recorded as the negative increment of the signal intensity fluctuation curve, which is used to represent the decrease in the signal intensity between adjacent sampling moments. The greater the decrease, the more likely it is that the label is blocked by the environment, and the less perfect the signal coverage completeness is;

[0120] At the same time, the downward amplitude represents the degree of decrease of the minimum signal intensity in the current area compared with other sampling moments. The larger the value, the more sampling nodes with incomplete signal coverage exist in the current area, that is, insufficient coverage will cause the agricultural products to be unable to be identified and sensed when transported to some areas.

[0121] Step S004, obtain the label loss frequency and signal coverage completeness of each area; according to the consistency of the covariation relationship between the label loss frequency and signal coverage completeness of the current area and the areas before passing through the current area, obtain the positioning coverage attenuation degree of the current area.

[0122] The above has obtained the signal coverage completeness of the current area and the label loss frequency of the current area when the agricultural products of the same batch pass through the current area. Similarly, when the agricultural products of this batch pass through each area, the label loss frequency and signal coverage completeness of each area will be obtained;

[0123] It should be noted that the above-mentioned passing through each area refers to the areas passed by the current area, excluding the areas after the current area.

[0124] It should be noted that the frequency of identification loss indicates the loss situation of agricultural products when passing through this area, while the signal coverage rate indicates the signal coverage situation in this area when collecting the identification of agricultural products; when the signal loss is caused by the stacking of agricultural products during transportation, etc., resulting in the identification being blocked, or the number of agricultural products in the same batch increases, resulting in the number of identifications exceeding the perception load of the perception system, there is a correlation between the signal coverage rate and the frequency of identification loss of the same batch of agricultural products in different regions. Therefore, in this embodiment, by analyzing the covariation relationship between the signal coverage rate and the frequency of identification loss of the same batch of agricultural products in all regions passed through, the covariation relationship is used as the weight to obtain the positioning coverage attenuation degree of the current region.

[0125] Preferably, the specific steps for obtaining the positioning coverage attenuation degree of the current region according to the consistency of the covariation relationship between the frequency of identification loss and the signal coverage completeness of the current region and the regions passed through before the current region include:

[0126] Construct a sequence of the frequency of identification loss and a sequence of signal coverage completeness of this batch of agricultural products according to the frequency of identification loss and the signal coverage completeness of the same batch of agricultural products passing through each region, where the serial number value represents the order of the regions passed through;

[0127] Obtain the mean square error of the sequence of the frequency of identification loss and the sequence of signal coverage completeness of the same batch of agricultural products, denoted as the subjective deviation degree of the labels of the same batch of agricultural products;

[0128] The subjective deviation degree of the label is used to reflect the constant performance of the label of this batch of agricultural products in regions with different perception accuracies. The larger the value, the more the missing of the identification data of this batch of agricultural products is affected by the regional perception accuracy; when the label of agricultural products is not affected by the perception accuracy, there is no longer a relationship between the frequency of identification loss and the signal coverage completeness, that is, the stronger the compensation effect for a batch of agricultural products with more obvious perception accuracy. Therefore, the subjective deviation degree of the label of this batch of agricultural products can be used as the compensation weight for signal compensation.

[0129] Further, a batch of agricultural products that recently passed through the current region is denoted as the current batch of agricultural products;

[0130] Based on the frequency of identification loss and the signal coverage completeness of the current region, combined with the subjective deviation degree of the label of the current batch of agricultural products, obtain the positioning coverage attenuation degree of the current region; the frequency of identification loss and the subjective deviation degree of the label are in a direct proportional relationship with the positioning coverage attenuation degree, and the signal coverage completeness is in an inverse proportional relationship with the positioning coverage attenuation degree.

[0131] As an example, the way to obtain the positioning coverage attenuation degree of the current region is:

[0132] Denote the frequency of identification loss of the current region as R;

[0133] Record the subjective deviation degree of the labels of the current batch of agricultural products as Z;

[0134] The calculation method of the positioning coverage attenuation degree S of the current area is as follows:

[0135]

[0136] Among them, D is the signal coverage completeness of the current area.

[0137] Step S005: Generate the compensation parameter of the current area by using the positioning coverage attenuation degree and the environmental positioning error; sample and compensate the identification data of the next batch of agricultural products passing through the current area by using the compensation parameter.

[0138] It should be noted that the purpose of this embodiment is to analyze the acquisition result of the identification data of the current batch of agricultural products based on the current batch of agricultural products passing through the current area, so as to compensate the signal strength of the label sensing device in the current area and improve the identification accuracy of the label sensing device for the next batch of agricultural products when the next batch of agricultural products enter the current area.

[0139] Furthermore, it should be noted that the above method obtains the positioning coverage attenuation degree and the environmental positioning error of the current area. The larger the environmental positioning error, the greater the influence of environmental interference in the current area, and the more signal strength needs to be compensated to reduce the number of identification data marked as missing; at the same time, the larger the value of the positioning coverage attenuation degree of the current area, the greater the influence of other factors in the current area, such as the influence of factors such as the greater distance between agricultural products and the identification sensing device on the accuracy of identification data acquisition; therefore, this embodiment generates the compensation parameter of the current area by using the positioning coverage attenuation degree and the environmental positioning error; samples and compensates the identification data of the next batch of agricultural products passing through the current area by using the compensation parameter.

[0140] Preferably, the specific steps of generating the compensation parameter of the current area by using the positioning coverage attenuation degree and the environmental positioning error of the current area are as follows:

[0141] As an example, the calculation method of the compensation parameter of the current area is as follows:

[0142] Record the environmental positioning error of the current area as H;

[0143]

[0144] Among them, B is the compensation parameter of the current area, S is the positioning coverage attenuation degree of the current area; sigmoid{} is the sigmoid function, which is used to normalize the positioning coverage attenuation degree and the environmental positioning error to [0,1] respectively to eliminate the influence of different magnitudes.

[0145] As another example, the compensation parameter of the current area is calculated as follows:

[0146] Denote the environmental positioning error of the current area as H;

[0147]

[0148] Among them, B is the compensation parameter of the current area, and S is the positioning coverage attenuation degree of the current area.

[0149] Furthermore, the sampling compensation for the identification data of the next batch of agricultural products passing through the current area by using the compensation parameter specifically includes:

[0150] As an example, this embodiment uses pre-emphasis technology for signal compensation. Among them, the tag sensing device takes the receiving reader as an example, and the sensing technology for tags takes radio frequency identification technology as an example;

[0151] When the next batch of agricultural products passes through the current area, a radio frequency signal is transmitted to the current area through a radio frequency transmitting device. The tag on the next batch of agricultural products sends out the product information stored in the tag by virtue of the energy obtained from the induced current, and the received signal is received by the receiving reader of the logistics transportation system to obtain the identification signal to be processed. The identification signal to be processed is an analog signal. The identification signal to be processed is compensated by using the compensation parameter of the current area in combination with the pre-emphasis technology to obtain the processed identification signal. The result of the compensation is that the high-frequency signal of the processed identification signal is 1 plus the compensation parameter multiple of the high-frequency signal of the identification signal to be processed; the processed identification signal is subjected to digital-to-analog conversion to obtain the identification data of each agricultural product in the next batch at different sampling times when passing through each area.

[0152] It should be noted that the above sampling compensation for the identification data of the next batch of agricultural products passing through the current area is an example compensation method proposed in this embodiment. The pre-emphasis technology used therein is a well-known existing technology. Other embodiments can adopt other compensation methods for sampling compensation, and this embodiment does not specifically limit the method of sampling compensation.

[0153] In a second aspect, another embodiment of the present invention further provides a positioning data sensing device based on identification parsing, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The memory is used to store the computer program, and the processor runs the computer program to implement the steps of the foregoing positioning data sensing method based on identification parsing.

[0154] In a third aspect, another embodiment of the present invention further provides a storage medium, on which a computer instruction is stored. When the computer instruction is executed by a processor, the following operations are performed:

[0155] Collect the identifiers of all agricultural products in the same batch, and obtain the identifier data, signal strength at different sampling times when each agricultural product passes through each region, and the humidity at each sampling time in each region.

[0156] When the agricultural products in the same batch pass through the current region, obtain the environmental constraint coefficient of the current region, where the environmental constraint coefficient is positively correlated with the fluctuation of the humidity of the agricultural products in the current region; count the number of missing identifier data of all agricultural products at the same sampling time, and obtain the environmental positioning error of the current region in combination with the environmental constraint coefficient; count the missing situation of the identifier data of all agricultural products at different sampling times, and obtain the frequent loss of identifiers in the current region; obtain the complete signal coverage of the current region according to the signal strength of the agricultural products in the same batch at different sampling times.

[0157] Obtain the frequent loss of identifiers and the complete signal coverage of each region; obtain the positioning coverage attenuation degree of the current region according to the consistency of the covariation relationship of the frequent loss of identifiers and the complete signal coverage between the current region and the region before passing through the current region.

[0158] Generate a compensation parameter for the current region by using the positioning coverage attenuation degree and the environmental positioning error; use the compensation parameter to perform sampling compensation on the identifier data of the next batch of agricultural products passing through the current region.

[0159] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A positioning data perception method based on identity resolution, characterized in that: The method comprises the following steps: Collect the identification of all agricultural products in the same batch, obtain the identification data and signal strength of each agricultural product at different sampling times when it passes through each region, and the humidity at each sampling time in each region; When the same batch of agricultural products passes through the current area, the environmental constraint coefficient of the current area is obtained, and the environmental constraint coefficient is positively correlated with the fluctuation of the humidity of the agricultural products in the current area; the number of missing identification data of all agricultural products at the same sampling time is counted, and the environmental positioning error of the current area is obtained by combining the environmental constraint coefficient; the missing identification data of all agricultural products at different sampling times are counted to obtain the frequency of identification loss in the current area; according to the signal strength of the same batch of agricultural products at different sampling times, the signal coverage completeness of the current area is obtained; Obtain the identification loss frequency and signal coverage completeness of each region; obtain the positioning coverage attenuation of the current region based on the consistency of the covariant relationship between the identification loss frequency and signal coverage completeness of the current region and the regions before the current region; The compensation parameters of the current area are generated by using the positioning coverage attenuation and the environmental positioning error; the identification data of the next batch of agricultural products passing through the current area are sampled and compensated by using the compensation parameters; wherein the identification signal to be processed is compensated by combining the compensation parameters of the current area with the pre-emphasis technology to obtain a processed identification signal, and the compensation result is that the high-frequency signal of the processed identification signal is 1 of the high-frequency signal of the identification signal to be processed plus a multiple of the compensation parameter; The specific steps of obtaining the environmental constraint coefficient include: Construct a humidity curve for the current region based on the humidity at each sampling time in the current region, where the horizontal axis of the humidity curve is the sampling time and the vertical axis is the humidity value; Take all the maximum and minimum values ​​in the humidity curve of the current area; record the range between each maximum value and the previous minimum value as the positive increment of the maximum value; calculate the mean of all positive increments in the humidity curve of the current area; The product of the mean of all positive increments in the humidity curve of the current region and the variance of all humidity in the humidity curve of the current region is recorded as the environmental constraint coefficient of the current region; The specific steps of obtaining the environmental positioning error include: The number of identification data marked as missing in all agricultural products at the same sampling time in the current region is recorded as the number of missing identification data at the sampling time in the current region; The ratio of the mean of the missing amount of identification data at all sampling moments in the current region to the amount of agricultural products is recorded as the missing rate of the current region; The product of the missing rate and the environmental constraint coefficient of the current area is recorded as the environmental positioning error of the current area; The specific steps of obtaining the positioning coverage attenuation include: According to the frequency of label loss and signal coverage completeness of the same batch of agricultural products passing through various regions, the label loss frequency sequence and signal coverage completeness sequence of the batch of agricultural products are constructed; The mean square error of the label loss frequency sequence and signal coverage completeness sequence of the same batch of agricultural products is obtained, which is recorded as the subjective deviation degree of the label of the same batch of agricultural products; Record the batch of agricultural products that has passed through the current area most recently as the current batch of agricultural products; The positioning coverage attenuation of the current area is obtained by comprehensively considering the frequency of identification loss and the completeness of signal coverage in the current area and combining the degree of subjective deviation of the labels of the current batch of agricultural products. The frequency of identification loss and the degree of subjective deviation of the labels are directly proportional to the positioning coverage attenuation, and the signal coverage completeness is inversely proportional to the positioning coverage attenuation.

2. According to claim 1, a positioning data perception method based on identity resolution is characterized in that: The specific steps of obtaining the frequency of identification loss include: When the same batch of agricultural products passes through the current area, the identification data of each agricultural product marked as missing is recorded as a missing sampling point of the agricultural product at all sampling moments; According to the continuous missing situation of all missing sampling points of each agricultural product, the identification perception failure rate of each agricultural product in the current area is obtained; Classifying all agricultural products in the current region according to the identification perception failure rate to obtain agricultural products that are prone to missing in the current region; The frequency of label loss in the current region is obtained based on the proportion of easily missing agricultural products in the current region and their label perception failure rate; The product of the ratio of the number of easily missing agricultural products in the same batch to the total number of agricultural products in the same batch and the identification perception failure rate of all easily missing agricultural products in the same batch is recorded as the identification loss frequency in the current area.

3. According to claim 2, a positioning data perception method based on identity resolution is characterized in that: The specific steps of obtaining the identification perception failure rate include: For the i-th agricultural product in the current region, the continuous missing sampling points of the i-th agricultural product are merged to obtain several continuous missing segments of the i-th agricultural product; When the i-th agricultural product passes through the current area, the identification perception failure rate Y of the i-th agricultural product i The calculation method is: Among them, G i is the total number of identification data of the i-th agricultural product passing through the current region, g i is the total number of missing sampling points when the i-th agricultural product passes through the current area; m i is the total number of continuously missing segments of the i-th agricultural product; Δt is the length of the preset sampling interval, Δt k The length of time of the kth continuous missing segment of the ith agricultural product; ω is the prior fault tolerance multiple.

4. According to claim 2, a positioning data perception method based on identity resolution is characterized in that: The specific steps for obtaining the easily missing agricultural products include: Get the identification perception failure rate of all agricultural products passing through the current area; Arrange the identification perception failure rates of all agricultural products when passing through the current area in descending order to obtain a descending sequence of identification perception failure rates of agricultural products when passing through the current area, calculate the difference between two adjacent sequence values ​​in the descending sequence of identification perception failure rates, record the smaller sequence number of the two sequence values ​​corresponding to the maximum difference in the difference between the two adjacent sequence values ​​as the separation sequence number, and record the agricultural products corresponding to all sequence values ​​from the first sequence number to the separation sequence number and including the first sequence number in the descending sequence of identification perception failure rates as easily missing agricultural products in the current area.

5. The positioning data perception method based on identity resolution according to claim 1 is characterized in that: The specific steps of obtaining the signal coverage completeness include: When the same batch of agricultural products passes through the current area, the mean signal strength of the identification data of all agricultural products at the same sampling time is calculated, and recorded as the mean signal strength of the same batch of agricultural products at each sampling time; The signal mean strength of the same batch of agricultural products at all sampling times is mapped into a two-dimensional space, where the horizontal axis is the sampling time and the vertical axis is the signal mean strength, and the signal strength fluctuation curve of the same batch of agricultural products when passing through the current area is obtained; The ratio of the minimum value of the signal mean strength to the average value of the signal mean strength at all sampling moments of the signal strength fluctuation curve when the same batch of agricultural products passes through the current region is recorded as the downward amplitude of the signal strength fluctuation curve when the same batch of agricultural products passes through the current region, wherein the downward amplitude represents the degree of decrease of the minimum value of the signal strength in the current region compared with other sampling moments; The signal coverage completeness of the current area is obtained by combining the downward amplitude of the signal strength fluctuation curve when the same batch of agricultural products passes through the current area and the average of the amplitudes of all negative increments, wherein the downward amplitude is directly proportional to the signal coverage completeness, and the average of the amplitudes of the negative increments is inversely proportional to the signal coverage completeness, wherein when the increment is less than 0, it is recorded as a negative increment of the signal strength fluctuation curve.

6. A positioning data perception device based on identity resolution, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a positioning data perception method based on identity resolution as described in any one of claims 1 to 5 are implemented.

7. A storage medium having computer instructions stored thereon, characterized in that: When the computer instruction is executed by a processor, the steps of a positioning data perception method based on identity resolution as described in any one of claims 1 to 5 are implemented.

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