CN112106736A - Insect breeding system based on surplus rubbish of meal is retrieved and is recycled - Google Patents
Insect breeding system based on surplus rubbish of meal is retrieved and is recycled Download PDFInfo
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- 238000009395 breeding Methods 0.000 title claims abstract description 113
- 230000001488 breeding effect Effects 0.000 title claims abstract description 113
- 241000238631 Hexapoda Species 0.000 title claims abstract description 76
- 235000012054 meals Nutrition 0.000 title claims description 32
- 239000010813 municipal solid waste Substances 0.000 title description 2
- 238000012544 monitoring process Methods 0.000 claims abstract description 91
- 239000002689 soil Substances 0.000 claims abstract description 51
- 238000001514 detection method Methods 0.000 claims abstract description 42
- 230000007613 environmental effect Effects 0.000 claims abstract description 29
- 239000010794 food waste Substances 0.000 claims abstract description 29
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 12
- 238000004064 recycling Methods 0.000 claims abstract description 10
- 238000012163 sequencing technique Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims abstract description 7
- 230000004083 survival effect Effects 0.000 claims description 61
- 238000004458 analytical method Methods 0.000 claims description 27
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- 238000003860 storage Methods 0.000 claims description 13
- IOVCWXUNBOPUCH-UHFFFAOYSA-M Nitrite anion Chemical compound [O-]N=O IOVCWXUNBOPUCH-UHFFFAOYSA-M 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 230000037406 food intake Effects 0.000 claims description 9
- 235000012631 food intake Nutrition 0.000 claims description 9
- 235000013601 eggs Nutrition 0.000 claims description 8
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000009313 farming Methods 0.000 claims description 6
- 238000007726 management method Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 description 12
- 235000013305 food Nutrition 0.000 description 9
- 241000254137 Cicadidae Species 0.000 description 7
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- 241000931705 Cicada Species 0.000 description 4
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- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K67/00—Rearing or breeding animals, not otherwise provided for; New or modified breeds of animals
- A01K67/033—Rearing or breeding invertebrates; New breeds of invertebrates
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G—PHYSICS
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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Abstract
The invention discloses an insect breeding system based on recycling of food waste, which comprises a registration login unit, a database, an environment monitoring unit, a restaurant matching unit, a delivery detection unit, an insect monitoring module and an alarm module, wherein the registration login unit is used for registering the food waste; the method comprises the steps of monitoring environmental data and soil data of a cultivation box in a cultivation area through an environmental monitoring unit, obtaining a temperature value, a humidity value and a wind strength value in the cultivation box, obtaining an environmental monitoring coefficient Tk through a formula, obtaining the water content and the pH value of soil, obtaining a soil coefficient Lk through the formula, comparing the environmental monitoring coefficient Tk and the soil coefficient Lk with Q1 and Q2 respectively, dividing the cultivation box into a first-level cultivation box, a second-level cultivation box and a third-level cultivation box, sequencing the cultivation boxes from small to large according to the grades, sending the sequenced numbers to mobile phone terminals of monitoring personnel, and giving an early warning after the monitoring personnel receive the numbers.
Description
Technical Field
The invention relates to the technical field of insect breeding, in particular to an insect breeding system based on recycling of food waste.
Background
The kitchen waste is waste generated in activities such as daily life, food processing, food service, unit catering and the like of residents, and comprises abandoned vegetable leaves, leftovers, fruit peels, egg shells, tea leaves, bones and the like, the main sources of the kitchen waste are household kitchens, restaurants, dining halls, markets and other industries related to food processing, and the recovery of the kitchen waste is important; golden cicada is commonly known as a representative species of insects in cicadae, belongs to an incomplete metamorphosis insect, and when aged nymphs of golden cicada are changed into adults, shells removed by eclosion are called cicada slough, which is also called cicada skin, and mainly comprises chitin and protein;
the golden cicadas are unique in flavor and rich in nutrition, have good medicine and food health care effects, and are increasingly developed in the breeding places of the golden cicadas, but in the prior art, the breeding places of the golden cicadas cannot be recycled after meals, grains are prepared for the golden cicadas, meanwhile, the breeding environment of the golden cicadas cannot be monitored, so that the problem of the golden cicadas cannot be solved in time, and the survival rate of the golden cicadas is low.
Disclosure of Invention
The invention aims to provide an insect breeding system based on food waste recycling, which detects parameter data of food waste leaves delivered to a breeding place through a delivery detection unit, records delivery time, restaurant names and delivery person names after the food waste leaves recovered by a restaurant are delivered to the breeding place, obtains the placement duration of the food waste leaves, the placement environment temperature and the content of nitrite, obtains a food waste detection coefficient LO through a formula, generates a return instruction after a cloud platform receives an unqualified signal and sends the return instruction and a contact telephone of an alternative restaurant to a mobile phone terminal of a manager if the food waste detection coefficient LO is less than a detection coefficient threshold value, and the manager receives the return instruction and then collects and orders again; the quality of the surplus vegetable leaves can be detected in real time, the survival rate of insects is improved, and when the quality is unqualified, the insects can be collected and ordered immediately through an alternative restaurant, so that the risk of insufficient food of the insects is reduced.
The purpose of the invention can be realized by the following technical scheme:
an insect breeding system based on food waste recycling comprises a registration login unit, a database, an environment monitoring unit, a restaurant matching unit, a delivery detection unit, an insect monitoring module and an alarm module;
the environmental monitoring unit is used for monitoring the environmental data and the soil data of breeding the case in breeding the ground, and environmental data includes temperature value, humidity value and the wind strength value of breeding the incasement, and soil data includes the water content and the pH value of soil, will concrete monitoring process as follows:
t1: acquiring a temperature value, a humidity value and a wind strength value in the cultivation box, and sequentially and correspondingly marking as Hk, Sk and Fk, wherein k is 1, 2.
T2: acquiring an environment monitoring coefficient Tk through a formula Tk-Hk × v1+ Sk × v2+ Fk × v3, wherein v1, v2 and v3 are all preset proportionality coefficients, and v1 > v2 > v3 > 0;
t3: acquiring the water content and the pH value of the soil, and respectively and correspondingly marking the water content and the pH value of the soil as Rk and Pk;
t4: acquiring a soil coefficient Lk by a formula Lk ═ Rk × b1+ Pk × b2, wherein b1 and b2 are both preset proportional coefficients, and b1 is more than b2 and more than 0;
t5: respectively comparing the environment monitoring coefficient Tk and the soil coefficient Lk with Q1 and Q2, wherein Q1 is a monitoring coefficient threshold value, and Q2 is a soil coefficient threshold value;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is less than Q2, judging that the breeding box is abnormal and marking the breeding box as a first-grade breeding box;
if the environmental monitoring coefficient Tk is larger than or equal to Q1 and the soil coefficient Lk is smaller than Q2, judging that the breeding box is abnormal, and marking the breeding box as a second-level breeding box;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is more than or equal to Q2, judging that the breeding box is abnormal and marking the breeding box as a third-level breeding box;
if the environmental monitoring coefficient Tk is not less than Q1 and the soil coefficient Lk is not less than Q2, judging that the breeding box is normal and marking the breeding box as a normal breeding box;
t6: sending the serial numbers of the first-level cultivation box, the second-level cultivation box and the third-level cultivation box to the cloud platform;
after receiving the numbers of the first-level cultivation box, the second-level cultivation box and the third-level cultivation box, the cloud platform sorts the first-level cultivation box, the second-level cultivation box and the third-level cultivation box in the order from small to large in level, sends the sorted numbers to a mobile phone terminal of a monitoring person, and the monitoring person gives an early warning after receiving the numbers.
Further, the registration login unit is used for the manager and the monitoring personnel to submit the manager information and the monitoring personnel information through the mobile phone terminals and send the successfully registered manager information and the successfully registered monitoring personnel information to the database for storage, the manager information comprises the name, the job number, the age of the manager and the mobile phone number of the real name authentication of the person, and the monitoring personnel information comprises the name, the job number, the age, the time of entry and the mobile phone number of the real name authentication of the person.
Further, the restaurant matching unit is used for reasonably matching restaurants by analyzing restaurant characteristic data, wherein the restaurant characteristic data comprises the total amount of restaurant food residues, the frequency of restaurant quality complaints and the distance from the restaurants to a breeding place, and the restaurants are marked as i, i is 1, 2.
Step one, obtaining the total amount of restaurant food residues, and marking the total amount of the restaurant food residues to a position Si;
step two, obtaining the frequency of the restaurant quality complaints, and marking the frequency of the restaurant quality complaints as a position Ci;
step three, passing through a formula
Obtaining an analysis coefficient Xi of the restaurant, wherein a1 and a2 are preset proportional coefficients, a1 is greater than a2 is greater than 0, beta is a correction factor, and the value is 2.3021532;
step four, comparing the analysis coefficient Xi of the restaurant with L1, wherein L1 is an analysis coefficient threshold value:
if the difference value of the analysis coefficient Xi of the restaurant and the L1 is less than 0, judging that the restaurant does not conform, and marking the restaurant as the non-conforming restaurant;
if the difference value between the analysis coefficient Xi of the restaurant and the L1 is larger than or equal to 0, judging that the restaurant is in line, marking the restaurant as a restaurant to be selected, marking the Chinese restaurant to be selected as o, wherein o is 1, 2, and the Chinese restaurant to be selected is sorted according to the sequence of the difference value from large to small;
step five, obtaining the distance Jo from the restaurant to be selected to the breeding place, and sequencing the distance Jo from the restaurant to be selected to the breeding place according to the sequence from small to large;
and step six, marking the restaurant with the first rank as a selected restaurant, marking the restaurant with the second rank as an alternative restaurant, and then sending the names, the geographic positions and the contact calls of the selected restaurant and the alternative restaurant to the cloud platform for storage.
Further, the distribution detection unit is used for detecting parameter data of the surplus meal leaves distributed to the breeding place, the parameter data of the surplus meal leaves comprise the placing time length, the placing environment temperature and the nitrite content of the surplus meal leaves, and the specific distribution detection process is as follows:
s1: after the surplus vegetable leaves recovered by the restaurant are delivered to a breeding place, recording delivery time, a restaurant name and a delivery person name, sending the delivery time, the restaurant name and the delivery person name to a cloud platform for storage, and after receiving, the cloud platform sorts and stores the surplus vegetable leaves from near to far according to dates;
s2: acquiring the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves, and sequentially and correspondingly marking the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves as SC, WD and HL;
s3: by the formula
Obtaining a meal residue detection coefficient LO, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
s4: compare the remaining-of-meal detection coefficient LO to a detection coefficient threshold:
if the surplus meal detection coefficient LO is larger than or equal to the detection coefficient threshold value, judging that the surplus meal leaves are qualified, generating a qualified signal, sending the qualified signal to the cloud platform, and generating a receiving confirmation instruction and sending the receiving confirmation instruction to the mobile phone terminal of the manager after the cloud platform receives the qualified signal;
if the surplus meal detection coefficient LO is smaller than the detection coefficient threshold value, judging that the surplus meal leaves are unqualified, generating an unqualified signal, sending the unqualified signal to the cloud platform, generating a return instruction after the cloud platform receives the unqualified signal, sending the return instruction and a contact telephone of an alternative restaurant to a mobile phone terminal of a manager, and collecting and booking again after the manager receives the return instruction.
Further, the insect monitoring module is used for analyzing the survival data of the insects, the survival data comprise the food intake, the activity times and the survival rate of eggs of the insects in the breeding box, and the specific analysis monitoring process comprises the following steps:
l1: obtaining the food intake of insects in the breeding box, and marking the food intake of the insects in the breeding box as Ak;
l2: acquiring the activity times of insects in the breeding box, and marking the activity times of the insects in the breeding box as Bk;
l3: acquiring the survival rate of eggs of insects in the breeding box, and marking the survival rate of the eggs of the insects in the breeding box as Ck;
l4: by the formula
Obtaining the survival coefficient Vk of the insect, wherein s1, s2 and s3 are all preset proportional coefficients, and s1 is larger than s2 is larger than s3 is larger than 0;
l5: comparing the survival coefficient Vk of the insect with a survival coefficient threshold:
if the survival coefficient Vk of the insect is larger than or equal to the survival coefficient threshold value, judging that the survival state of the insect is good, generating a good survival signal and sending the good survival signal and the serial number of the breeding box to the cloud platform for storage;
if the survival coefficient Vk of the insect is smaller than the survival coefficient threshold value, judging that the survival state of the insect is bad, generating a bad survival signal and sending the bad survival signal and the number of the breeding box to an alarm unit;
the alarm unit generates an alarm signal after receiving the severe survival signal and sends the alarm signal to the mobile phone terminals of the management personnel and the monitoring personnel in the form of short messages, the management personnel detect the surplus vegetable leaves of meals again after receiving the alarm signal, the monitoring personnel monitor the environment and soil in the breeding box again after receiving the alarm signal, and if the detection result and the monitoring result are qualified, the insect needs to be subjected to medical examination.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, parameter data of the surplus vegetable leaves delivered to a culture place are detected through a delivery detection unit, after the surplus vegetable leaves recovered by a restaurant are delivered to the culture place, delivery time, a restaurant name and a delivery person name are recorded, the delivery time, the restaurant name and the delivery person name are obtained, the placement time length, the placement environment temperature and the content of nitrite of the surplus vegetable leaves are obtained, a surplus detection coefficient LO is obtained through a formula, if the surplus detection coefficient LO is less than a detection coefficient threshold value, a return order is generated after a cloud platform receives an unqualified signal, the return order and a contact telephone of an alternative restaurant are sent to a mobile phone terminal of a manager, and the manager receives the surplus vegetable leaves and then re-orders the surplus vegetable leaves; the quality of the surplus vegetable leaves can be detected in real time, the survival rate of insects is improved, and when the quality is unqualified, the insects can be collected and ordered immediately by an alternative restaurant, so that the risk of insufficient food of the insects is reduced;
2. according to the method, an environment monitoring unit is used for monitoring environment data and soil data of a cultivation box in a cultivation area to obtain a temperature value, a humidity value and a wind power strength value in the cultivation box, an environment monitoring coefficient Tk is obtained through a formula, a water content and a pH value of soil are obtained, a soil coefficient Lk is obtained through the formula, the environment monitoring coefficient Tk and the soil coefficient Lk are respectively compared with Q1 and Q2 correspondingly, the cultivation box is divided into a first-level cultivation box, a second-level cultivation box and a third-level cultivation box, the cultivation boxes are sorted in a sequence from small to large according to the grades, the sorted numbers are sent to a mobile phone terminal of a monitoring person, and the monitoring person gives an early warning after receiving the numbers; the breeding boxes with the abnormal conditions are graded, so that timely and reasonable personnel allocation can be realized, and the phenomenon that the breeding boxes cannot be timely corrected and modified due to insufficient capability of monitoring personnel is prevented;
3. in the invention, the restaurant matching unit is used for reasonably matching restaurants by analyzing characteristic data of the restaurants to obtain the total amount of the restaurant food and the frequency of quality complaints of the restaurants, obtaining an analysis coefficient Xi of the restaurants by a formula, comparing the analysis coefficient Xi of the restaurants with L1, judging that the restaurants are in accordance with the result if the difference value between the analysis coefficient Xi of the restaurants and L1 is more than or equal to 0, marking the restaurants as restaurants to be selected, and sequencing the restaurants to be selected and the restaurants to be selected according to the sequence of the difference values from large to small; obtaining the distance Jo from the restaurant to be selected to the breeding place, and sequencing the distance Jo from the restaurant to be selected to the breeding place from small to large; the restaurant with the first rank is marked as a selected restaurant, the restaurant with the second rank is marked as an alternative restaurant, and a proper restaurant is selected through analysis of the restaurants, so that the safety quality of the insect food is improved, and the reduction of the survival rate of the insect caused by food problems is prevented.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an insect farming system based on food waste recycling includes a registration unit, a database, an environment monitoring unit, a restaurant matching unit, a delivery detection unit, an insect monitoring module, and an alarm module;
the registration login unit is used for the manager and the monitoring personnel to submit manager information and monitoring personnel information through mobile phone terminals and send the manager information and the monitoring personnel information which are successfully registered to the database for storage, the manager information comprises the name, the job number and the age of the manager and the mobile phone number of real name authentication of the person, and the monitoring personnel information comprises the name, the job number, the age, the time of job entry and the mobile phone number of real name authentication of the person;
the restaurant matching unit is used for reasonably matching restaurants by analyzing restaurant characteristic data, the restaurant characteristic data comprise the total amount of restaurant food residue, the frequency of restaurant quality complaints and the distance from the restaurants to a breeding place, the restaurants are marked as i, i is 1, 2.
Step one, obtaining the total amount of restaurant food residues, and marking the total amount of the restaurant food residues to a position Si;
step two, obtaining the frequency of the restaurant quality complaints, and marking the frequency of the restaurant quality complaints as a position Ci;
step three, passing through a formula
Obtaining an analysis coefficient Xi of the restaurant, wherein a1 and a2 are presetThe proportionality coefficient a1 is more than a2 is more than 0, beta is a correction factor, and the value is 2.3021532;
step four, comparing the analysis coefficient Xi of the restaurant with L1, wherein L1 is an analysis coefficient threshold value:
if the difference value of the analysis coefficient Xi of the restaurant and the L1 is less than 0, judging that the restaurant does not conform, and marking the restaurant as the non-conforming restaurant;
if the difference value between the analysis coefficient Xi of the restaurant and the L1 is larger than or equal to 0, judging that the restaurant is in line, marking the restaurant as a restaurant to be selected, marking the Chinese restaurant to be selected as o, wherein o is 1, 2, and the Chinese restaurant to be selected is sorted according to the sequence of the difference value from large to small;
step five, obtaining the distance Jo from the restaurant to be selected to the breeding place, and sequencing the distance Jo from the restaurant to be selected to the breeding place according to the sequence from small to large;
marking the restaurant with the first rank as a selected restaurant, marking the restaurant with the second rank as an alternative restaurant, and then sending the names, the geographic positions and the contact telephone of the selected restaurant and the alternative restaurant to the cloud platform for storage;
the distribution detection unit is used for detecting parameter data of the surplus meal leaves distributed to the breeding place, the parameter data of the surplus meal leaves comprise the placing time length of the surplus meal leaves, the placing environment temperature and the nitrite content, and the specific distribution detection process is as follows:
s1: after the surplus vegetable leaves recovered by the restaurant are delivered to a breeding place, recording delivery time, a restaurant name and a delivery person name, sending the delivery time, the restaurant name and the delivery person name to a cloud platform for storage, and after receiving, the cloud platform sorts and stores the surplus vegetable leaves from near to far according to dates;
s2: acquiring the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves, and sequentially and correspondingly marking the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves as SC, WD and HL;
s3: by the formula
Obtaining a meal residue detection coefficient LO, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
s4: compare the remaining-of-meal detection coefficient LO to a detection coefficient threshold:
if the surplus meal detection coefficient LO is larger than or equal to the detection coefficient threshold value, judging that the surplus meal leaves are qualified, generating a qualified signal, sending the qualified signal to the cloud platform, and generating a receiving confirmation instruction and sending the receiving confirmation instruction to the mobile phone terminal of the manager after the cloud platform receives the qualified signal;
if the surplus meal detection coefficient LO is smaller than the detection coefficient threshold value, judging that the surplus meal leaves are unqualified, generating an unqualified signal, sending the unqualified signal to the cloud platform, generating a return instruction after the cloud platform receives the unqualified signal, sending the return instruction and a contact telephone of an alternative restaurant to a mobile phone terminal of a manager, and collecting and booking again after the manager receives the return instruction;
the environmental monitoring unit is used for monitoring the environmental data and the soil data of breeding the case in breeding the ground, and environmental data includes temperature value, humidity value and the wind strength value of breeding the incasement, and soil data includes the water content and the pH value of soil, will concrete monitoring process as follows:
t1: acquiring a temperature value, a humidity value and a wind strength value in the cultivation box, and sequentially and correspondingly marking as Hk, Sk and Fk, wherein k is 1, 2.
T2: acquiring an environment monitoring coefficient Tk through a formula Tk-Hk × v1+ Sk × v2+ Fk × v3, wherein v1, v2 and v3 are all preset proportionality coefficients, and v1 > v2 > v3 > 0;
t3: acquiring the water content and the pH value of the soil, and respectively and correspondingly marking the water content and the pH value of the soil as Rk and Pk;
t4: acquiring a soil coefficient Lk by a formula Lk ═ Rk × b1+ Pk × b2, wherein b1 and b2 are both preset proportional coefficients, and b1 is more than b2 and more than 0;
t5: respectively comparing the environment monitoring coefficient Tk and the soil coefficient Lk with Q1 and Q2, wherein Q1 is a monitoring coefficient threshold value, and Q2 is a soil coefficient threshold value;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is less than Q2, judging that the breeding box is abnormal and marking the breeding box as a first-grade breeding box;
if the environmental monitoring coefficient Tk is larger than or equal to Q1 and the soil coefficient Lk is smaller than Q2, judging that the breeding box is abnormal, and marking the breeding box as a second-level breeding box;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is more than or equal to Q2, judging that the breeding box is abnormal and marking the breeding box as a third-level breeding box;
if the environmental monitoring coefficient Tk is not less than Q1 and the soil coefficient Lk is not less than Q2, judging that the breeding box is normal and marking the breeding box as a normal breeding box;
t6: sending the serial numbers of the first-level cultivation box, the second-level cultivation box and the third-level cultivation box to the cloud platform;
the cloud platform sorts the first-level breeding box, the second-level breeding box and the third-level breeding box according to the sequence of the grades from small to large after receiving the serial numbers of the first-level breeding box, the second-level breeding box and the third-level breeding box, sends the sorted serial numbers to a mobile phone terminal of a monitoring person, and the monitoring person gives an early warning after receiving the serial numbers;
insect monitoring module is used for analyzing the survival data of insect, and the survival data is including the food intake, the number of times of activity and the worm ovum survival rate of breeding incasement insect, and concrete analysis monitoring process is as follows:
l1: obtaining the food intake of insects in the breeding box, and marking the food intake of the insects in the breeding box as Ak;
l2: acquiring the activity times of insects in the breeding box, and marking the activity times of the insects in the breeding box as Bk;
l3: acquiring the survival rate of eggs of insects in the breeding box, and marking the survival rate of the eggs of the insects in the breeding box as Ck;
l4: by the formula
Obtaining the survival coefficient Vk of the insect, wherein s1, s2 and s3 are all preset proportional coefficients, and s1 is larger than s2 is larger than s3 is larger than 0;
l5: comparing the survival coefficient Vk of the insect with a survival coefficient threshold:
if the survival coefficient Vk of the insect is larger than or equal to the survival coefficient threshold value, judging that the survival state of the insect is good, generating a good survival signal and sending the good survival signal and the serial number of the breeding box to the cloud platform for storage;
if the survival coefficient Vk of the insect is smaller than the survival coefficient threshold value, judging that the survival state of the insect is bad, generating a bad survival signal and sending the bad survival signal and the number of the breeding box to an alarm unit;
the alarm unit generates an alarm signal after receiving the severe survival signal and sends the alarm signal to mobile phone terminals of managers and monitoring personnel in a form of short messages, the managers detect the surplus vegetable leaves again after receiving the alarm signal, the monitoring personnel monitor the environment and soil in the cultivation box again after receiving the alarm signal, and if the re-detection and monitoring results are qualified, the insects are judged to need medical examination;
the automatic light supplementing unit is used for performing illumination compensation on insects in the breeding box, the illumination intensity received in the breeding box is monitored in real time through an illumination intensity sensor, if the illumination intensity is lower than an illumination intensity threshold value, a light supplementing signal is generated and sent to the cloud platform, the cloud platform receives the light supplementing signal and then controls the light supplementing lamp to be turned on, the turn-on time of the light supplementing lamp is recorded, when the illumination intensity is higher than the illumination intensity threshold value, no light supplementing signal is generated and sent to the cloud platform, the cloud platform receives the no light supplementing signal and then controls the light supplementing lamp to be turned off, the turn-off time of the light supplementing lamp is recorded, the turn-on time of the light supplementing lamp is marked as a light supplementing time period, and the light supplementing time period is sent to the cloud platform to be stored.
The working principle of the invention is as follows: analyzing characteristic data of the restaurant through a restaurant matching unit, reasonably matching the restaurant to obtain the total amount of restaurant food residue and the frequency of restaurant quality complaints, and obtaining an analysis coefficient Xi of the restaurant through a formula; the analysis coefficient Xi of the restaurant is compared with L1, L1 being the analysis coefficient threshold: if the difference value of the analysis coefficient Xi of the restaurant and the L1 is less than 0, judging that the restaurant does not conform, and marking the restaurant as the non-conforming restaurant; if the difference value between the analysis coefficient Xi of the restaurant and the L1 is larger than or equal to 0, judging that the restaurant is in line, marking the restaurant as a restaurant to be selected, marking the Chinese restaurant to be selected as o, wherein o is 1, 2, and the Chinese restaurant to be selected is sorted according to the sequence of the difference value from large to small; obtaining the distance Jo from the restaurant to be selected to the breeding place, and sequencing the distance Jo from the restaurant to be selected to the breeding place from small to large; marking the restaurant with the first rank as a selected restaurant, marking the restaurant with the second rank as an alternative restaurant, and then sending the names, the geographic positions and the contact telephone of the selected restaurant and the alternative restaurant to the cloud platform for storage;
the method comprises the steps of monitoring environmental data and soil data of a cultivation box in a cultivation area through an environmental monitoring unit, obtaining a temperature value, a humidity value and a wind strength value in the cultivation box, obtaining an environmental monitoring coefficient Tk through a formula, obtaining the water content and the pH value of soil, obtaining a soil coefficient Lk through the formula, comparing the environmental monitoring coefficient Tk and the soil coefficient Lk with Q1 and Q2 respectively, dividing the cultivation box into a first-level cultivation box, a second-level cultivation box and a third-level cultivation box, sequencing the cultivation boxes from small to large according to the grades, sending the sequenced numbers to mobile phone terminals of monitoring personnel, and giving an early warning after the monitoring personnel receive the numbers.
The above formulas are all quantitative calculation, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. An insect breeding system based on food waste recycling is characterized by comprising a registration login unit, a database, an environment monitoring unit, a restaurant matching unit, a delivery detection unit, an insect monitoring module and an alarm module;
the environmental monitoring unit is used for monitoring the environmental data and the soil data of breeding the case in breeding the ground, and environmental data includes temperature value, humidity value and the wind strength value of breeding the incasement, and soil data includes the water content and the pH value of soil, will concrete monitoring process as follows:
t1: acquiring a temperature value, a humidity value and a wind strength value in the cultivation box, and sequentially and correspondingly marking as Hk, Sk and Fk, wherein k is 1, 2.
T2: acquiring an environment monitoring coefficient Tk through a formula Tk-Hk × v1+ Sk × v2+ Fk × v3, wherein v1, v2 and v3 are all preset proportionality coefficients, and v1 > v2 > v3 > 0;
t3: acquiring the water content and the pH value of the soil, and respectively and correspondingly marking the water content and the pH value of the soil as Rk and Pk;
t4: acquiring a soil coefficient Lk by a formula Lk ═ Rk × b1+ Pk × b2, wherein b1 and b2 are both preset proportional coefficients, and b1 is more than b2 and more than 0;
t5: respectively comparing the environment monitoring coefficient Tk and the soil coefficient Lk with Q1 and Q2, wherein Q1 is a monitoring coefficient threshold value, and Q2 is a soil coefficient threshold value;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is less than Q2, judging that the breeding box is abnormal and marking the breeding box as a first-grade breeding box;
if the environmental monitoring coefficient Tk is larger than or equal to Q1 and the soil coefficient Lk is smaller than Q2, judging that the breeding box is abnormal, and marking the breeding box as a second-level breeding box;
if the environmental monitoring coefficient Tk is less than Q1 and the soil coefficient Lk is more than or equal to Q2, judging that the breeding box is abnormal and marking the breeding box as a third-level breeding box;
if the environmental monitoring coefficient Tk is not less than Q1 and the soil coefficient Lk is not less than Q2, judging that the breeding box is normal and marking the breeding box as a normal breeding box;
t6: sending the serial numbers of the first-level cultivation box, the second-level cultivation box and the third-level cultivation box to the cloud platform;
after receiving the numbers of the first-level cultivation box, the second-level cultivation box and the third-level cultivation box, the cloud platform sorts the first-level cultivation box, the second-level cultivation box and the third-level cultivation box in the order from small to large in level, sends the sorted numbers to a mobile phone terminal of a monitoring person, and the monitoring person gives an early warning after receiving the numbers.
2. The insect farming system based on food waste recycling and reusing of claim 1, wherein the registration and login unit is used for managers and monitoring personnel to submit manager information and monitoring personnel information through mobile phone terminals and send the manager information and the monitoring personnel information which are successfully registered to the database for storage, the manager information comprises names, work numbers, ages of the managers and mobile phone numbers for authenticating real names of the managers, and the monitoring personnel information comprises names, work numbers, ages, time of entry and mobile phone numbers for authenticating real names of the monitoring personnel.
3. The insect farming system based on the recycling of food waste according to claim 1, wherein the restaurant matching unit is used for reasonably matching restaurants by analyzing restaurant characteristic data, the restaurant characteristic data comprises the total amount of restaurant food waste, the number of restaurant quality complaints and the distance from the restaurants to a farming place, and the restaurants are marked as i, i-1, 2.
Step one, obtaining the total amount of restaurant food residues, and marking the total amount of the restaurant food residues to a position Si;
step two, obtaining the frequency of the restaurant quality complaints, and marking the frequency of the restaurant quality complaints as a position Ci;
step three, passing through a formula
Obtaining an analysis coefficient Xi of the restaurant, wherein a1 and a2 are preset proportional coefficients, a1 is greater than a2 is greater than 0, beta is a correction factor, and the value is 2.3021532;
step four, comparing the analysis coefficient Xi of the restaurant with L1, wherein L1 is an analysis coefficient threshold value:
if the difference value of the analysis coefficient Xi of the restaurant and the L1 is less than 0, judging that the restaurant does not conform, and marking the restaurant as the non-conforming restaurant;
if the difference value between the analysis coefficient Xi of the restaurant and the L1 is larger than or equal to 0, judging that the restaurant is in line, marking the restaurant as a restaurant to be selected, marking the Chinese restaurant to be selected as o, wherein o is 1, 2, and the Chinese restaurant to be selected is sorted according to the sequence of the difference value from large to small;
step five, obtaining the distance Jo from the restaurant to be selected to the breeding place, and sequencing the distance Jo from the restaurant to be selected to the breeding place according to the sequence from small to large;
and step six, marking the restaurant with the first rank as a selected restaurant, marking the restaurant with the second rank as an alternative restaurant, and then sending the names, the geographic positions and the contact calls of the selected restaurant and the alternative restaurant to the cloud platform for storage.
4. The insect culture system based on food waste recycling and reusing of claim 1, wherein the delivery detection unit is used for detecting parameter data of food waste leaves delivered to a culture place, the parameter data of the food waste leaves comprise the placing duration, the placing environment temperature and the nitrite content of the food waste leaves, and the specific delivery detection process is as follows:
s1: after the surplus vegetable leaves recovered by the restaurant are delivered to a breeding place, recording delivery time, a restaurant name and a delivery person name, sending the delivery time, the restaurant name and the delivery person name to a cloud platform for storage, and after receiving, the cloud platform sorts and stores the surplus vegetable leaves from near to far according to dates;
s2: acquiring the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves, and sequentially and correspondingly marking the placing time length, the placing environment temperature and the nitrite content of the meal surplus vegetable leaves as SC, WD and HL;
s3: by the formula
Obtaining a meal residue detection coefficient LO, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
s4: compare the remaining-of-meal detection coefficient LO to a detection coefficient threshold:
if the surplus meal detection coefficient LO is larger than or equal to the detection coefficient threshold value, judging that the surplus meal leaves are qualified, generating a qualified signal, sending the qualified signal to the cloud platform, and generating a receiving confirmation instruction and sending the receiving confirmation instruction to the mobile phone terminal of the manager after the cloud platform receives the qualified signal;
if the surplus meal detection coefficient LO is smaller than the detection coefficient threshold value, judging that the surplus meal leaves are unqualified, generating an unqualified signal, sending the unqualified signal to the cloud platform, generating a return instruction after the cloud platform receives the unqualified signal, sending the return instruction and a contact telephone of an alternative restaurant to a mobile phone terminal of a manager, and collecting and booking again after the manager receives the return instruction.
5. The insect farming system based on food waste recycling and reusing of claim 1, wherein the insect monitoring module is used for analyzing survival data of the insects, the survival data comprises food intake, activity times and survival rate of eggs of the insects in the farming tank, and the specific analysis and monitoring process comprises the following steps:
l1: obtaining the food intake of insects in the breeding box, and marking the food intake of the insects in the breeding box as Ak;
l2: acquiring the activity times of insects in the breeding box, and marking the activity times of the insects in the breeding box as Bk;
l3: acquiring the survival rate of eggs of insects in the breeding box, and marking the survival rate of the eggs of the insects in the breeding box as Ck;
l4: by the formula
Obtaining the survival coefficient Vk of the insect, wherein s1, s2 and s3 are all preset proportional coefficients, and s1 is larger than s2 is larger than s3 is larger than 0;
l5: comparing the survival coefficient Vk of the insect with a survival coefficient threshold:
if the survival coefficient Vk of the insect is larger than or equal to the survival coefficient threshold value, judging that the survival state of the insect is good, generating a good survival signal and sending the good survival signal and the serial number of the breeding box to the cloud platform for storage;
if the survival coefficient Vk of the insect is smaller than the survival coefficient threshold value, judging that the survival state of the insect is bad, generating a bad survival signal and sending the bad survival signal and the number of the breeding box to an alarm unit;
the alarm unit generates an alarm signal after receiving the severe survival signal and sends the alarm signal to the mobile phone terminals of the management personnel and the monitoring personnel in the form of short messages, the management personnel detect the surplus vegetable leaves of meals again after receiving the alarm signal, the monitoring personnel monitor the environment and soil in the breeding box again after receiving the alarm signal, and if the detection result and the monitoring result are qualified, the insect needs to be subjected to medical examination.
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